Tag: Future

  • Slowing Growth and Uncertainty: A Look at IIE’s Open Doors Report 2024 and What the Future Might Hold

    Slowing Growth and Uncertainty: A Look at IIE’s Open Doors Report 2024 and What the Future Might Hold

    Bryce Loo, Associate Director of Higher Education Research

    International students navigate a landscape of uncertainty and opportunity, as the 2024 IIE Open Doors Report highlights shifting trends in U.S. enrollment and global migration.

    The Institute of International Education’s (IIE) annual Open Doors Report on International Education Exchange (Open Doors, for short),[1] along with its companion Fall Enrollment Snapshot Survey (Fall Snapshot Survey, for short),[2] was released only a few weeks after a consequential presidential election in which former president Donald Trump defeated Vice President Kamala Harris. Trump’s win will significantly shift the landscape around international students in the U.S.

    Open Doors is a retrospective report on international enrollment and other student data in the U.S., focused on the previous full academic year—in this case 2023-24.[3] The Fall Snapshot Survey provides insights into the current fall term. But uncertainty abounds in this new environment with the return of Trump, known for his tough stances on immigration, which may affect non-immigrant residents such as international students and temporary workers. This is happening against a backdrop of global uncertainty, in a year with a tremendous number of important elections around the world, many armed conflicts, and growing climate change. At the same time, there are bright spots for the U.S. as a host of international students and for global student migration in general.

    In this article, I also compare results from Open Doors and the Fall Snapshot Survey against recently released data from SEVIS (the Student Exchange and Visitor Information System), maintained by the Department of Homeland Security (DHS), for Fall 2024.[4] This dataset captures all students with a record in SEVIS, the U.S. government database in which all international students are required to be registered by their hosting U.S. institution. Data are organized monthly, with the most recently released is for November 2024, and they vary little month-to-month within a given term, such as a fall semester. For consistency, I compare November 2024 with November 2023. Such data help us to gain a fuller picture of current international enrollment trends this fall.

    What the data tell us: Continued but leveling growth

    Total international student enrollment in the U.S. hit an all-time high of 1,126,690 in 2023–24, a growth rate of 6.6 percent from the previous year. This has followed a few years of recovery following the dramatic enrollment decrease during the COVID-19 pandemic. The post-pandemic growth rate peaked in 2022-23 at 11.5 percent.

    However, growth is slowing. While this year’s Fall Snapshot Survey indicates a 3 percent growth rate in fall 2024, analysis of the SEVIS data indicates a drop in overall enrollment. International enrollment is down to 1,091,190 students in November 2024, a 10 percent decrease from the previous November, according to SEVIS records.[5] IIE’s data confirms slowing growth, too. New international student enrollment slowed to only 0.1 percent in 2023-24. Additionally, the Fall Snapshot Survey indicates a 5 percent decrease in new students this fall.

    This recent slowdown, which happened prior to the presidential election results, is tricky to diagnose. One likely culprit, though certainly not the only one, is economics: An education in the U.S. has become particularly expensive, due largely to a combination of inflation and a strong U.S. dollar. A more expensive U.S. education particularly impacts many students from South Asia and sub-Saharan Africa, the two regions showing the strongest rise in U.S. international enrollment in recent years, who are particularly price sensitive.

    Changing trends in South Asia and East Asia

    International enrollments in the U.S. from South Asia, driven dominantly by India, continued growing at a rapid rate in 2023–24. In 2023–24, India became the top country of origin among international students in the U.S. and by a substantial margin, at 331, 602 students. There was about a 23 percent increase in Indian students from the previous year, accounting for almost 30 percent of all international students in the U.S.

    In 2023-24, South Asia firmly dominated among regions of origin for U.S. international students and its numbers continue to rise. South and Central Asia (which IIE groups together as one[6]) account for one-third (34.3 percent) of all U.S. international students, just ahead of East Asia. South and Central Asia’s sending numbers grew 22 percent over the last year, more than those of any other region. Beyond India, there continues to be robust enrollment growth from Bangladesh (26 percent), Nepal (11 percent), Sri Lanka (10 percent), and Pakistan (8 percent). Bangladesh and Nepal broke into the top 10 countries of origin in 2023-24.

    By contrast, the number of Chinese students in the U.S. declined more than 4 percent to 277,398 during the same period and accounted for less than 25 percent of all U.S. international students. Overall, numbers from East Asia are declining steadily (by nearly 4 percent last academic year). Numbers from South Korea (-2 percent) and Japan (-13 percent) continued to drop. The one bright spot among major East Asian nations was Taiwan, which saw a 6 percent rise from the previous year and was the fifth largest sending country. Students from East Asia have been decreasing in the last few years, and forecasts suggest further steady decline.

    For many East Asian students, the calculus about studying in the U.S. and in Western countries has changed in recent years. Holding a degree from a highly ranked U.S. or Western institution holds less cachet than it once did. In both China and South Korea, local universities have become more prestigious and offer students the opportunity to connect directly with the local job market, putting those studying far afield at a disadvantage. For Chinese students, geopolitical tensions and strict policies against Chinese students and scholars largely enacted by the first Trump Administration, many of which were continued by the Biden Administration, may make studying in the U.S. feel riskier. There has also been growing intra-regional mobility, with many East Asian students choosing to go to another country in the region. According to the British Council, for example, there are more Japanese students in China than in any anglophone country.

    Despite the recent increases in enrollment from South Asia, the SEVIS data show a rapid reversal of trends heading in Fall 2024. Indian enrollment in the U.S. this fall has declined by 24 percent, and overall South Asian enrollment has fallen at a similar rate. Meanwhile, Chinese and overall East Asian enrollment has flatlined, each with a barely perceptible decrease. As a result, however, China has become the top country of origin once again, with 263,523 students in the U.S., followed by India (25,5443), in Fall 2024. Likewise, East Asia has returned to the top spot among region of origin, with modest enrollment increases from Japan and South Korea.

    A slowdown of enrollment growth from South Asia likely is attributable to rising costs in the U.S., particularly given currency exchange rates, as noted earlier. Safety, a frequent concern for Indian students and their families, could also be a factor. Many Indian media outlets, such as The Economic Times and The Indian Express, have recently reported on increasing safety issues for Indian students in the U.S.

    That said, these declines from India and South Asia do not necessarily foretell a long-term trend. Many prominent models, notably that of HolonIQ, predict growth from India into 2030.

    Graduate students continue to dominate. For now.

    International student growth in the U.S. continues to be driven at the graduate level, particularly among master’s degree students. Graduate students made up almost 45 percent of all U.S. international enrollment in 2023-24. Total international student graduate enrollment increased by 7.6 percent in 2023-24, while undergraduate enrollment fell by 1.4 percent and non-degree enrollment fell by 11.5 percent. These trends are somewhat parallel with new international student enrollment. India has driven much of this growth in grad students, as have South and Central Asian students in general. More Chinese students came to study at the graduate level that year, too.

    This growth of international graduate students does not appear to be holding into 2024-25, however. The Fall Snapshot Survey indicates a slight decrease of about 2 percent in international grad students this fall and an increase (6 percent) in international undergrad students. The SEVIS numbers show decreases for both, including a significant decrease of 15 percent among international grad students. (International undergrad enrollment declined by 3 percent.) However, international graduate enrollment is still greater than undergraduate enrollment currently.

    The decrease in international graduate students appears to be driven by Indian students and South Asian students overall. Indian students account for about 40 percent of all U.S. international student graduate students, and 60 percent of Indian students in the U.S. are studying at the graduate level, according to Open Doors. Per the SEVIS data, Indian graduate enrollment in the U.S. declined by almost 26 percent in Fall 2024. Additionally, South and Central Asian and East Asian student together account for nearly three-quarters of all international graduate students. Chinese graduate enrollment in the U.S. decreased about 4 percent this fall, according to the data from SEVIS.

    U.S. universities and colleges continue to focus heavily on India and to a lesser extent China for their international student recruitment, according to the Fall Snapshot Survey. India is the top country of focus for both graduate students (81 percent of respondents) and undergrads (65 percent). China was second top country of focus for grad students and third for undergrads (just after Vietnam). Given the volatility of enrollment from India and steady declines from China, U.S. institutions may wish to ensure diversity of countries from which they recruit.

    What could impact international enrollment in the near future?

    The Trump Administration

    When it comes to potential impacts on international student enrollment in the U.S., a primary factor will be the incoming Trump administration. Donald Trump will take office with a decisive agenda, having campaigned and won with a tough-on-immigration stance. This stance seemed to resonate with many voters, along with concerns about the economy and inflation.

    The first Trump administration may provide a useful look at what could happen in the second one. Trump’s first term brought a decline in international student enrollment, due in part to policies like the 2017 travel ban and a slowdown in visa processing. This trend reversed somewhat during the Biden administration but could resume under the policies of a second Trump term.

    Going forward, much will depend on the incoming administration’s policies as well as rhetoric. Trump’s immigration agenda is mostly focused on asylum, primarily at the U.S.-Mexico border, and on undocumented immigrants, whom he has pledged to deport at unprecedented rates. The extent that he will focus on international students and immigrants with specialty occupations, notably the H1-B visa program under which some international students seek to remain in the U.S., is unclear. In June 2024, Trump, known for making offhand comments, proposed on a podcast hosted by Silicon Valley investors that international students who graduate from U.S. institutions, including community colleges, should receive a green card (permanent residency). He and his team later walked back that remark, and many commentators see such policy as highly unlikely given Trump’s overall immigration stance. In fact, reports suggest the administration is likely to limit pathways to H1-B visas, international students’ primary means of staying in the U.S. beyond Optional Practical Training (OPT), effectively making such visas virtually inaccessible.

    Policy changes under Trump’s second administration could also affect OPT and “duration of status,” the length of time students with visas have been allowed to stay in the U.S. without needing to renew. Such changes were attempted in the first Trump administration but did not succeed. His first administration also tried to eliminate STEM OPT, the 24-month extension of OPT for those graduating with a degree in fields related to science, technology, engineering, or mathematics. Indian students in particular may be concerned about such changes if they are proposed again, as they are often drawn to the U.S. by opportunities to gain work experience. Toward the end of that term the administration also put forward a rule to limit duration of status to a finite period of two or four years, rather than allow the time needed to finish earning a degree, after which a student would be required to pay a fee and renew.

    Still, it is possible to overestimate the attitudinal impact of a presidential administration, and recent survey research by Intead and Studyportals found a majority of prospective international students this fall were “indifferent” to the election outcome and how it might affect their plans to study in the U.S., according to The PIE News. There is certainly no monolithic view of President-elect Trump or U.S. politics among international students. If any declines in numbers happen again under Trump, it will likely be in response to policies that specifically impact international students or rhetoric aimed at individuals from their home country or region of origin. It may also be driven in part by visa delays and denials caused by administration policies.

    Policies and politics in other major host countries

    One other major factor is current policy changes in other major host countries, driven largely by politics and public opinion, which might actually boost the attractiveness of the U.S. The other three Big Four predominantly anglophone destinations—Australia, Canada, and the United Kingdom—have had massive international student enrollment in recent years, particularly as a percentage of total higher education enrollment. According to IIE’s Project Atlas, Canada’s international enrollment rate in 2023 was 30 percent, Australia’s was 24 percent, and the U.K.’s was 22 percent. (By contrast, only 6 percent of U.S. higher education students were international, although overall size of its system makes the U.S. numerically the top enroller of international students.) Canada’s enrollment in particular has seen explosive growth, a rise of nearly 70 percent from 2019 to 2023. Many Canadian locales have struggled to accommodate such an influx, often viewed as a way to fill provincial funding gaps yet sometimes lacking steps to ensure students’ well-being.

    Additionally, international students have been ensnared in broader immigration debates within these three countries, often being unfairly blamed for systemic housing and employment challenges, among other issues. As in the U.S., immigration has been a major political topic in many Western countries and in recent elections in France and the U.K.

    As a result, the other three Big Four countries have begun implementing policies designed to rein in international enrollment growth and limit access to opportunities to work and stay after graduation. Canada, which according to IIE’s recent Open Doors briefing just overtook the U.K. to become the second most popular international student destination, adopted new policies in rapid-fire succession from late 2023 to fall 2024. The most consequential is a cap on the number of study permits (required in Canada for international students) granted per province, particularly meant to limit growth in higher-enrollment provinces, in 2024 and 2025. Other new policies include a significant hike in the financial resources international students are required to demonstrate, restrictions on work permits for spouses, limits on permission to work during study, and stricter requirements for obtaining the popular post-graduation work permit (PGWP), which allows graduated students to work in Canada and often transition to permanent residency.

    The Australian government is strongly considering similar caps on international student enrollment in an attempt to reduce overall migration to the country. Already it has stricter visa regulations for international students, including stronger “tests” to ensure that prospective students are coming with the intention of studying, not working, as well as a significant increase in the visa fee. In the U.K. a new regulation enacted by the Conservative Party prohibits international students at all levels except postgraduate from bringing family members starting in 2024, in order to “slash migration and curb abuse of the immigration system,” according to the U.K, government. The new Labour government has opted not to reverse the policy.

    The effects of these changes are already evident. The three other Big Four countries are all seeing declining applications for relevant visas and permits. Preliminary analysis of Canadian study permit application data shows the number of approved study permits will likely come in below the actual caps for 2024. The U.K. reported a 16 percent drop in student visa applications in summer 2024 compared to the same time period in 2023, and in Australia, the decrease in such applications has been particularly steep, nearly 40 percent from October 2023 to August 2024.

    So far, the prospective beneficiary of these changes has been the U.S., according to both prospective student surveys and media reports. For example, in IDP Education’s Emerging Futures Report for 2024, a prominent series based on prospective student survey data, the U.S. came in second place (at 23 percent) as destination of choice for survey takers, just behind Australia (24 percent). Interest in the U.S. increased four percentage points; Australia’s percentage point declined by one. By contrast, interest in the U.K. and Canada decreased 1 percent and 9 percent respectively, dropping them to third and fourth places. In media coverage of the restrictions, Indian outlets such as Business Standard and The Indian Express note that many Indian students are switching focus to the U.S, although some, including the Express, also report students are looking beyond the Big Four to other study destinations entirely.

    Still, President-elect Trump may introduce cuts or caps of his own, which, depending on their scope, may cause the U.S. to lose its developing enrollment edge. If all Big Four destinations have policies significantly cutting student influx, that could alter the student mobility landscape, shifting enrollments to other countries—notably, smaller anglophone destinations such as Ireland, New Zealand, and Singapore and non-predominantly anglophone countries in continental Europe and Asia—where English-taught programs have increased greatly in recent years.

    Student mobility in an uncertain world

    The incoming Trump administration and policy changes in other countries are only two factors apt to impact movement to the U.S.; internal issues in other countries and regions also come into play. For example, while U.S. policies and tensions with China have affected the number of Chinese students coming to the U.S., factors within China also played a role, as we examined in a recent series in WENR.

    Worldwide, uncertainty and systemic challenges lie ahead. Several major conflicts, notably Russia’s war in Ukraine and escalated fighting in the Middle East, threaten to spiral into bigger geopolitical crises. Authoritarianism is rising around the globe, creating more potential crises, as is the threat of climate change, with 2024 recently declared the hottest year on record. Among its many effects, climate change will likely continue spurring global migration, including, increasingly, the forcibly displaced. In fact, all these factors will likely increase global migration. Luckily, U.S. institutions are well-placed to take in students from affected regions and offer them pathways for academic and professional growth.

    In general terms, there is reason for optimism. Global student migration will continue and most likely rise, increasing economic and social opportunities for many globally mobile young people. International students also benefit their host societies, communities, and institutions, including domestic students, by bringing diverse international perspectives as well as economic benefits. By some estimates, international students will increase worldwide from about 6 million in 2023 to 10 million in 2030. The U.S. could host as many as 2 million, a still significant capacity compared to other destinations.

    Despite domestic and international pressures, U.S. institutions can continue to demonstrate the value of a U.S. education and what unique value they in particular offer. They can continue to make clear, through channels like the #youarewelcomehere campaign, that international students are both accepted and embraced. Institutions can continue to show that international education benefits not only students and institutions but communities and the nation. For example, huge numbers of U.S.-based entrepreneurs and  STEM professionals came to the U.S. as international students and have been an asset for U.S. business and research and development. And international educators can advocate for policies at local, state, and federal levels (for example, via NAFSA: Association of International Educators) that continue to make the U.S. a hospitable place for students from abroad.

    Most important, U.S. institutions can and should take proactive steps to ensure inclusion and integration of their international students. This means initial support in everything from securing good housing to culturally sensitive mental health resources to campus career services that recognize international students’ unique needs. It may mean assisting students with financing in any way possible. It also means more efforts toward academic and social integration, which involves educating faculty, staff, and domestic students as well.

    Looking to the future, U.S. policymakers, educators, and institutions must work together to create an environment that remains welcoming, inclusive, and responsive to the needs of international students. By doing so, the U.S. can maintain its position as a global leader in higher education and continue to benefit from the diverse perspectives and talents that international students bring.

     

    [1] Open Doors is an annual census of international students (those on a nonimmigrant student visa) enrollment in U.S. higher education institutions, as well as U.S. students who studied abroad two academic years prior.

    [2] The Fall Snapshot Survey is sent to all institutions that report data to IIE for Open Doors. This year, IIE collected 693 valid responses.

    [3] Open Doors always tracks data from the previous full academic year.

    [4] The SEVIS data released by DHS is usually the most up-to-date data available. Open Doors, however, provides more analysis and a greater breakdown of data compared with what is provided by SEVIS.

    [5] Usually, IIE’s Fall Snapshot Survey aligns with current data trends from SEVIS and is a strong predictor of numbers that appear in the following year’s Open Doors Report. This year, however, the data between the Fall Snapshot Survey and SEVIS are quite different, though both indicate slowing growth in international enrollment in the U.S.

    [6] Central Asia, which includes mostly former Soviet republics in Asia (such as Kazakhstan and Uzbekistan), only accounts for about 1 percent of total enrollment from the overall South and Central Asia region, according to my analysis of IIE Open Doors data.

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  • Skilled for the Future: How China is Transforming Vocational Education with Gerard Postiglione

    Skilled for the Future: How China is Transforming Vocational Education with Gerard Postiglione

    If there’s one thing we know for sure about Confucian societies, it’s the value they place on scholarship.  Being a student doesn’t just connote future financial success; the very act of studying itself carries an important element of moral virtue.  It’s one of the things that has driven university participation rates to extraordinarily high levels in East Asia, and also among diaspora populations in countries around the world.  Here in Canada, 25 years ago, Statistics Canada polled parents across Canada on their expectations for their children’s education, and they literally could not fins a Chines parent whose ambitions for their children involved community college.

    But not everyone can go to university.  Well, they can, but it doesn’t leave you with the most balanced labour force.  So if you’re running a higher education system and you want to get people to focus on vocational skills, what do you do?  Well, if you’re China, one strategy might be to create vocational credentials but attach to them something a little bit more academic…like a degree?  Call it a “vocational university”

    With me once again today, this time to talk about Vocational higher education in China is Gerard Postiglione, professor of higher education at the University of Hong Kong.  We cover the origins of the Chinese government’s vocational education policy, it’s recent successes, and the development of a new type of institution called a vocational university.  It’s a good, quick tour through an underappreciated part of the global higher education system. 

    Let’s turn things over to Gerry.

    The World of Higher Education Podcast
    Episode 3.13 | Skilled for the Future: How China is Transforming Vocational Education with Gerard Postiglione

    Transcript

    Alex Usher (AU): Gerry, could you give us a sense of where vocational education traditionally fits within Chinese tertiary or post-secondary education? This is a Confucian society, and as places like Korea and Japan have shown, there’s a strong cultural preference for book learning. The connotations of being a scholar often include elements of moral virtue. So, where does vocational education fit into this?

    Gerard Postiglione (GP): Well, China has gone through tremendous transitions in the 20th century—from the Qing dynasty to the Republic, and then from the Republic to the People’s Republic of China in 1949. At that time, China was overwhelmingly poor, with about 80 to 85 percent of the population living in poverty. There was a lot to do. The first phase of change involved learning from the Soviet Union, which placed a strong emphasis on linking schools and factories, education, and labor.

    During this period, there was no issue of employment because jobs were assigned. But with the market reforms starting in 1978 and accelerating in the 1980s, everything changed. In 1985, there was a major Communist Party policy to universalize nine years of basic education. However, at the same time, access to universities remained extremely limited—only about 1 to 2 percent of the 18-to-22-year-old age group. At the senior secondary level, vocational and technical education accounted for about 50 percent of enrollment. That was a significant shift toward developing technical skills in senior secondary high school. That was a major change, and it was very difficult. Of course, there were costs and finances to handle, equipment to manage, and so on but that’s when China launched into its first phase of technical vocational education.

    AU: There’s always been kind of a view, and this isn’t restricted to China, of course, that vocational education is a “less than” choice. Earlier this year, there was a big stir about a student named Jiang Ping from a fashion vocational high school. She won a national math competition beating students from very prestigious institutions. She said she wanted to stay in vocational education, which caused quite a sensation. Though, of course, there was even more attention later when it turned out her teacher had helped her during the competition, which led to her disqualification. But it reflects this broader tension, doesn’t it? That vocational education is still seen as a distant second choice to a traditional academic university if you can get in. What do you think?

    GP: The Jiang Ping case was widely reported, and it was unfortunate. I can understand her teacher’s hope to see her student excel, especially in such a high-profile competition as the Alibaba Global Mathematics Competition. It was the first time contestants were allowed to use AI tools, which added a new dimension. But when it was discovered that her teacher had helped her during the competition, she was eliminated.

    As for the broader question, yes, that traditional Confucian view of education as primarily academic does still resonate, and you’re right—it’s not unique to China. In many countries, academic higher education is seen as more prestigious and valuable than vocational pathways. In China’s case, for students moving into senior secondary education, if they weren’t excelling academically, vocational technical education became the primary option for about 50 percent of students.

    It’s also worth noting that China’s higher education system includes both four-year bachelor’s degrees and three-year specialized colleges, similar to community colleges. So there’s always been a dual pathway. But roll ahead to the future, I think those attitudes are starting to shift, especially with the leadership taking strong steps to highlight the value and persuade people of the value of vocational and technical education in an age increasingly defined by high technology and specialized skills.

    AU: In 2019, the Chinese government introduced the National Vocational Education Reform Implementation Plan. What were its key elements? What were they trying to achieve?

    GP: That was a massive plan, introduced at a time when access to senior secondary education had reached about 50 percent, moving China past the stage of mass higher education and into universal higher education, with a postsecondary access rate of around 60 percent. The government’s approach was very strategic. They looked at their industrial development plans, identified key industries, and considered their long-term goals for funding science and technology, as well as for developing both high-level and mid-level skills.

    The aim was clear: to become the global leader in vocational education. This included strengthening the three-year diploma programs, which already make up nearly 50 percent of China’s higher education system and transforming many of the rapidly expanded provincial universities into application-oriented institutions offering bachelor’s degrees that are heavily vocational and technical in focus.

    I’ve seen this transformation firsthand through work with Asian Development Bank projects in provinces like Gansu and Yunnan. In Gansu, they built an entire city of vocational and technical education colleges, referred to as a “vocational technical city.” Yunnan, meanwhile, has become a model province for western China, pushing ahead with this initiative.

    This plan is not just about upgrading skills but also about providing jobs for graduates in a slowing economy, with GDP growth now at around 5 percent. It’s a highly ambitious and comprehensive effort to align education with the needs of both the labor market and the country’s economic development.

    AU: Let’s talk about vocational universities specifically. My understanding is that they come out of the same period or the same plan. How do they differ from traditional universities or vocational colleges? What makes their programming and curriculum unique?

    GP: Well, the first thing to note is that the entire system, including the top-tier universities, is now putting more emphasis on application-oriented skills. That said, the top universities—like the flagship and highly-ranked institutions—are focused on the rapid advancements in science, technology, and innovation, so there’s not as much of an issue there.

    But for the rest of the system, which is massive, the focus is aligning more closely with the labor market and economic needs. Vocational universities—now sometimes translated as Colleges of Applied Science or Universities of Applied Science—are distinct in their close relationship with industry. That’s the key element. They aim to bring industries much closer to the education system.

    This is challenging because many of the academics at these institutions were trained in traditional disciplines, often with PhDs, and they’re now being asked to collaborate with industry, which is more focused on production and profits. But that collaboration is crucial to the success of these institutions. At the upper levels, this is working quite well—for example, Huawei now employs a large number of PhD holders and is very application-oriented. But for the rest of the country, it’s more complex.

    State-owned enterprises are heavily encouraged to engage with these application-oriented universities. Meanwhile, the private sector, which is growing, also plays a significant role. Private vocational colleges or universities of applied science have a strong incentive to ensure their graduates get jobs—otherwise, they won’t attract students. This dynamic means there’s learning on both sides, with the public and private sectors influencing each other.

    Another distinct feature of these institutions is their emphasis on skills certification. Students earn credits for the skills they acquire, and a credit bank system is in place to support this. This allows students to build up credentials over time, aligning their education with workforce needs.

    AU: You raised something here that I think is kind of important because in India, they’re building what are called skills universities. I can’t quite figure out how they work or what they’re supposed to do, but there seems to be a big corporate aspect to them. For instance, they’re inviting industries directly to teach programs or design the curriculum. Is that also happening in Chinese vocational universities, whether public or private?

    GP: Well, I’ve only been to India a couple of times, so I wouldn’t claim to be an expert on the system there. But from what I’ve seen, they’re dealing with similar issues around skills training and apprenticeships for college students. That said, I think China is moving much faster in this respect.

    In China, there’s a real effort to bring industry into the universities. This involves recruiting members of companies to go into universities and teach, collaborate with academic staff, and form centers for training and experimentation. There are experimental vocational—or let’s call them colleges of applied science—being set up in cities all over the country. This is a very serious effort, and both the government and the Communist Party are strongly committed to making it work.

    China is also working on developing proper evaluation systems for this model, though that process is still underway. But the key is getting industry directly involved in the university, and that’s a central part of the plan. There’s also a focus on internationalization, with China being very open to learning from models around the world. For instance, I’ve been asked to introduce elements of the German model. I actually published a paper with a Chinese economist comparing the German model with China’s approach, and that’s been influential in shaping how this sector is developing.

    AU: Is this focus on vocationalization a reaction to high graduate unemployment from traditional universities? I recall that back in 2014, China planned to convert several universities into polytechnics. Is this part of the same trend?

    GP: Yes, I think the translations of the terms—whether you call them polytechnics, universities of applied science, or something else—don’t really matter too much. The key thing is that these are application-oriented bachelor’s degree programs. And the introduction of these degrees addresses a critical issue: families in China traditionally don’t want their children to go anywhere but academic higher education. But if a degree comes from a university, even if it’s vocationally oriented, that helps resolve concerns about the image of vocational education.

    Graduate unemployment is certainly a pressing issue. The economy is growing more slowly than before, and when you move from mass higher education to universal higher education—China’s access rate is now over 60%—it’s inevitable that this kind of challenge emerges. It’s partly a transitional phase, but it’s also something the government is addressing with both short-term measures and longer-term plans.

    I’ve seen this kind of thing before. For example, when I was a student in the United States during an economic downturn, graduate unemployment was a serious issue for several years. China is dealing with something similar now. It’s likely to take three, four, or even five years to turn things around, but the government is actively working on stimulus plans to address these short-term challenges.

    At the same time, they’re focusing on the longer-term development of a higher education system that aligns with the labor market and the country’s broader economic goals. It’s a significant concern, but I think they’re holding the line for now.

    AU: Sure. And so what’s student uptake like at these vocational universities? I mean, you said earlier that if their graduates don’t get jobs, then students won’t apply. So are students actually enrolling in these institutions? Do parents want their kids to attend? What’s the demand for this compared to traditional universities?

    GP: Well, the demand for education in China is still tremendous. It’s deeply rooted in Chinese civilization. Education is highly valued, and many of my own students, even in Hong Kong, have gone all the way through the system. If there were such a thing as a second or third doctorate, I’m sure they’d pursue that too. So yes, the demand is there, and the students are generally very good. There’s a heavy emphasis on education across the board.

    Now, when it comes to uptake, there’s a bit of a difference between the state-run system and the private sector. For public vocational universities, there’s no problem with enrollment because these are degree-granting programs. Degrees carry significant weight culturally, and parents and students see the value in them.

    The private sector is a different story. Private institutions don’t receive much government funding; they rely on student fees, investments, and donations. Some private vocational colleges are extremely successful and manage to compete well, but they need to deliver outcomes—mainly, good job placements—or they won’t attract students. What’s interesting is how the state system learns from the private sector. The private colleges have to be responsive to the labor market to survive, and their success in this area can influence public institutions.

    For the public system, though, uptake isn’t really an issue. Plus, there are opportunities for additional training. For example, if you have a bachelor’s degree and find that you need certain skills for the job market, you can take a “top-up” year to get the training you need. It’s a flexible system that adapts to labor market demands.

    AU: Right. Well, that’s very similar to our community colleges in Canada. Final question: as China continues to reform and expand its higher education system, what do you see as the future for vocational universities? Are they going to become a bigger part of the mix moving forward? And if so, will it be focused on certain fields, or do you see it expanding more broadly?

    GP: Vocational and technical higher education in China is already a major component of the higher education system, and it’s going to remain that way. One of the reasons for China’s productivity in areas like green skills, battery production for electric vehicles, and other technical aspects of the green economy is this strong foundation in vocational education.

    China has learned a lot from international experience—working with companies like Tesla, IBM, and John Deere—and it’s applying those lessons. The government’s plan is to go full throttle with higher vocational technical colleges, polytechnics, or colleges of applied science—whatever you want to call them. And they have a long-term strategy to ensure these institutions are central to their higher education system.

    I’d also expect that other countries in the region, particularly middle-income developing countries, will follow this path. China’s approach is setting an example for how to align higher education with economic development, especially in sectors that are crucial for the future.

    AU: Gerry, thanks so much for being with us today.

    GP: You’re very welcome.

    AU: And it just remains for me to thank our excellent producers, Tiffany MacLennan and Sam Pufek, and of course, you, our listener, for joining us. If you have any questions or suggestions for future episodes, please get in touch at [email protected]. Don’t forget to subscribe to our YouTube channel and join us next week for the final episode of the year, featuring Robert Kelchen from the University of Tennessee. He’ll share his top 10 stories in U.S. higher education. Bye for now.

    *This podcast transcript was generated using an AI transcription service with limited editing. Please forgive any errors made through this service.

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  • Technology’s Role in Helping Educators Navigate the Future of Learning

    Technology’s Role in Helping Educators Navigate the Future of Learning

    Our panel of experts discusses the biggest challenges facing educators today and how educational technology can help — if used properly.

    Melinda French Gates

    Philanthropist, Businesswoman, Author

    What is the biggest challenge you see educators facing today, especially women educators?

    The worst thing you can do is put a lot of pressure on yourself to fit in. I know because I’ve been there. What I learned is that I was much happier — and much more effective as a professional — when I found my own leadership style. My advice to anyone in that position today is this: You will succeed because of who you are, not in spite of it. In the meantime, surround yourself with people who believe in you and will bring out the best in you.

    What would you tell today’s educators to help them ignite a passion for STEM subjects in the next generation of female innovators?

    The best educators understand that many girls are interested in STEM subjects — and many girls are really good at STEM subjects — but they get interested in them at different times and for different reasons. For example, because girls don’t always get the same early exposure to STEM that boys do, their interest tends to develop later. While boys often get into tech through video games, girls are more likely to develop an interest in the subject when they see it as a way to solve real-world problems. Educators can help by introducing STEM to girls early, bringing these subjects to life, and telling the girls in their classes, “Hey, I think you’d be good at this.” 

    Sean Ryan

    President, McGraw Hill School

    What is the biggest challenge you see educators face today?

    The social context in which teachers operate poses immense challenges. Educating a child — though all are natural learners — has become more complex in recent years; more complex than I’ve seen in my entire education-related career. Poverty, social media, gun violence, ideology, belief systems, and the unrelenting advance of technology mean that what worked yesterday might be less relevant today, and what we might need tomorrow is harder to discern. That’s why as a curriculum and technology provider, we must stay in close contact with educators to ensure that we remain a worthy, agile, and, most importantly, trusted partner.

    Where do you see the adoption of education technology headed in the next year?

    Education technology has been deployed in a piecemeal fashion to serve a variety of specialized needs. Together, the promise is immense. Separately, confusion and frustration can ensue. The key, in my view, is systems integration to create an increasingly coherent digital learning environment that complements the physical classroom. However, this takes time. I’m less interested in new features and functionality and more enthusiastic about what happens to the teacher’s workload when core, intervention, and supplemental solutions work in harmony to ease the teacher’s burden. There will be progress next year, but it will be of an evolutionary nature, not revolutionary. You might not even notice it.

    With the increased use of education technology, how can we help keep teachers from burning out and ensure that technology enhances, rather than complicates, their instructional practices?

    Teachers have a near-impossible task of educating a class of students with a wide variety of demonstrated performance levels across subjects. The year of a child’s birth is a poor organizing principle. Given that principle is not likely to change any time soon, technology must be deployed thoughtfully to handle the administrative, logistic, and computational work that supports personalization at scale. Automation should absorb time-consuming tasks that teachers are taking home or missing lunch to complete. Let’s empower teachers to get to know their students, to create a vibrant learning environment that goes beyond a universal and rigid scope and sequence with a single subject.

    What advice would you give to educators, administrators, and policymakers as they navigate the increasingly complex landscape of educational technology solutions?

    Despite daily pressures, try to think long-term. Despite political difference, try to think universally. What is in the best interest of the students today? What is in the best interest of all of us outside of the classroom tomorrow? An educated polity is vital to improving the human experience. We are constantly planting and replanting democracy and the precursors of prosperity in the minds of the next generation. For it to take root, flourish, and grow, there must be constancy of purpose. It’s through the lens of that purpose that we can evaluate new technologies to determine if they serve or, perversely, demand servitude. Technology in isolation is neutral. Only in the context of human wants and needs can we determine if a technology is useful or harmful.

    How can K-12 schools address concerns of the digital divide, especially when it comes to equitable access to devices, internet connectivity, and high-quality content?

    It begins with measurement. Don’t assume national headlines reflect your local reality. Take time to understand the computing environment across buildings and between the homes of your students. We should neither assume a problem nor that there isn’t one. Once you know the state of things, administrators can go to work with trusted technology partners to close known gaps. Today, with the near ubiquity of devices and high-speed connectivity, there’s no reason to leave anyone out. This requires communication and cooperation between home and school. With respect to high-quality content, take the time to understand the differences between solutions. The lower the quality, the more grandiose the promises.  

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  • 2024 Election Results and Analysis of Future Policy Impacts

    2024 Election Results and Analysis of Future Policy Impacts

    by CUPA-HR | November 14, 2024

    The results of the 2024 election are in: Donald Trump will serve as the 47th president of the United States, while both the Senate and House of Representatives will be controlled by Republicans. With the Republican trifecta in the White House and Congress, Republicans can focus on passing their policy priorities through legislation in Congress and regulatory action at the federal agencies. CUPA-HR’s government relations team provides the following analysis to offer insight into possible leadership, policies and regulations we expect starting in January 2025.

    Federal Agencies and Congressional Committees

    Department of Labor

    The Department of Labor (DOL), overseen by the secretary of labor, directs policy and regulations for employers, workers, and retirees in the U.S. Throughout the election season, news organizations have speculated President-elect Trump’s potential picks for the secretary position, though who will be nominated will be unknown until Trump announces it. According to Politico, two possible candidates are Patrick Pizzella and Bryan Slater. Under the first Trump administration, Pizzella served as deputy secretary of labor and acting secretary of labor between former secretaries Alex Acosta and Eugene Scalia. Slater, who currently serves as Virginia’s secretary of labor, had also previously served as assistant secretary at DOL under the previous Trump administration.

    In addition to the secretary of labor, Trump will pick people to head the subagencies at DOL, including the Employee Benefits Security Administration, Occupational Safety and Health Administration, and Wage and Hour Division, among others. These agencies draft and implement regulations governing retirement and health benefits plans, workplace safety and health, and minimum wage and overtime pay requirements. Leaders of the DOL subagencies are typically selected later in the Cabinet-appointment process.

    National Labor Relations Board

    The party control of the National Labor Relations Board (NLRB) depends on actions taken by the Senate during the lame-duck session between the election and President-elect Trump’s inauguration. Current chair of the NLRB Lauren McFerran’s term is set to expire in December 2024, but she has been renominated to serve on the board for another five years by President Biden. Senate Democrats, who are likely to push for her confirmation now that the Senate and White House will be Republican-controlled in 2025, will need to vote to confirm her position, only needing a simple majority. If confirmed, NLRB would be under Democratic control until at least August 2026, more than a year and a half into the Trump administration, leaving President Trump unable to obtain a Republican majority on the board — and thereby control the policy at the NLRB — for nearly half of his second term.

    Despite possibly not having control of the NLRB, President Trump may choose to fire the NLRB General Counsel Jennifer Abruzzo (Democrat), whose term is not set to expire until July 2025. In 2021, President Biden terminated then-General Counsel Peter Robb (Republican) within hours of his inauguration, despite Robb’s term not ending until November of that same year. This was the first time any sitting president had fired a sitting general counsel at an independent agency for policy differences. Federal courts upheld Robb’s termination, so President Trump is highly likely to terminate Abruzzo immediately upon taking office. As a reminder, Abruzzo issued several memos stating her position regarding employment status for student-athletes, severance agreements, and disclosure obligations under the National Labor Relations Act and Family Educational Rights and Privacy Act, all of which would likely be rescinded by Trump’s NLRB general counsel appointee.

    Equal Employment Opportunity Commission

    Unless a commissioner leaves their post before their term expires, the Equal Employment Opportunity Commission (EEOC) will maintain a Democrat majority (currently 3-1, with one Republican seat vacant) until July 2026. Despite this, President-elect Trump is likely to appoint Commissioner Andrea Lucas to serve as chair of the EEOC. Lucas and the EEOC would be limited in their ability to adopt new policies or reverse actions taken by the Democrat-controlled commission prior to July 2026. At that time, we expect the Republican-controlled EEOC to issue revised guidance that narrows the scope of the agency’s interpretation of Title VII protections in light of Bostock v. Clayton County and the legality of diversity, equity, and inclusion (DEI) initiatives in employment practices, possibly extending legal principles established under the Students for Fair Admission v. Harvard case.

    Similar to the NLRB, we expect that President-elect Trump will replace the current EEOC General Counsel Karla Gilbride (Democrat). In her role, Gilbride has litigated on behalf of the EEOC in federal court, but the position typically does not provide policy recommendations to the full commission like the NLRB general counsel does.

    Department of Education

    The Department of Education (ED) oversees and implements policy and regulations governing federal assistance to education. With respect to higher education, ED governs issues like federal financial aid, Title IX compliance, and other laws aimed at promoting student success. Under the incoming Trump administration, Politico has speculated that there are a few possible contenders who could ultimately lead the agency.

    One possible candidate for ED’s secretary is Betsy DeVos, who served as secretary of education during Trump’s first term. During DeVos’ first term as ED secretary, she led the agency to implement the 2020 Title IX regulations that are still currently in place in 26 states and hundreds of schools around the country, pending legal challenges to the Biden administration’s rule. However, DeVos resigned from her position as secretary of education after the January 6, 2021, riots at the U.S. Capitol, which may lead the incoming Trump administration to search for new candidates. Despite her resignation, DeVos has indicated that she is open to discussions about potentially serving in the role again.

    As we also discuss below, Rep. Virginia Foxx (R-NC) will be stepping down from her role as chair of the House Education and the Workforce Committee, where she most recently led an investigation into antisemitism on campus in higher education. This, along with her previous experience serving as an English instructor and president of a community college, may set her up for a bid for the secretary position.

    Some additional names that have been discussed by Politico are Virginia Governor Glenn Youngkin, Oklahoma State Superintendent of Public Instruction Ryan Walters, and Moms for Liberty founder Tiffany Justice.

    House Education and the Workforce Committee

    Republicans held control of the House in the 2024 election, but there will still be some shakeup in leadership for the Education and Workforce Committee. Chair of the committee Virginia Foxx will be stepping down from her role, leaving open the Republican leader position of the Committee. The two front-runners to chair the committee are Reps. Tim Walberg (R-MI) and Burgess Owens (R-UT), both currently serving on the committee. Notably, Walberg has served on the committee for 16 years, and Owens currently serves as the chair of the Higher Education and Workforce Development Subcommittee. For Democrats, current ranking member of the committee Bobby Scott (D-VA) is expected to maintain his position as leader of the Committee Democrats.

    Walberg and Owens have both publicized their policy priorities. Walberg has stated that, under his leadership, the committee would focus on legislation to make college more affordable, boost apprenticeships, implement a short-term Pell grant for workforce training programs, and reauthorize the Workforce Innovation and Opportunity Act. Owens hopes to steer the committee with a more education-centric focus, stating that top priorities for him are school choice and oversight into how ED uses its funding.

    Senate Health, Education, Labor, and Pensions Committee

    Republicans in the Senate gained control during the 2024 election, flipping the previously Democrat-controlled chamber. As a result, Senator Bill Cassidy (R-LA) will likely rise to the role of chair on the Health, Education, Labor, and Pensions (HELP) Committee. Senator Bernie Sanders (I-VT) will shift into the ranking member position after serving as the chair of the full committee in the 118th Congress. Before his political career, Cassidy was a physician, meaning he could pivot the committee to focus more on health policy. Despite this, Cassidy has also advocated for the HELP committee to advance a Workforce Innovation and Opportunity Act reauthorization bill, and he has advocated for the committee to focus on other education issues as well.

    Policy Implications of the Election

    FLSA Overtime

    As you already know, the Biden administration is in the process of implementing their FLSA overtime regulations. The final rule took a two-phased approach to increasing the minimum salary threshold. The first increase raised the salary threshold to $43,888 per year and took effect on July 1, 2024. The second increase would raise the salary threshold to $58,656 per year and is set to take effect on January 1, 2025. The regulations are currently being challenged in a federal district court in Texas, where a preliminary injunction to block the rule from taking effect has been placed only for public employers in the state of Texas. It remains to be seen how the federal judge will rule on the lawsuits, though a hearing for the cases was held on November 8 and a ruling is imminent.

    As the Trump administration will not take office until after the January 1 threshold, the regulation will take effect, pending further appeals, if the final rule is upheld in federal court. If the rule is struck down, we expect the Trump administration will let the court’s decision remain and make no further effort to appeal the decision. If the Trump administration decides to increase the minimum salary threshold during this upcoming term, they will likely use the methodology from the 2019 rule to increase the threshold.

    Title IX

    Similar to the overtime final rule, the Biden administration issued Title IX regulations in 2024 that are also facing legal challenges. The Biden administration’s Title IX rule took effect on August 1, 2024, but several lawsuits challenging the rule have resulted in preliminary injunctions blocking ED from enforcing it in 26 states and hundreds of other schools in states that did not challenge the final rule.

    The Biden administration’s regulations replaced the previous Trump administration’s 2020 Title IX regulations. If the regulations are upheld in federal court, we expect that the incoming secretary of education will repeal the Biden administration’s regulations in favor of keeping the 2020 regulations in effect across the country.

    Immigration

    There are several policies and regulations that CUPA-HR has been tracking on the immigration front that face uncertain futures under the incoming Trump administration. During the first term, the Trump administration placed a proposed rule on the regulatory agenda aiming to restrict the Optional Practical Training  program, which allows international students who graduate from U.S. institutions to work in their degree-related field for at least 12 months after graduating. The Trump administration also finalized a couple of final rules that would have increased wage obligations for H-1B visas and narrowed eligibility for H-1B visas to positions that qualified as “specialty occupations.” These rules were struck down in court, so while Trump is unlikely to implement the same rules, we could see similar attempts to increase H-1B wage obligations and narrow the H-1B program.

    Additionally, the incoming Trump administration will likely look to reverse policies implemented by the Department of Homeland Security under the Biden administration, including dropping any appeal of the recent court ruling against the “Keeping Families Together” program for undocumented spouses and children of U.S. citizens, as well as rescinding the guidance to streamline the H-1B visa waiver process for Deferred Action for Childhood Arrivals (DACA) recipients. Similarly, if the Biden administration does not finalize the H-1B modernization rule before the end of his term, a new Trump administration may seek to implement a more restrictive version, reshaping the rule to reflect its own priorities rather than those outlined in the Biden administration’s October 2023 proposal.

    Legislative Priorities

    With Republicans controlling both the House and the Senate, legislative priorities should be mostly aligned between the two chambers and the White House. However, their ability to pass legislation will still depend on bipartisan support, as Republicans hold a narrow majority in the House and do not have a large enough majority in the Senate to bypass the 60-vote filibuster. Despite these challenges, we expect Republicans to focus on issues like paid leave, workforce development, and affordable college and workforce training.

    Though paid leave is a priority for both parties, Republicans and Democrats have previously not agreed on the best approach to establish it through federal legislation. In his first term, Trump and other Republicans backed paid leave legislation that allowed parents to collect a portion of their future child tax credits early to use for leave and receive smaller credits in the following years. This proposal ran counter to the Democrat-supported Family and Medical Insurance Leave (FAMILY) Act, which would establish a payroll tax to fund a paid family and medical leave program that can be used to pay workers who are new parents or who are caring for their own health issues or those of their family. Republicans and Democrats will need to find a compromise if they are to pass any paid leave legislation in the upcoming Congress, as they will need 60 votes in the Senate to bypass a filibuster.

    Despite their differences on paid leave, Republicans and Democrats have made bipartisan efforts to pass legislation to improve workforce development and create a short-term Pell grant. During this Congress, both the House and Senate have worked to pass legislation to reauthorize the Workforce Innovation and Opportunity Act, which serves as the nation’s primary federal workforce development legislation designed to help Americans receive training and support to obtain skills necessary for high-quality jobs and careers. Additionally, there has been bipartisan support to pass legislation that would expand the Pell grant program to cover short-term workforce development and training programs that are outside the traditional higher education path. Again, Republicans and Democrats will need to find consensus on these issues in order to bypass the Senate’s 60-vote filibuster, but bipartisan issues like workforce development and short-term Pell grants appear to have a possible path to becoming law.

    CUPA-HR is hosting a 2024 election analysis webinar on November 21 at 12 PM ET. Registration is free for CUPA-HR members. Additional updates will be provided through future blogs and Washington Insider alerts.



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  • The Future of Online Learning Brands

    The Future of Online Learning Brands

    Embracing a “One School” Approach for a Better Student Experience

    Let’s draw a line in the sand. On one side, we have a university campus and its on-ground offerings. On the other side, we have the digital higher education space and the online programs that live within it. 

    Traditionally, this line has been stark and rigid, with universities treating the two modalities as separate entities with dedicated teams, technology, systems, budgets, and strategies. 

    The initial separation was, in part, driven by the perception of online education as a lesser counterpart to its on-ground equivalent. This view may have held some truth in the early stages of digital learning. But the division has come with a cost, as institutions have had to do double the work, which is inefficient. 

    We can all see that significant changes are underway. Traditional educational boundaries are fading, with online learning gaining respect and sophistication. There are online programs that outpace their on-ground counterparts in quality and rigor. We’re looking at a future where traditional, hybrid, and online modalities are integrated, balancing both quality and accessibility. 

    As we leave the comfort of land and head out to sea, embracing a holistic approach is the way forward for universities.

    Separation Comes at a Cost 

    The traditional division between on-ground and online learning modalities increases costs and complicates operations for institutions, weakening their ability to present a unified, powerful brand to prospective students. Here are a few of the pain points: 

    Fragmented Systems

    Multiple Platforms: Utilizing different customer relationship management (CRM) systems, student information systems (SIS), and learning management systems (LMS) introduces inefficiencies. Each platform requires its own set of training, maintenance, and integration protocols. Those protocols often don’t integrate well, either.

    Increased Costs: The need to support various tech stacks and administrative systems significantly drives up operational costs, as resources are duplicated across the board.

    Conflicting Marketing Strategies

    Brand Fragmentation: With separate marketing teams for its on-ground and online programs, an institution risks sending mixed messages to potential students. This can lead to brand dilution and confusion about what the university stands for.

    Measurement Challenges: Disparate strategies make it difficult to track and analyze the effectiveness of marketing efforts. This makes the decisions on where to invest marketing dollars effectively difficult.

    Diluted Resources

    Split Focus: Dividing an institution’s time, talent, and budget between its on-ground and online initiatives means neither receives the full investment needed to thrive. This can result in underperforming programs that fail to meet their potential.

    By managing resources under one unified strategy, universities can maximize the impact of their educational offerings, ensuring that both online and on-ground programs benefit from full institutional support and cohesion.              

    Advances in Online Learning Have Closed the Quality Gap 

    Technology is rapidly advancing, and higher ed is keeping pace with the changes. As institutions become more skilled at applying learning technologies, the following shifts have occurred: 

    • Tech Innovations: Cutting-edge technologies have enabled more interactive and engaging learning environments. Tools such as virtual reality, augmented reality, and artificial intelligence (AI) are being used to enhance classroom interactions and provide personalized learning experiences at scale.
    • Instructional Design: Modern instructional design tailors course content for digital consumption, optimizing learning outcomes. This involves structuring courses to leverage digital tools effectively, ensuring that the quality of online learning is equal to that of traditional classroom learning.
    • Faculty Training: Universities have increasingly invested in training faculty to excel in online settings. This includes equipping them with the skills to use digital tools effectively and to engage students who are not physically present. 

    Today, online courses match on-ground courses in their rigor and depth and offer the flexibility and accessibility that modern students demand. It’s a win-win. The shift isn’t just about maintaining academic standards; it’s about enhancing them to make education more inclusive and adaptable to students’ varied lifestyles.             

    The Case for a “One School” Strategy 

    As the distinction between online and on-ground academic quality becomes murkier, more universities are beginning to embrace a “one school” strategy. This holistic approach integrates online and on-ground modalities into a single, unified brand, ensuring a seamless and coherent student experience. 

    It’s kind of like how my son doesn’t see the athletics department, student advising, and his faculty members as being on different teams with different budget sources. They all make up one thing — his university and the way it feels to be a student. 

    By operating under a single brand, universities can streamline their processes, unify their messaging, and bolster their identity, enhancing their appeal in a competitive educational market. The unified brand experience provides students with a consistent set of resources and support mechanisms, which proves crucial in building trust and satisfaction.

    The shift toward a one school strategy also aligns with the evolving preferences and expectations of students, particularly their growing desire for flexible learning environments. Modern students increasingly favor hybrid experiences — asynchronous learning modules combined with synchronous meetings. This allows them to manage their schedules while benefiting from real-time interactions. 

    Adopting this approach not only improves the overall experience for students but also positions institutions to more effectively manage their resources, enhance their operational efficiency, and strengthen their academic offerings across the board, redefining the educational experience to be more inclusive and adaptable to today’s learners. 

    Adopting a one school approach helps universities accomplish goals such as the following:

    1. Establish a Unified Systems and Technology Stack

    Currently, the existence of different application systems for different modalities often leads to disparate experiences and management challenges, increasing the risk of students falling through the cracks. A unified technology stack can address these issues, fostering a more integrated and seamless educational environment.

    Using the same CRM and SIS systems across an organization can significantly streamline operations in all areas, from marketing through student retention. This unification not only reduces operational costs but also consolidates institutional data, enabling more effective tracking and support of student activities. 

    2. Create an Integrated Marketing Strategy

    Universities often work with multiple marketing agencies that compete against each other using similar keywords but with slightly different visuals and landing pages. Bad idea. This not only dilutes the marketing efforts but also creates confusion for students who are comparing programs. 

    An integrated approach helps streamline these efforts, ensuring a cohesive, clear marketing message that effectively attracts and retains students.

    3. Align Academic and Enrollment Calendars 

    A particularly troubling symptom of separate identities within a university is differing enrollment calendars for online and on-ground offerings. Online programs typically offer more start dates throughout the year. 

    With a single enrollment calendar, however, universities can eliminate this confusion and simplify the experience for students who might engage in both modalities. Additionally, as faculty members frequently teach in both online and on-ground formats, a unified calendar ensures that all students have equal access to faculty resources, regardless of the learning format. 

    A Note on Organizational … Resistance 

    While the theoretical benefits of integrating online and on-ground educational modalities are clear, the practical implementation can face organizational resistance. This stems from the “this is the way we’ve always done it” mindset, presenting real challenges in terms of system integration and cultural adaptation. 

    Addressing these challenges requires a strategic approach and readiness to tackle potential roadblocks. Here are a few things to keep in mind:

    • Data migration from separate systems can quickly become a miserable task. Institutions should consider a phased integration plan with substantial buy-in from technology teams. Prioritizing key areas and establishing a unified data strategy can streamline this process. 
    • Faculty and staff who are accustomed to operating within separate models may have concerns about changes to their job roles, security, workload, and work quality. That’s natural. Engaging stakeholders early, providing clear communication, and offering comprehensive training are important for easing this transition. 
    • Merging marketing strategies might initially increase confusion. A deliberate process beginning with a comprehensive brand audit can help align key messages and value propositions. Starting with a unified marketing plan that leverages institutional strengths can facilitate smoother consolidation, guiding teams toward a cohesive strategy.

    You Don’t Have to Implement the One School Model Alone

    Starting the journey toward overhauling the outdated model and creating a unified experience can be complex and challenging, but you don’t have to navigate it alone. 

    Archer Education is equipped to empower your institution at every step with our growth enablement approach, offering expert guidance in storytelling, technology, audience insights, and data analytics to support a seamless transition to the one school model. Then, once things are up and running, you’ll have the internal knowledge and capacities you need to cast us out to sea. 

    Contact us to learn more about how we can help you integrate your educational offerings and maximize the potential of your institution.

    Subscribe to the Higher Ed Marketing Journal:

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  • the future of learning design. – Sijen

    the future of learning design. – Sijen

    There is a looming skills deficit across all disciplines currently being taught in Universities today. The vast majority of degree programmes are, at best, gradual evolutions of what has gone before. At their worst they are static bodies of knowledge transmission awaiting a young vibrant new member of faculty to reignite them. Internal reviews are too often perfunctory exercises, seldom challenging the future direction of graduates as long as pass rates are sustained. That is until is to late and failure rates point to a ‘problem’ at a fundamental level around a degree design.

    We, collectively, are at the dawn of a new knowledge-skills-cognition revolution. The future of the professionals has been discussed for some years now. It will be a creeping, quiet, revolution (Susskind and Susskind, 2017). Although we occasionally hear about some fast food business firing all of its front-of-house staff in favour of robotic manufacturing processes and A.I. Ordering services, the reality is that in the majority of contexts the intelligent deployment of A.I. to enhance business operations requires humans to describe how these systems operate with other humans. This is because at present none of these systems score highly on any markers or Emotional Intelligence or EQ.

    Image generaed by Windows Copilot

    Arguably it has become increasingly important to ensure that graduates from any and all disciplines have been educated as to how to describe what they do and why they do it. They need to develop a higher degree of comfort with articulating each thought process and action taken. To do this we desperately need course and programme designers to desist from just describing (and therefore assessing) purely cognitive (intellectual) skills as described by Bloom et.al, and limit themselves to one or two learning outcomes using those formulations. Instead they need to elevate the psychomotor skills in particular, alongside an increasing emphasis on interpersonal ones.

    Anyone who has experimented with prompting any large language model (LLM) will tell you the language used falls squarely under the psychomotor domain. At the lowest levels one might ask to match, copy, imitate, then at mid-levels of skill deployment one might prompt a system to organise, calibrate, compete or show, rating to the highest psychomotor order of skills to ask A.I. systems to define, specify, even imagine. This progressive a type of any taxonomy allows for appropriate calibration of input and output. The ability to use language, to articulate, is an essential skill. There are some instructive (ad entertaining) YouTube videos of parents supporting their children to write instructions (here’s a great example), a skill that is seldom further developed as young people progress into tertiary studies.

    Being able to assess this skill is also challenging. When one was assessing text-based comprehension, even textual analysis, then one could get away with setting an essay question and having a semi-automated process for marking against a rudimentary rubric. Writing instructions, or explanations, of the task carried out, is not the same as verbally describing the same task. Do we imagine that speech recognition technology won’t become an increasingly part of many productive job roles. Not only do courses and programmes need to be designed around a broader range of outcomes, we also need to be continuously revising our assessment opportunities for those outcomes.

    References

    Susskind, R., & Susskind, D. (2017). The Future of the Professions: How Technology Will Transform the Work of Human Experts (Reprint edition). OUP Oxford.

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  • Equipping the Future – New Mexico Education

    Equipping the Future – New Mexico Education

    In the heart of Albuquerque’s west side, a new beacon of hope for elementary education is set to rise: Equip Academy of New Mexico.

    Spearheaded by Mercy Herrera, a Yale graduate with deep New Mexico roots, the school is designed to empower Kindergarten through 5th grade students through a unique blend of high academic expectations and culturally responsive teaching. With a personal history marked by overcoming educational challenges, Herrera is bringing her passion and vision to Equip Academy, aiming to equip every child with the tools to live out their greatness.

    On August 21 Equip Academy received unanimous approval to open as a charter school from the Public Education Commission.

    The school is set to open on Albuquerque’s west side in August 2025, with a focus to help improve student achievement and support the academic success of all students. This comes out of experience, as Herrera’s own academic journey was anything but straightforward.

    Raised in a family that moved frequently due to financial instability and personal challenges, Herrera attended multiple elementary schools, making it difficult to establish a strong academic foundation. “College seemed super out-of-reach,” she recalled, but her determination led her to Central New Mexico College (CNM), where she began to rebuild her academic confidence.

    After transferring to the University of New Mexico (UNM) and excelling in a Sign Language Interpreting Program, Herrera’s educational path took her to Harvard, where she presented research on translating scriptural metaphors from English to American Sign Language (ASL). This experience eventually led her to Yale University, where she earned her master’s degree in Disability Studies and Biblical Literature.

    In applying for Yale, Herrera didn’t tell a soul. She almost didn’t believe that someone like her, who struggled in school, could elevate to such a college. And yet, Herrera got in.

    Despite her achievements, Herrera never forgot her New Mexico roots or the struggles she faced growing up.

    Reflecting on the 2018 Yazzie-Martinez decision, which highlighted the state’s failure to provide an adequate education to many of its students, Herrera acknowledged that she would have been classified as a Yazzie-Martinez student.

    “My story isn’t unique,” Herrera said, “it’s common.”

    With support from mentors who believed in her, Herrera found the importance of quality education in shifting the narrative for students from backgrounds like hers. With the support, she made it to CNM, graduated UNM, attended an Ivy League, and earned a second masters in the Science of Teaching from New York City’s Pace University. It is this experience, and the belief that New Mexico’s students deserve to succeed, that drives the vision and mission of Equip Academy.

    “Every child has the opportunity to live out their greatness, and our commitment is to equip them to do so,” Herrera said, quoting the school’s vision.

    Equip Academy aims to provide a joyful and engaging environment with high expectations that prioritizes measurable academic learning while celebrating student curiosity and community, regardless of that student’s background.

    A key aspect of Equip Academy’s approach is its commitment to culturally responsive education. Understanding the diverse cultural landscape of New Mexico, Herrera has integrated culturally respectful education efforts into the school’s curriculum. “New Mexico has so much richness and beauty, and I think it took me leaving to understand that,” she said.

    To ensure the school is responsive to students across all walks of life, Herrera is working closely with the Hispanic Cultural Center, National Institute of Flamenco, Indian Pueblo Cultural Center and utilizing resources from the Native American Community Academy (NACA) to ensure that the school’s curriculum respects and reflects the cultural heritage of its students.

    To support students academically, Equip Academy will implement a two-teacher model for kindergarten and first grade, allowing for more individualized attention. As part of her background, Herrera has worked as a teacher instructional coach and has made teacher support a key for the school’s success.

    The school will also use cross-grade, flexible guided reading groups to ensure that students receive instruction at their individual “just right” level, helping them progress academically. Herrera emphasizes the importance of data-driven instruction and teacher excellence, which will be central to the school’s success.

    Herrera’s return to New Mexico came after years of working in high-performing charter schools in New York City and driven by a desire to bring the same level of educational excellence to her home state. The experience shaped her vision for Equip Academy, prompting her to say, “I don’t know how, and I don’t know when, but I want to start a charter school in New Mexico.”

    Now, that vision is becoming a reality.

    Equip Academy plans to open with two kindergarten classes and one first-grade class, eventually growing to serve 450 students from kindergarten through fifth grade. The school will operate on a slow-growth model, adding one grade level each year to ensure that students receive a consistent and high-quality education throughout their elementary years.

    As Herrera prepares for Equip Academy’s opening, she remains focused on the bigger picture: equipping students with the knowledge and skills they need to dream audaciously, engage deeply, and pursue lives of purpose. Her journey from a struggling student to an educational leader is proof that, with the right support and opportunities, New Mexico’s students can achieve greatness.

    Herrera’s words and hope for Equip Academy’s incoming students, “Believe in yourself, know what you want to do, and pursue it with everything you’ve got. With the right support, anything is possible.”

    Equip Academy is now accepting interest forms for future teachers and students. For more information, visit the Equip Academy LinkedIn page.

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  • Embracing the Future of HR: Your AI Questions Answered – CUPA-HR

    Embracing the Future of HR: Your AI Questions Answered – CUPA-HR

    by Julie Burrell | April 16, 2024

    In his recent webinar for CUPA-HR, Rahul Thadani, senior executive director of HR information systems at the University of Alabama at Birmingham, answered some of the most frequently raised questions about AI in HR. He also spoke to the most prevalent worries, including concerns about data privacy and whether AI will compete with humans for jobs.

    In addition to covering the basics on AI and how it works, Thadani addressed questions about the risks and rewards of using AI in HR, including:

    • How can AI speed up productivity now?
    • What AI tools should HR be using?
    • How well is AI integrated into enterprise software?
    • What are the risks and downsides of using AI?
    • What role will AI play in the future of HR?

    Thadani also put to rest a common fear about AI: that it will replace human jobs. He believes that HR is too complex, too fundamentally human a role to be automated. AI only simulates human intelligence, but it can’t make human decisions. Thadani reminded HR pros, “you all know how complex humans are, how complex decision-making is for humans.” AI can’t understand “the many components that go into hiring somebody,” for example, or how to measure employee engagement.

    AI won’t replace skilled HR professionals, but HR can’t afford to ignore AI. Thadani and other AI leaders stress that HR has a critical role to play in how AI is used on campuses. As the people experts, HR must have a seat at the table in AI discussions, partnering with IT and leadership on decisions such as how employees’ data are used and which AI software to test and purchase.

    Take the First Step

    Most people are just getting started on their AI journey. As a first step for those new to AI, Thadani recommends signing up for a ChatGPT account or another chatbot, like Google’s Gemini. He suggests using your private email account in case you need to sign a privacy agreement that doesn’t align with your institution’s policies. Test out what these chatbots are capable of by using this quick guide to chatbots.

    For leaders and supervisors, Thadani proposes having ongoing conversations within your department, on your campus and with your leadership. Some questions to consider in these conversations: Does your campus have an AI governance council? If so, is HR taking part? Do you have internal AI guidelines in place to protect data and privacy, in your department or for your campus? If not, do you have a plan to develop them? (As a leader in the AI space, the University of Michigan has AI guidelines that provide a good model, and are broken down into staff, faculty and student guidance categories.) Have you identified thought leaders in AI in your office or on your campus who can spur discussions and recommend best practices?

    In HR, “there’s definitely an eagerness to be ready and be ahead of the curve” when it comes to AI, Thadani noted. AI will undoubtedly be central to the future of work, and it’s up to HR to proactively guide how AI can be leveraged in ethical and responsible ways.

    HR-Specific Resources on AI



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  • Dr. Jennifer T. Edwards: A Texas Professor Focused on Artificial Intelligence, Health, and Education: Preparing Our Higher Education Institutions for the Future

    Dr. Jennifer T. Edwards: A Texas Professor Focused on Artificial Intelligence, Health, and Education: Preparing Our Higher Education Institutions for the Future

    As we prepare for an upcoming year, I have to stop and think about the future of higher education. The pandemic changed our students, faculty, staff, and our campus as a whole. The Education Advisory Board (EAB) provides colleges and universities across the country with resources and ideas to help the students of the future.

    I confess, I have been a complete fan of EAB and their resources for the past ten years. Their resources are at the forefront of higher education innovation.

    🏛 – Dining Halls and Food Spaces

    🏛 – Modern Student Housing

    🏛 – Hybrid and Flexible Office Spaces

    🏛 – Tech-Enabled Classrooms

    🏛 – Libraries and Learning Commons

    🏛 – Interdisciplinary Research Facilities


    Higher education institutions should also focus on the faculty and staff as well. When I ask most of my peers if they are comfortable with the numerous changes happening across their institution, most of them are uncomfortable. We need to prepare our teams for the future of higher education. 

    Here’s the Millennial Professor’s Call the Action Statements for the Higher Education Industry

    🌎 – Higher Education Conferences and Summits Need to Provide Trainings Focused on Artificial Intelligence (AI) for Their Attendees

    🌎 – Higher Education Institutions Need to Include Faculty and Staff as Part of Their Planning Process (an Important Part)

    🌎 – Higher Education Institutions Provide Wellness and Holistic Support for Faculty and Staff Who are Having Problems With Change (You Need Us and We Need Help)

    🌎 – Higher Education Institutions Need to Be Comfortable with Uncommon Spaces (Flexible Office Spaces)

    🌎 – Faculty Need to Embrace Collaboration Opportunities with Faculty at Their Institutions and Other Institutions

    Here are some additional articles about the future of higher education:

    Higher education will continue to transition in an effort to meet the needs of our current and incoming students. 

    For our particular university, we are striving to modify all of these items simultaneously. It is a challenge, but the changes are well worth the journey.

    Here’s the challenge for this post: “In your opinion, which one of the items on the list is MOST important for your institution?”

    ***. 

    Check out my book – Retaining College Students Using Technology: A Guidebook for Student Affairs and Academic Affairs Professionals.

    Remember to order copies for your team as well!


    Thanks for visiting! 


    Sincerely,


    Dr. Jennifer T. Edwards
    Professor of Communication

    Executive Director of the Texas Social Media Research Institute & Rural Communication Institute

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  • Generative AI and the Near Future of Work: An EdTech Example –

    Generative AI and the Near Future of Work: An EdTech Example –

    A friend recently asked me for advice on a problem he was wrestling with related to an issue he was having with a 1EdTech interoperability standard. It was the same old problem of a standard not quite getting true interoperability because people implement it differently. I suggested he try using a generative AI tool to fix his problem. (I’ll explain how shortly.)

    I don’t know if my idea will work yet—he promised to let me know once he tries it—but the idea got me thinking. Generative AI probably will change EdTech integration, interoperability, and the impact that interoperability standards can have on learning design. These changes, in turn, impact the roles of developers, standards bodies, and learning designers.

    In this post, I’ll provide a series of increasingly ambitious use cases related to the EdTech interoperability work of 1EdTech (formerly known as IMS Global). In each case, I’ll explore how generative could impact similar work going forward, how it changes the purpose of interoperability standards-making, and how it impacts the jobs and skills of various people whose work is touched by the standards in one way or another.

    Generative AI as duct tape: fixing QTI

    1EdTech’s Question Test Interoperability (QTI) standard is one of its oldest standards that’s still widely used. The earliest version on the 1EdTech website dates back to 2002, while the most recent version was released in 2022. You can guess from the name what it’s supposed to do. If you have a test, or a test question bank, in one LMS, QTI is supposed to let you migrate it into another without copying and pasting. It’s an import/export standard.

    It never worked well. Everybody has their own interpretation of the standard, which means that importing somebody else’s QTI export is never seamless. When speaking recently about QTI to a friend at an LMS company, I commented that it only works about 80% of the time. My friend replied, “I think you’re being generous. It probably only works about 40% of the time.” 1EdTech has learned many lessons about achieving consistent interoperability in the decades since QTI was created. But it’s hard to fix a complex legacy standard like this one.

    Meanwhile, the friend I mentioned at the top of the post asked me recently about practical advice for dealing with this state of affairs. His organization imports a lot of QTI question banks from multiple sources. So his team spends a lot of time debugging those imports. Is there an easier way?

    I thought about it.

    “Your developers probably have many examples that they’ve fixed by hand by now. They know the patterns. Take a handful of before and after examples. Embed them into a prompt in a generative AI that’s good at software code, like Hugging Chat. [As I was drafting this post, OpenAI announced that ChatGPT now has a code interpreter.] “Then give the generative AI a novel input and see if it produces the correct output.”

    Generative AI are good at pattern matching. The differences in QTI implementations are likely to have patterns to them that an LLM can detect, even if those differences change over time (because, for example, one vendor’s QTI implementation changed over time).

    In fact, pattern matching on this scale could work very well with a smaller generative AI model. We’re used to talking about ChatGPT, Google Bard, and other big-name systems that have between half a billion and a billion transformers. Think of transformers as computing legos. One major reason that ChatGPT is so impressive is that it uses a lot of computing legos. Which makes it expensive, slow, and computationally intensive. But if your goal is to match patterns against a set of relatively well-structured set of texts such as QTI files, you could probably train a much smaller model than ChatGPT to reliably translate between implementations for you. The smallest models, like Vicuña LLM, are only 7 billion transformers. That may sound like a lot but it’s small enough to run on a personal computer (or possibly even a mobile phone). Think about it this way: The QTI task we’re trying to solve for is roughly equivalent in complexity to the spell-checking and one-word type-ahead functions that you have on your phone today. A generative AI model for fixing QTI imports could probably be trained for a few hundred dollars and run for pennies.

    This use case has some other desirable characteristics. First, it doesn’t have to work at high volume in real time. It can be a batch process. Throw the dirty dishes in the dishwasher, turn it on, and take out the clean dishes when the machine shuts off. Second, the task has no significant security risks and wouldn’t expose any personally identifiable information. Third, nothing terrible happens if the thing gets a conversion wrong every now and then. Maybe the organization would have to fix 5% of the conversions rather than 100%. And overall, it should be relatively cheap. Maybe not as cheap as running an old-fashioned deterministic program that’s optimized for efficiency. But maybe cheap enough to be worth it. Particularly if the organization has to keep adding new and different QTI implementation imports. It might be easier and faster to adjust the model with fine-tuning or prompting than it would be to revise a set of if/then statements in a traditional program.

    How would the need for skilled programmers change? Somebody would still need to understand how the QTI mappings work well enough to keep the generative AI humming along. And somebody would have to know how to take care of the AI itself (although that process is getting easier every day, especially for this kind of a use case). The repetitive work they are doing now would be replaced by the software over time, freeing up the human brains for other things that human brains are particularly good at. In other words, you can’t get rid of your programmer but you can have that person engaging in more challenging, high-value work than import bug whack-a-mole.

    How does it change the standards-making process? In the short term, I’d argue that 1EdTech should absolutely try to build an open-source generative AI of the type I’m describing rather than trying to fix QTI, which is a task they’ve not succeeded in doing over 20 years. This strikes me as a far shorter path to achieving the original purpose for which QTI was intended, which is to move question banks from one system to another.

    This conclusion, in turn, leads to a larger question: Do we need interoperability standards bodies in the age of AI?

    My answer is a resounding “yes.”

    Going a step further: software integration

    QTI provides data portability but not integration. It’s an import/export format. The fact that Google Docs can open up a document exported from Microsoft Word doesn’t mean that the two programs are integrated in any meaningful way.

    So let’s consider Learning Tool Interoperability (LTI). LTI was quietly revolutionary. Before it existed, any company building a specialized educational tool would have to write separate integrations for every LMS.

    The nature of education is that it’s filled with what folks in the software industry would disparagingly call “point solutions.” If you’re teaching students how to program in python, you need a python programming environment simulator. But that tool won’t help a chemistry professor who really needs virtual labs and molecular modeling tools. And none of these tools are helpful for somebody teaching English composition. There simply isn’t a single generic learning environment that will work well for teaching all subjects. None of these tools will ever sell enough to make anybody rich.

    Therefore, the companies that make these necessary niche teaching tools will tend to be small. In the early days of the LMS, they couldn’t afford to write a separate integration for every LMS. Which meant that not many specialized learning tools were created. As small as these companies’ target markets already were, many of them couldn’t afford to limit themselves to the subset of, say, chemistry professors whose universities happened to use Blackboard. It didn’t make economic sense.

    LTI changed all that. Any learning tool provider could write integration once and have their product work with every LMS. Today, 1EdTech lists 240 products that are officially certified as supporting LTI interoperability standard. Many more support the standard but are not certified.

    Would LTI have been created in a world in which generative AI existed? Maybe not. The most straightforward analogy is Zapier, which connects different software systems via their APIs. ChatGPT and its ilk could act as instant Zapier. A programmer using generative AI could use the API documentation of both systems, ask the generative AI to write integration to perform a particular purpose, and then ask the same AI for help with any debugging.

    Again, notice that one still needs a programmer. Somebody needs to be able to read the APIs, understand the goals, think about the trade-offs, give the AI clear instructions, and check the finished program. The engineering skills are still necessary. But the work of actually writing the code is greatly reduced. Maybe by enough that generative AI would have made LTI unnecessary.

    But probably not. LTI connections pass sensitive student identity and grade information back and forth. It has to be secure and reliable. The IT department has legal obligations, not to mention user expectations, that a well-tested standard helps alleviate (though not eliminate). On top of that, it’s just a bad idea to have spread bits of glue code here, there, and everywhere, regardless of whether a human or a machine writes it. Somebody—an architect—needs to look at the big picture. They need to think about maintainability, performance, security, data management, and a host of other concerns. There is value in having a single integration standard that has been widely vetted and follows a pattern of practices that IT managers can handle the same way across a wide range of product integrations.

    At some point, if a software integration fails to pass student grades to the registrar or leaks personal data, a human is responsible. We’re not close to the point where we can turn over ethical or even intellectual responsibility for those challenges to a machine. If we’re not careful, generative AI will simply write spaghetti code much faster the old days.

    The social element of knowledge work

    More broadly, there are two major value components to the technical interoperability standards process. The first is obvious: technical interoperability. It’s the software. The second is where the deeper value lies. It’s in the conversation that leads to the software. I’ve participated in a 1EdTech specification working group. When the process went well, we learned from each other. Each person at that table brought a different set of experiences to an unsolved problem. In my case, the specification we were working on sent grade rosters from the SIS to the LMS and final grades back from the LMS to the SIS. It sounds simple. It isn’t. We each brought different experiences and lessons learned regarding many aspects of the problem, from how names are represented in different cultures to how SIS and LMS users think differently in ways that impact interoperability. In the short term, a standard is always a compromise. Each creator of a software system has to make adjustments that accommodate the many ways in which others thought differently when they built their own systems. But if the process works right, everybody goes home thinking a little differently about how their systems could be built better for everybody’s benefit. In the longer term, the systems we continue to build over time reflect the lessons we learn from each other.

    Generative AI could make software integration easier. But without the conversation of the standards-making process, we would lose the opportunity to learn from each other. And if AI can reduce the time and cost of the former, then maybe participants in the standards-making effort will spend more time and energy on the latter. The process would have to be rejiggered somewhat. But at least in some cases, participants wouldn’t have to wait until the standard was finalized before they started working on implementing it. When the cost of implementation is low enough and the speed is fast enough, the process can become more of an iterative hackathon. Participants can build working prototypes more quickly. They would still have to go back to their respective organizations and do the hard work of thinking through the implications, finding problems or trade-offs and, eventually, hardening the code. But at least in some cases, parts of the standards-making process could be more fluid and rapidly iterative than they have been. We could learn from each other faster.

    This same principle could apply inside any organization or partnership in which different groups are building different software components that need to work together. Actual knowledge of the code will still be important to check and improve the work of the AI in some cases and write code in others. Generative AI is not ready to replace high-quality engineers yet. But even as it improves, humans will still be needed.

    Anthopologist John Seely Brown famously traced the drop in Xerox copier repair quality to a change in its lunch schedule for their repair technicians. It turns out that technicians learn a lot from solving real problems in the field and then sharing war stories with each other. When the company changed the schedule so that technicians had less time together, repair effectiveness dropped noticeably. I don’t know if a software program was used to optimize the scheduling but one could easily imagine that being the case. Algorithms are good at concrete problems like optimizing complex schedules. On the other hand, they have no visibility into what happens at lunch or around the coffee pot. Nobody writes those stories down. They can’t be ingested and processed by a large language model. Nor can they be put together in novel ways by quirky human minds to come up with new insights.

    That’s true in the craft of copier repair and definitely true in the craft of software engineering. I can tell you from direct experience that interoperability standards-making is much the same. We couldn’t solve the seemingly simple problem of getting the SIS to talk to the LMS until we realized that registrars and academics think differently about what a “class” or a “course” is. We figured that out by talking with each other and with our customers.

    At its heart, standards-making is a social process. It’s a group of people who have been working separately on solving similar problems coming together to develop a common solution. They do this because they’ve decided that the cost/benefit ratio of working together is better than the ratio they’ve achieved when working separately. AI lowers the costs of some work. But it doesn’t yet provide an alternative to that social interaction. If anything, it potentially lowers some of the costs of collaboration by making experimentation and iteration cheaper—if and only if the standards-making participants embrace and deliberately experiment with that change.

    That’s especially true the more 1EdTech tries to have a direct role in what it refers to as “learning impact.”

    The knowledge that’s not reflected in our words

    In 2019, I was invited to give a talk at a 1EdTech summit, which I published a version of under the title “Pedagogical Intent and Designing for Inquiry.” Generative AI was nowhere on the scene at the time. But machine learning was. At the same time, long-running disappointment and disillusionment with learning analytics—analytics that actually measure students’ progress as they are learning—was palpable.

    I opened my talk by speculating about how machine learning could have helped with SIS/LMS integration, much as I speculated earlier in the post about how generative AI might help with QTI:

    Now, today, we would have a different possible way of solving that particular interoperability problem than the one we came up with over a decade ago. We could take a large data set of roster information exported from the SIS, both before and after the IT professionals massaged it for import into the LMS, and aim a machine learning algorithm at it. We then could use that algorithm as a translator. Could we solve such an interoperability problem this way? I think that we probably could. I would have been a weaker product manager had we done it that way, because I wouldn’t have gone through the learning experience that resulted from the conversations we had to develop the specification. As a general principle, I think we need to be wary of machine learning applications in which the machines are the only ones doing the learning. That said, we could have probably solved such a problem this way and might have been able to do it in a lot less time than it took for the humans to work it out.

    I will argue that today’s EdTech interoperability challenges are different. That if we want to design interoperability for the purposes of insight into the teaching and learning process, then we cannot simply use clever algorithms to magically draw insights from the data, like a dehumidifier extracting water from thin air. Because the water isn’t there to be extracted. The insights we seek will not be anywhere in the data unless we make a conscious effort to put them there through design of our applications. In order to get real teaching and learning insights, we need to understand the intent of the students. And in order to understand that, we need insight into the learning design. We need to understand pedagogical intent.

    That new need, in turn, will require new approaches in interoperability standards-making. As hard as the challenges of the last decade have been, the challenges of the next one are much harder. They will require different people at the table having different conversations.

    Pedagogical Intent and Designing for Inquiry

    The core problem is that the key element for interpreting both student progress and the effectiveness of digital learning experiences—pedagogical intent—is not encoded in most systems. No matter how big your data set is, it doesn’t help you if the data you need aren’t in it. For this reason, I argued, fancy machine learning tricks aren’t going to give us shortcuts.

    That problem is the same, and perhaps even worse in some ways, with generative AI. All ChatGPT knows is what it’s read on the internet. And while it’s made progress in specific areas at reading between the lines, the fact is that important knowledge, including knowledge about applied learning design, simply is extremely scarce in the data it can access and even in the data living in our learning systems that it can’t access.

    The point of my talk was that interoperability standards could help by supplying critical metadata—context—if only the standards makers set that as their purpose, rather than simply making sure that quiz questions end up in the right place when migrating from one LMS to another.

    I chose to open the talk by highlighting the ambiguity of language that enables us to make art. I chose this passage from Shakespeare’s final masterpiece, The Tempest:

    O wonder!
    How many goodly creatures are there here!
    How beauteous mankind is! O brave new world
    That has such people in’t!

    William Shakespeare, The Tempest

    It’s only four lines. And yet it is packed with double entendres and the ambiguity that gives actors room to make art:

    Here’s the scene: Miranda, the speaker, is a young woman who has lived her entire life on an island with nobody but her father and a strange creature who she may think of as a brother, a friend, or a pet. One day, a ship becomes grounded on the shore of the island. And out of it comes, literally, a handsome prince, followed by a collection of strange (and presumably virile) sailors. It is this sight that prompts Miranda’s exclamation.

    As with much of Shakespeare, there are multiple possible interpretations of her words, at least one of which is off-color. Miranda could be commenting on the hunka hunka manhood walking toward her.

    “How beauteous mankind is!”

    Or. She could be commenting on how her entire world has just shifted on its axis. Until that moment, she knew of only two other people in all of existence, each of who she had known her entire life and with each of whom she had a relationship that she understood so well that she took it for granted. Suddenly, there was literally a whole world of possible people and possible relationships that she had never considered before that moment.

    “O brave new world / That has such people in’t”

    So what is on Miranda’s mind when she speaks these lines? Is it lust? Wonder? Some combination of the two? Something else?

    The text alone cannot tell us. The meaning is underdetermined by the data. Only with the metadata supplied by the actor (or the reader) can we arrive at a useful interpretation. That generative ambiguity is one of the aspects of Shakespeare’s work that makes it art.

    But Miranda is a fictional character. There is no fact of the matter about what she is thinking. When we are trying to understand the mental state of a real-life human learner, then making up our own answer because the data are not dispositive is not OK. As educators, we have a moral responsibility to understand a real-life Miranda having a real-life learning experience so that we can support her on her journey.

    Pedagogical Intent and Designing for Inquiry

    Generative AI like ChatGPT can answer questions about different ways to interpret Miranda’s lines in the play because humans have written about this question and made their answers available on the internet. If you give the chatbot an unpublished piece of poetry and ask it for an interpretation, its answers are not likely to be reliably sophisticated. While larger models are getting better at reading between the lines—a topic for a future blog post—they are not remotely as good as humans are at this yet.

    Making the implicit explicit

    This limitation of language interpretation is central to the challenge of applying generative AI to learning design. ChatGPT has reignited fantasies about robot tutors in the sky. Unfortunately, we’re not giving the AI the critical information it needs to design effective learning experiences:

    The challenge that we face as educators is that learning, which happens completely inside the heads of the learners, is invisible. We can not observe it directly. Accordingly, there are no direct constructs that represent it in the data. This isn’t a data science problem. It’s an education problem. The learning that is or isn’t happening in the students’ heads is invisible even in a face-to-face classroom. And the indirect traces we see of it are often highly ambiguous. Did the student correctly solve the physics problem because she understands the forces involved? Because she memorized a formula and recognized a situation in which it should be applied? Because she guessed right? The instructor can’t know the answer to this question unless she has designed a series of assessments that can disambiguate the student’s internal mental state.

    In turn, if we want to find traces of the student’s learning (or lack thereof) in the data, we must understand the instructor’s pedagogical intent that motivates her learning design. What competency is the assessment question that the student answered incorrectly intended to assess? Is the question intended to be a formative assessment? Or summative? If it’s formative, is it a pre-test, where the instructor is trying to discover what the student knows before the lesson begins? Is it a check for understanding? A learn-by-doing exercise? Or maybe something that’s a little more complex to define because it’s embedded in a simulation? The answers to these questions can radically change the meaning we assign to a student’s incorrect answer to the assessment question. We can’t fully and confidently interpret what her answer means in terms of her learning progress without understanding the pedagogical intent of the assessment design.

    But it’s very easy to pretend that we understand what the students’ answers mean. I could have chosen any one of many Shakespeare quotes to open this section, but the one I picked happens to be the very one from which Aldous Huxley derived the title of his dystopian novel Brave New World. In that story, intent was flattened through drugs, peer pressure, and conditioning. It was reduced to a small set of possible reactions that were useful in running the machine of society. Miranda’s words appear in the book in a bitterly ironic fashion from the mouth of the character John, a “savage” who has grown up outside of societal conditioning.

    We can easily develop “analytics” that tell us whether students consistently answer assessment questions correctly. And we can pretend that “correct answer analytics” are equivalent to “learning analytics.” But they are not. If our educational technology is going to enable rich and authentic vision of learning rather than a dystopian reductivist parody of it, then our learning analytics must capture the nuances of pedagogical intent rather than flattening it.

    This is hard.

    Pedagogical Intent and Designing for Inquiry

    Consider the following example:

    A professor knows that her students tend to develop a common misconception that causes them to make practical mistakes when applying their knowledge. She very carefully crafts her course to address this misconception. She writes the content to address it. In her tests, she provides wrong answer choices—a.k.a. “distractors”—that students would choose if they had the misconception. She can tell, both individually and collectively, whether her students are getting stuck on the misconception by how often they pick the particular distractor that fits with their mistaken understanding. Then she writes feedback that the students see when they choose that particular wrong answer. She crafts it so that it doesn’t give away the correct answer but does encourage students to rethink their mistakes.

    Imagine if all this information were encoded in the software. Their hierarchy would look something like this:

    • Here is learning objective (or competency) 1
      • Here is content about learning objective 1
        • Here is assessment question A about learning objective 1.
          • Here is distractor c in assessment question A. Distractor c addresses misconception alpha.
            • Here is feedback to distractor c. It is written specifically to help students rethink misconception alpha without giving away the answer to question A. This is critical because if we simply tell the student the answer to question A then we can’t get good data about the likelihood that the student has mastered learning objective 1.

    All of that information is in the learning designer’s head and, somehow, implicitly embedded in the content in subtle details of the writing. But good luck teasing it out by just reading the textbook if you aren’t an experienced teacher of the subject yourself.

    What if these relationships were explicit in the digital text? For individual students, we could tell which ones were getting stuck on a specific misconception. For whole courses, we could identify the spots that are causing significant numbers of students to get stuck on a learning objective or competency. And if that particular sticking point causes students to be more likely to fail either that course or a later course that relies on a correct understanding of a concept, then we could help more students persist, pass, stay in school, and graduate.

    That’s how learning analytics can work if learning designers (or learning engineers) have tools that explicitly encode pedagogical intent into a machine-readable format. They can use machine learning to help them identify and smooth over tough spots where students tend to get stuck and fall behind. They can find the clues that help them identify hidden sticking points and adjust the learning experience to help students navigate those rough spots. We know this can work because, as I wrote about in 2012, Carnegie Mellon University (among others) has been refining this science and craft for decades.

    Generative AI adds an interesting twist. The challenge with all this encoding of pedagogical intent is that it’s labor-intensive. Learning designers often don’t have time to focus on the work required to identify and improve small but high-value changes because they’re too busy getting the basics done. But generative AI that creates learning experiences modeled after the pedagogical metadata in the educational content it is trained on could provide a leg up. It could substantially speed up the work of writing the first-draft content so that designers can focus on the high-value improvements that humans are still better at than machines.

    Realistically, for example, generative AI is not likely to know particular common misconceptions that block students from mastering a competency. Or how to probe for and remediate those misconceptions. But if were trained on the right models, it could generate good first-draft content through a standards-based metadata format that could be imported into a learning platform. The format would have explicit placeholders for those critical probes and hints. Human experts. supported by machine learning. could focus their time on finding and remediating these sticking points in the learning process. Their improvements would be encoded with metadata, providing the AI with better examples of what effective educational content looks like. Which would enable the AI to generate better first-draft content.

    1EdTech could help bring about such a world through standards-making. But they’d have to think about the purpose of interoperability differently, bring different people to the table, and run a different kind of process.

    O brave new world that has such skilled people in’t

    I spoke recently to the head of product development for an AI-related infrastructure company. His product could enable me to eliminate hallucinations while maintaining references and links to original source materials, both of which would be important in generating educational content. I explained a more elaborate version of the basic idea in the previous section of this post.

    “That’s a great idea,” he said. “I can think of a huge number of applications. My last job was at Google. The training was terrible.”

    Google. The company that’s promoting the heck out of their free AI classes. The one that’s going to “disrupt the college degree” with their certificate programs. The one that everybody holds up as leading the way past traditional education and toward skills-based education.

    Their training is “terrible.”

    Yes. Of course it is. Because everybody’s training is terrible. Their learning designers have the same problem I described academic learning designers as having in the previous section. Too much to develop, too little time. Only much, much worse. Because they have far fewer course design experts (if you count faculty as course design experts). Those people are the first to get cut. And EdTech in the corporate space is generally even worse than academic EdTech. Worst of all? Nobody knows what anybody knows or what anybody needs to know.

    Academia, including 1EdTech and several other standards bodies, funded by corporate foundations, are pouring incredible amounts of time, energy, and money into building a data pipeline for tracking skills. Skill taxonomies move from repositories to learning environments, where evidence of student mastery is attached to those skills in the form of badges or comprehensive learner records. Which are then sent off to repositories and wallets.

    The problem is, pipelines are supposed to connect to endpoints. They move something valuable from the place where it is found to the place where it is needed. Many valuable skills are not well documented if they are documented at all. They appear quickly and change all the time. The field of knowledge management has largely failed to capture this information in a timely and useful way after decades of trying. And “knowledge” management has tended to focus on facts, which are easier to track than skills.

    In other words, the biggest challenge that folks interested in job skills face is not an ocean of well-understood skill information that needs to be organized but rather a problem of non-consumption. There isn’t enough real-world, real-time skill information flowing into the pipeline and few people who have real uses for it on the other side. Almost nobody in any company turns to their L&D departments to solve the kinds of skills problems that help people become more productive and advance in their careers. Certainly not at scale.

    But the raw materials for solving this problem exist. A CEO for HP once famously noted knows a lot. It just doesn’t know what it knows.

    Knowledge workers do record new and important work-related information, even if it’s in the form of notes and rough documents. Increasingly, we have meeting transcripts thanks to videoconferencing and AI speech-to-text capabilities. These artifacts could be used to train a large language model on skills as they are emerging and needed. If we could dramatically lower the cost and time required to create just-in-time, just-enough skills training then the pipeline of skills taxonomies and skill tracking would become a lot more useful. And we’d learn a lot about how it needs to be designed because we’d have many more real-world applications.

    The first pipeline we need is from skill discovery to learning content production. It’s a huge one, we’ve known about it for many decades, and we’ve made very little progress on it. Groups like 1EdTech could help us to finally make progress. But they’d have to rethink the role of interoperability standards in terms of the purpose and value of data, particularly in an AI-fueled world. This, in turn, would not only help match worker skills with labor market needs more quickly and efficiently but also create a huge industry of AI-aided learning engineers.

    Summing it up

    So where does this leave us? I see a few lessons:

    • In general, lowering the cost of coding through generative AI doesn’t eliminate the need for technical interoperability standards groups like 1EdTech. But it could narrow the value proposition for their work as currently applied in the market.
    • Software engineers, learning designers, and other skilled humans have important skills and tacit knowledge that don’t show up in text. It can’t be hoovered up by a generative AI that swallows the internet. Therefore, these skilled individuals will still be needed for some time to come.
    • We often gain access to tacit knowledge and valuable skills when skilled individuals talk to each other. The value of collaborative work, including standards work, is still high in a world of generative AI.
    • We can capture some of that tacit knowledge and those skills in machine-readable format if we set that as a goal. While doing so is not likely to lead to machines replacing humans in the near future (at least in the areas I’ve described in this post), it could lead to software that helps humans get more work done and spend more of their time working on hard problems that quirky, social human brains are good at solving.
    • 1EdTech and its constituents have more to gain than to lose by embracing generative AI thoughtfully. While I won’t draw any grand generalizations from this, I invite you to apply the thought process of this blog post to your own worlds and see what you discover.

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