Mark Scott was a major advocate for no overseas student cap last year. Picture: Jane Dempster
University of Sydney vice-chancellor Mark Scott reaffirmed that all international students are welcome at his university during a meeting of student unions on Wednesday.
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Women in higher education and industry leadership, especially in Engineering and STEM, have reshaped academia and industry through groundbreaking contributions. Over the past two centuries, they have advanced knowledge, dismantled systemic barriers, and set new standards in innovation and leadership. Yet Black women remain significantly underrepresented, highlighting the urgent need for institutional change.
After all, when we lack diversity, we limit our ability to evolve and tackle the challenges of a rapidly changing world.
Despite the progress made, the numbers remain stark. In the UK, women constitute 48% of overall academic staff, yet only 30% hold professorial roles. At present, among these, only 80+ Black women hold professorial positions across all disciplines. In the US, Black women account for just 2% of science and engineering roles. These figures underscore the persistent barriers that hinder progression into leadership roles in academia and industry.
These disparities highlight the urgent need for fundamental change to ensure equitable access to opportunities and resources.
The 200-year journey of Black women in academia has been shaped by structural barriers but also by resilience and advocacy. Initiatives like the Black Female Academics’ Network and the national EDI Hub+, led by the University of Leeds, have played pivotal roles in championing change and providing visibility and support for Black women in academia and higher education leadership. But the reality is that real change comes not just from support networks but from institutions and governance bodies truly committed to transformation through policy implementation and its incorporation into operational management.
Black women have broken barriers in education, research, and industry, driving policy changes and fostering inclusivity. They have led pioneering research, challenged outdated structures, and risen to leadership in historically non-diverse sectors. In Engineering and STEM, figures like Dr. Aprille Ericsson, the first Black woman to earn a PhD in Mechanical Engineering from Howard University, have held key roles at NASA. Yewande Akinola, a Nigerian-born engineer, has advanced sustainable water systems while advocating for diversity. In the UK, Professor Esther Akinlabi has made significant contributions to academic leadership, engineering, research, and advocacy.
These Black women, and countless others, have played critical roles, and yet their paths have not been easy. They have faced barriers, from being underestimated in their abilities to encountering biases that make progression in academia and industry far harder than it should be. It is important to highlight their successes but equally crucial to recognise the dramatic shifts needed to create a more inclusive landscape.
As the first Black female professor in the School of Mechanical Engineering at the University of Leeds, I have witnessed firsthand the impact of underrepresentation on individuals and institutions. Without diverse voices in leadership, we lose perspectives that drive innovation and meaningful change. True equity and inclusion require representation at the highest levels, where policies and practices are shaped.
Mentorship and networking are vital for career progression, yet many Black women in academia and industry lack mentors with shared experiences. Institutions must formalise support systems rather than relying on individual efforts. A cultural shift is needed, one where diversity is not just discussed but reinforced through real structural changes that create lasting opportunities.
Breaking barriers is not just about individuals but about how institutions respond. Are they fostering environments where Black women can thrive? Are they tackling unconscious bias in hiring and promotions? Are they offering real support for retention and advancement beyond just celebrating ‘firsts’? It’s time to move from symbolic gestures to tangible change that empowers the next generation in academia and industry.
The legacy of Black women in academia and industry extends beyond their achievements to the opportunities they create for future generations. Recognising and amplifying their voices is essential. Their contributions must be seamlessly woven into the broader narrative of advancement and innovation in women’s higher education and industry leadership.
Much work remains. Representation is not enough; true progress requires dismantling barriers to access and opportunity. Black women in academia and industry, especially in Engineering and STEM, must be empowered, supported, and able to lead without the constant need to justify their place.
The goal should be that, in the future, their contributions are not exceptional but expected, and their presence in leadership roles is not a rarity but the norm.
Advocates for apprenticeship programs came into a second Trump administration with a rosy outlook on their future.
Historically, these on-the-job training programs have enjoyed bipartisan support, and apprenticeships featured prominently in Project 2025, the conservative policy blueprint for a second Trump administration put forth by the Heritage Foundation, a right-wing think tank. The plan encouraged their expansion, lauding the programs as a meaningful alternative to “the woke-dominated system of public schools and universities.”
But now, apprenticeship proponents are divided on how hopeful to feel.
Some maintain their optimism. They foresee a potential period of growth for the programs, as Trump administration officials and supporters speak positively about apprenticeships and nondegree pathways.
But others worry that at least some apprenticeship programs—and their financial supports—could be hurt by the administration’s slashing of federal spending. Already, some grants supporting apprenticeship programs have been cut to trim costs or for perceived connections to diversity, equity and inclusion work. The Advisory Committee on Apprenticeship, which advises the Department of Labor on apprenticeship issues, has been disbanded, along with other federal advisory bodies.
“If the approach is to just cut, cut, cut grants across the government—and the kind of machete-wielding, indiscriminate cutting of things continues—I think that could pose some long-term stress on the system and halt a lot of the momentum that it’s had,” said Taylor White, director of postsecondary pathways for youth at New America, a left-wing think tank, and a former member of the now-defunct advisory committee. She fears the uncertainty caused by federal spending cuts in general could scare off employers or state agencies that otherwise would have invested in these programs.
Apprenticeship-related grants have gotten “caught up” in efforts to scrutinize government spending, said Vinz Koller, vice president of the Center for Apprenticeship and Work-Based Learning at Jobs for the Future, an organization focused on workforce development, though he doesn’t think “they’ve been the target” or that the moves are necessarily indicative of apprenticeships’ future under Trump.
“What we are hearing from the administration is a commitment to registered apprenticeship and to apprenticeship writ large and to making it more widely accessible,” he said. “That leads us to believe, looking into the future, that’s where we’re headed.”
John Colborn, executive director of Apprenticeships for America, a nonprofit working to expand apprenticeships in the U.S., said it’s “too early to say for sure” what the next four years hold for apprenticeships. But he sees “plenty of positive signs out there,” including supportive rhetoric from current and nominated Trump administration officials.
For example, Secretary of Education Linda McMahon posted on X in November that apprenticeship programs “are a pathway to successful careers,” praising Switzerland’s apprenticeship system as “a model the rest of the world can adapt.”
Similarly, Trump’s pick for secretary of labor, Lori Chavez-DeRemer, said during her Feb. 19 confirmation hearing that she values investing in and “doubling down” on registered or federally recognized apprenticeships.
“Right now, we’re focused on the registered apprenticeships, growing those, investing in those and making sure that those are adhered to,” she told lawmakers.
Her comments were a notable departure from the vision for apprenticeships laid out in Project 2025, which called for a return to an earlier Trump policy of industry-recognized apprenticeships, a separate system to circumvent what Republican lawmakers view as excessive federal regulation. Registered apprenticeships are required to meet certain quality standards to receive federal dollars.
Chavez-DeRemer’s position “came as good news to many of us listening and watching,” White said, though she wonders if Chavez-DeRemer will retain that stance if there’s pressure from the administration to do otherwise.
Colborn believes the current administration might improve the registered apprenticeship system, including speeding up program approvals and expanding the types of occupations that offer apprenticeship options.
He added that so far, the Trump administration hasn’t interfered with financial supports for apprenticeships that the Biden administration instituted. Under Biden, the Department of Labor announced the State Apprenticeship Expansion Formula grant program, which makes $85 million available for states and territories to grow the capacity of existing registered apprenticeships and invest in new offerings.
“I don’t have any official word on this, but every indication we have is that that grant process is going forward,” Colborn said. “We take that as a signifier that this administration is committed to apprenticeship.”
Some apprenticeship advocates hope the moment might be ripe to push for more support and see their policy wish lists fulfilled, including more reliable federal and state funding for apprenticeships, rather than one-off grants, and incentives like tax credits for employers to participate in apprenticeship programs.
“There’s definitely room for the administration to make a mark on the broadening of apprenticeship into more sectors where traditionally they haven’t been as common,” Koller said.
Still, some advocates worry apprenticeships will be negatively affected by other policies advanced by the Trump administration.
White, for example, was jarred by the Department of Labor’s decision to ax its Advisory Committee on Apprenticeship, a group of about 30 employers, labor organization representatives and other stakeholders that advises the department on apprenticeship-related policy.
She doesn’t believe the move was intended to signal an anti-apprenticeship stance, given that the committee isn’t the only federal advisory body to bite the dust. A February executive order got rid of a handful of them and called on government officials to flag “Federal Advisory Committees that should be terminated on grounds that they are unnecessary.”
But the disbanding of the committee still feels like a “confusing signal” and a potential obstacle to progress, White said.
“What’s lost by dissolving a community like that is the connection to the people on the ground who are actually having to interpret regulation, live regulation, build the programs, implement the programs,” she added. She sees such perspectives as critical to making apprenticeships “more efficient, more accessible, more functional and, frankly, a more mainstream training option for Americans to access high-quality training and good middle-class jobs.”
Like the advisory committee, some federal funding for apprenticeship programs and apprenticeship-related research projects has gotten caught in the crossfire as the administration works to downsize government and curtail DEI work.
Notably, the Department of Government Efficiency’s website shows about $18 million in cuts to three grants issued by the Department of Labor’s Office of Apprenticeship, according to The Job, a newsletter that covers education and workforce issues.
Managed by the consulting firm ICF, one of the grants was for “technical assistance and coaching support” and one for “industry engagement and outreach.” DOGE’s documentation said only that the grants were terminated “for convenience,” meaning the cuts were in the government’s interest. Another impacted grant was for “technical and coaching assistance support,” managed by a subsidiary of the American Institutes for Research. The Job also reported in late February that several research projects related to apprenticeships had their federal funding frozen.
Another victim of federal cuts was Reach University, a nonprofit institution with a mission to offer on-the-job credentials, called apprenticeship degrees. The institution lost three grants, totaling $14.7 million, from the Education Department. (Teacher-training grants at other institutions have also been slashed for supposed connections to DEI. Three teacher preparation groups sued the Department on Monday over the slew of grant cuts in the field.)
The grants to Reach were supposed to support apprenticeship-based degree programs training teachers in rural Arkansas and Louisiana through 2028. One program helps associate degree holders earn bachelor’s degrees while learning teaching skills on the job in local schools. (The grant application mentioned that the program is a partnership with Delgado Community College, a predominantly Black institution in New Orleans, and would “increase the number of teachers of color in high-need Louisiana schools,” The Job reported.) The other two grants were partnerships with nonprofits to help people in more isolated rural areas earn teaching credentials on the job.
Joe E. Ross, president and CEO of Reach, wrote to Inside Higher Ed that he remains “hopeful” the university will regain the funds through the Education Department’s internal appeals process, and he said university leaders are in touch with department officials. Despite the financial hits, he’s optimistic the administration will be good news for apprenticeships over all.
“We are confident that the projects funded by these grants align with long-standing, bipartisan priorities, including those of this administration,” Ross said. “As applied by Reach, all three of these grants are a merit-based, discrimination-free application of federal funds to meet the department’s long-held priority of alleviating the teacher shortage with residents of the local community.”
I’ve worked closely with colleagues in Silicon Valley throughout my career. Through these interaction, there are always new ideas, and the level of confidence in predictions typically starts strong and only gets stronger. This time felt different. Last week during a visit to Silicon Valley, I repeatedly heard the following as a preface to a prediction, and I can’t say I’ve ever heard it before when engaging with my most techno-optimistic colleagues: “I could be wrong, but …”
A few innocent words, but a rhetorical hedge that suggests even the most confident among us understand that the AI era is pretty, pretty complicated.
I was there to attend the Annual AI+Education Summit 2025, hosted by Stanford’s Institute for Human-Centered Artificial Intelligence (HAI) and the Stanford Accelerator for Learning. The theme—Human-Centered AI for a Thriving Learning Ecosystem—framed discussions that were both urgent and inspiring. AI is not just on the horizon; it is actively reshaping the educational landscape. Our responsibility is to ensure this transformation augments human potential rather than diminishes it.
The summit brought together leading researchers, educators and policymakers to explore AI’s role in personalizing learning, empowering educators and bridging educational divides. The pace of change is staggering—today, half of students use AI tools at least weekly, both inside and outside the classroom. Institutions must act now to shape AI’s role in education intentionally rather than reactively.
One of the key messages from the summit was that no single institution, company, innovator or researcher can tackle this challenge alone. A coordinated effort across higher education is essential to ensure AI serves students, faculty and society in equitable and effective ways.
At the University of Michigan, we have seen firsthand how faculty innovators are experimenting with generative AI to enhance teaching and learning. Our most recent call for proposals at the Center for Academic Innovation resulted in a diverse set of AI-enhanced teaching and learning projects designed to explore AI’s potential across disciplines, from medical education to humanities. These projects demonstrate not only how AI can enrich classroom experiences but also how it can deepen engagement, personalize learning and extend human creativity. We are helping faculty translate emerging technologies into meaningful applications, creating impactful learning experiences on campus and beyond.
Organizations like U-M’s Center for Academic Innovation and Stanford’s HAI and the Stanford Accelerator for Learning play a critical role in leading this work—through experimentation, research and convening communities of practice. Without spaces to explore AI’s potential responsibly, without research to test its effectiveness and without convenings to align efforts, the future of AI in education would be left to chance rather than deliberate innovation.
Michigan’s work is part of a broader movement. Across higher education, institutions are launching AI-driven initiatives to explore the role of AI in teaching, learning and research. One example is the California State University system, which recently announced a partnership with OpenAI to explore AI’s potential across its 23 campuses. This initiative, like many others, underscores the need for systemwide efforts to develop responsible and scalable AI solutions.
These efforts—faculty-led experiments at Michigan, large-scale system initiatives like CSU’s, and global convenings like Stanford’s AI+Education Summit—demonstrate the range of approaches to AI in education. Stanford’s summit, in particular, highlighted outstanding faculty-led experiments exploring AI’s role in augmenting learning, fostering creativity and addressing challenges in equitable access to technology. These initiatives reinforce the importance of institutional collaboration in shaping the future of AI in education. But the big question remains: How do we shape AI’s role in education to serve our preferred future rather than react to an imposed one?
AI is already reshaping how students learn and how educators teach. We must ensure AI serves as a tool for augmentation rather than automation. How do we steer away from optimizing automation and toward optimizing AI’s ability to augment human creativity, problem-solving and collaboration?
Some of the most compelling AI applications in education are emerging from faculty-led experimentation. Universities must create conditions for responsible innovation by investing in faculty training, providing resources for experimentation and developing ethical frameworks that support AI integration while prioritizing student learning. We need to understand what’s working for whom and be ready to quickly invest further in the most impactful efforts.
AI’s potential to amplify biases and exacerbate inequities is well documented. Institutions must focus on governance, transparency and bias mitigation to ensure AI benefits all learners. Without clear institutional leadership, regulation will fill the void. Can we build governance frameworks that protect learners and help them to flourish while also fostering innovation and global competitiveness and security?
There is universal agreement that students, educators and institutions need to accelerate AI literacy. However, what constitutes AI literacy remains unclear. Should AI literacy be about technical proficiency? Ethical responsibility? Practical applications? Probably all of the above—but the right balance is elusive. I could be wrong, but if we don’t actively shape this now, we may find that AI literacy is defined for us in ways that don’t align with our values. Definitions vary, but there is broad consensus that we need highly accessible and scalable opportunities for anyone to acquire AI literacy—and soon.
The AI+Education Summit made it clear that AI’s impact should be shaped by the collective choices of educators, institutions and policymakers. Without a shared vision, the future will be dictated by market forces alone. Speakers at the conference described the future they want to see: one that designs for the widest range of learners to support human flourishing, strengthens the essential relationship between teachers and students, and works for everyone—practically, equitably and responsibly.
Institutions have taken very different approaches to AI—some choosing to ban it, restricting its use until clearer guidelines emerge, while others have opted to embrace it, fostering a culture of experimentation and innovation. Others have decided to take a wait-and-see approach, uncertain about how AI will ultimately shape higher education. Perhaps all of these strategies have their merits. Or maybe in a few years we’ll look back and realize the most effective approach was something we haven’t even considered yet. I could be wrong—but that’s precisely why we need a wide range of perspectives shaping this conversation now.
As institutions embrace AI, we should ask ourselves:
The AI+Education Summit reinforced that we are not passive observers of AI’s impact on education—we are active participants in shaping its trajectory. The work happening at Stanford, Michigan, CSU and across the broader higher ed community signals a growing recognition that AI is not just another technology to integrate but a transformational force that demands intentionality, collaboration and vision.
Yet, it would be a collective failure if we simply make it easy for students to offload critical thinking. AI must not become a shortcut that undermines the cognitive skills we seek to develop in our learners and citizens.
Now is the time for institutions and individuals to come together, share knowledge and create our preferred future for AI in education. We don’t have all the answers, and some of today’s best ideas may prove incomplete or even misguided. It feels like there is little time for passive observation. AI’s role in education will be defined—either by us or for us. Let’s build the future we prefer—because if we don’t, well … I could be wrong, but I doubt we’ll like the alternative.
In contrast to the adage that ‘good strategy closes doors’, the Office for Students (OfS) Strategy consultation has left many options open. This is true of the Teaching Excellence Framework (TEF), which the OfS intends to bring into alignment with its wider quality regime:
‘TEF will be the core of our new integrated approach to quality, with assessment activity becoming more routine and more widespread to ensure that institutions are delivering high quality academic experiences and positive outcomes’.
The OfS has stated in its consultation that it will expand its quality assessment regime without evaluating how this exercise has, or will, enhance education provision.
Previous investigations were seen as burdensome and lacking transparency.[1] On transparency, Professor Amanda Broderick, Vice-Chancellor & President at the University of East London, reflected on a quality investigation: ‘…we were not informed of what the OfS’s concerns had been at any point of the review’.
On burden, Professor David Phoenix, Vice-Chancellor of London South Bank University, has written about an investigation at his provider: ‘…providers are already very used to…scrutiny. Professional and regulatory bodies (PSRBs) have their own approaches to course review and validation, and in many instances the level of scrutiny can greatly exceed that of the OfS’.
And in a recent HEPI blog, the ex-higher education minister and architect of TEF Lord Jo Johnson asserts that the OfS has consistently deprioritised innovation.
So perhaps the OfS has reached a moment of choice: to stick or pivot.
Stephanie Marshall has written previously about the different global ‘pivots’ in higher education quality: ‘massification, quality assurance, quality enhancement, and then a move to addressing equity deploying large data’.
The OfS’s decision to pause new provider entrants has arguably stalled massification. It is duplicative when it comes to assurance with other regulators such as Ofsted. And its deployment of data through the Data Futures process is beset by delays. Instead of enabling providers to embrace change, an unintended consequence of these decisions is that sector innovation is slowed. Amidst this and the sector’s financial challenges, the OfS seeks to expand its investigatory regime without a clear theory of change linked to enhancement.
In a Norwegian report to which Marshall contributed, it was noted that: ‘In English, the term ‘excellence’ is now much overused…In Norwegian the word “fremragende” has a sense of moving forward (frem) and upward (tall or reaching above the rest, ragende) and is reserved to describe something really cutting-edge’.[2]
Centres for disciplinary excellence in education were established in Norway through the Centres for Excellence (CfE) Initiative, introduced by their previous Quality Assurance body, NOKUT. To be eligible for CfE status and funding, higher education institutions had to meet baseline standards and evaluate the distinctive quality of their provision. Each Centre selected its own criteria aligned to the provider’s vision and mission.
Of course, there were challenges with this process, particularly when it came to differences in judgements of the panel assessing, against the institution being assessed. However, NOKUT was open to evolving its views, positioning itself as a ‘critical friend’. This process set out to be supportive and iterative, focused on both past impact and continuous improvement. The success of this approach has been validated over the years by regular evaluations of the impact of the scheme.
In England there were 227 providers who participated in TEF. Adopting a system from a country with 21 higher education providers is clearly not practical. The important lessons are, firstly, a critical friend approach can be beneficial to enhancement, and, secondly, institutions can be trusted to evolve some of their quality metrics in line with their mission and values. This is particularly important in a system as diverse as in England where most providers are already above the quality baseline.
Fremragende may be a more accurate framing of authentic educational enhancement rather than the English buzzword ‘excellence’. Frenragende suggests an ongoing journey: a verb rather than a noun. The higher education environment is and will be in a state of flux where quality frameworks need to be agile and unlock innovation, particularly in the territory of AI.
The OfS has a basket of lagging indicators: the National Student Survey (NSS) and Graduate Outcomes Survey (GOS) which comprise the TEF. If they are utilised in the next TEF, which seems likely, one way to begin to move from assurance to continuous improvement could be for the OfS to encourage greater use of the optional NSS bank. There are additional questions in place regarding the views of healthcare students, and several optional additional questions. An integrated approach could also be taken to the questions within the GOS, either enabling some optional questions for graduates, or mapping the GOS questions to those in the NSS.
This flexibility would demonstrate trust, give providers a way to articulate ‘learning gain’, and capture the diversity in the sector. It would also maintain many of the positive aspects of TEF for key stakeholders, including the centrality of the student voice through the NSS and other mechanisms.
Any approach to integration should be a partnership with students, providers, international organisations and employers. We hope that entrance into the International Network for Quality Assurance Agencies in Higher Education will enable the OfS to collaborate with other global quality bodies.
The OfS should consider how, in its assessment of excellence, it integrates learning from other inspection regimes, such as Ofsted and existing PSRB requirements. Through this, it should reduce regulatory duplication. This is in line with the Regulator’s Code principle of ‘collect once, use many times’.
A mindset shift from assessing the baseline to forward-facing, continuous enhancement is required, both by the OfS and the sector. With further contextualisation of provision, the sector can exercise its autonomy to drive excellence, and the OfS can fulfil its statutory role in enabling quality and innovation.
Let’s join our Norwegian colleagues in adopting the fremragende approach.
Mielad Ziaee
Healthcare is constantly evolving, and the future of the industry depends on the next generation of skilled professionals who are prepared to lead with knowledge, innovation, and compassion. Organizations like HOSA-Future Health Professionals play a critical role in shaping these future healthcare leaders by providing students with the resources, experiences, and mentorship needed to thrive in various medical and health-related careers.
HOSA is an international student organization dedicated to empowering young people who are passionate about healthcare. Through leadership development, competitive events, networking opportunities, and hands-on learning experiences, HOSA helps students build the essential skills they need to succeed in the medical field. Members engage in real-world healthcare scenarios, gain exposure to public health initiatives, and develop professional competencies that set them apart in their future careers.
Alumni and Former International Executive Council Member, HOSA-Future Health Professionals
One such success story is Mielad Ziaee, a Marshall Scholar, Truman Scholar, public health advocate, and innovator. Ziaee’s parents immigrated from Iran to the United States to manifest a new life. Their resilience and perspective deeply influenced his understanding of community, determination, and health from an early age. Before hitting the labs of prestigious institutions, Ziaee joined HOSA as a high school freshman thanks to their support. He saw it as a promising steppingstone to engage in his healthcare aspirations.
“I really wanted to hit the ground running with HOSA. It was so empowering to have [an organization] created for students interested in healthcare, where we could all sort of geek out together,” Ziaee recalled.
His two advisors, Angela Vong and Zenia Ridley, provided mentorship and guidance to immerse Ziaee in all HOSA could offer. His leadership journey quickly unfolded — from member to area officer, to Texas state officer, and eventually, to serving on HOSA’s International Executive Council. His tenure coincided with the challenges of the COVID-19 pandemic. Finding creative ways to engage members across middle school, high school, and college in virtual settings connected the dots between leadership and innovation.
“Being part of the ‘COVID generation’ was both challenging and inspiring,” Ziaee shared. “It taught me how to build community and how that community can enact change.”
Ziaee’s experiences ignited a passion for research, where he found the intersection of policy, public health, technology, and community engagement. In particular, food insecurity has become a focal point of his academic work.
“I’m a proud Houstonian. I go to the University of Houston, so one of the biggest challenges that my community faces is food insecurity,” Ziaee said. “I work with our Data Science Institute to try to understand both technological and community-based cultural approaches to food insecurity. A lot of the skills I learned in HOSA, like Zoom calls or identifying key problems and addressing them, are the same things I do in my research — just different vocabulary.”
Ziaee will continue his study of public health as a Marshall Scholar at the University of Edinburgh this fall. He beamed with excitement as he described studying at an institution that nurtured scientific legends such as Charles Darwin and Alexander Graham Bell and exploring Scotland’s unique healthcare system.
“Edinburgh, specifically, is where they did the Dolly the Sheep experiment, which is super cool,” Ziaee said. “It’s very interesting as an American to see how they’re doing things [in Scotland], and to hopefully bring that back and promote policy innovation here in public health.”
Reflecting on his journey, Ziaee underscores the importance of seizing opportunities and embracing HOSA as more than just an organization. As Ziaee embarks on this next chapter, his story exemplifies how HOSA-Future Health Professionals and strong family values can shape a life of innovation and impact. Following in his footsteps, his younger sister has now joined HOSA, continuing the family’s commitment to making a difference in healthcare.
“The connections and experiences you gain will inspire and guide you for years to come. It’s one of the main constants in my life,” he said.
Ziaee’s journey highlights that HOSA is more than just a steppingstone — it’s a foundation for lifelong growth, leadership, and meaningful connections. For students aspiring to make a difference in healthcare, organizations like HOSA offer an unparalleled opportunity to gain real-world experience, develop leadership skills, and join a network of like-minded individuals committed to improving health outcomes worldwide.
To join this inspiring legacy, become part of the HOSA alumni network today at www.hosa.org/alumni.
Key points:
In the words of Carol Dweck, “Becoming is better than being.” As novice sixth grade math and English teachers, we’ve learned to approach our mid-year benchmark assessments not as final judgments but as tools for reflection and growth. Many of our students entered the school year below grade level, and while achieving grade-level mastery is challenging, a growth mindset allows us to see their potential, celebrate progress, and plan for further successes amongst our students. This perspective transforms data analysis into an empowering process; data is a tool for improvement amongst our students rather than a measure of failure.
A growth mindset is the belief that abilities grow through effort and persistence. This mindset shapes how we view data. Instead of focusing on what students can’t do, we emphasize what they can achieve. For us, this means turning gaps into opportunities for growth and modeling optimism and resilience for our students. When reviewing data, we don’t dwell on weaknesses. We set small and achievable goals to help students move forward to build confidence and momentum.
Celebrating progress is vital. Even small wins (i.e., moving from a kindergarten grade-level to a 1st– or 2nd-grade level, significant growth in one domain, etc.) are causes for recognition. Highlighting these successes motivates students and shows them that effort leads to results.
Involving students in the process is also advantageous. At student-led conferences, our students presented their data via slideshows that they created after they reviewed their growth, identified their strengths, and generated next steps with their teachers. This allowed them to feel and have tremendous ownership over their learning. In addition, interdisciplinary collaboration at our weekly professional learning communities (PLCs) has strengthened this process. To support our students who struggle in English and math, we work together to address overlapping challenges (i.e., teaching math vocabulary, chunking word-problems, etc.) to ensure students build skills in connected and meaningful ways.
We also address the social-emotional side of learning. Many students come to us with fixed mindsets by believing they’re just “bad at math” or “not good readers.” We counter this by celebrating effort, by normalizing struggle, and by creating a safe and supportive environment where mistakes are part of learning. Progress is often slow, but it’s real. Students may not reach grade-level standards in one year, but gains in confidence, skills, and mindset set the stage for future success, as evidenced by our students’ mid-year benchmark results. We emphasize the concept of having a “growth mindset,” because in the words of Denzel Washington, “The road to success is always under construction.” By embracing growth and seeing potential in every student, improvement, resilience, and hope will allow for a brighter future.
Since the public release of ChatGPT in late 2022, artificial intelligence has rocketed from relative obscurity to near ubiquity. The rate of adoption for generative AI tools has outpaced that of personal computers and the internet. There is widespread optimism that, on one hand, AI will generate economic growth, spur innovation and elevate the role of quintessential “human work.” On the other hand, there’s palpable anxiety that AI will disrupt the economy through workforce automation and exacerbate pre-existing inequities.
History shows that education and training are key factors for weathering economic volatility. Yet, it is not entirely clear how postsecondary education providers can equip learners with the resources they need to thrive in an increasingly AI-driven workforce.
Here at the University of Tennessee, Knoxville’s Education Research and Opportunity Center, we are leading a three-year study in partnership with the Tennessee Board of Regents, Advance CTE and the Association for Career and Technical Education to explore this very subject. So far, we have interviewed more than 20 experts in AI, labor economics, career and technical education (CTE), and workforce development. Here are three things you should know.
First, AI is not new. ChatGPT continues to captivate attention because of its striking ability to reason, write and speak like a human. Yet, the science of developing machines and systems to mimic human functions has existed for decades. Many people are hearing about machine learning for the first time, but it has powered their Netflix recommendations for years. That said, generative AI does represent a leap forward—a big one. Simple machine learning cannot compose a concerto, write and debug computer code, or generate a grocery list for your family. Generative AI can do all of these things and infinitely more. It certainly feels futuristic, but it is not; AI is the present. And the generative AI of the present is not the AI of tomorrow.
Our interviews with experts have made clear that no one knows where AI will be in 15, 10 or even five years, but the consensus predicts the pace of change will be dramatic. How can students, education providers and employers keep up?
First, we cannot get hung up on specific tools, applications or use cases. The solution is not simply to incorporate ChatGPT in the classroom, though this is a fine starting point. We are in a speeding vehicle; our focus out the window needs to be on the surrounding landscape, not the passing objects. We need education policies that promote organizational efficiency, incentivize innovation and strengthen public-private partnerships. We need educational leadership focused on the processes, infrastructure and resources required to rapidly deploy technologies, break down disciplinary silos and guarantee learner safeguards. We need systemic and sustained professional development and training for incumbent faculty, and we need to reimagine how we prepare and hire new faculty. In short, we need to focus on building more agile, more adaptable, less siloed and less reactive institutions and classrooms because generative AI as we know it is not the future; AI is a harbinger of what is to come.
It is exceedingly difficult to predict which individual occupations will be impacted—positively or negatively—by AI. We simply cannot know for certain whether surgeons or meat slaughterers are at greatest risk of AI-driven automation. Not only is it guesswork, but it is also flawed thinking, rooted in a misunderstanding of how technology impacts work. Tasks constitute jobs, jobs constitute occupations and occupations constitute industries. Lessons from prior technological innovations tell us that technologies act on tasks directly, and occupations only indirectly. If, for example, the human skill required to complete a number of job-related tasks can be substituted by smart machines, the skill composition of the occupation will change. An entire occupation can be eliminated if a sufficiently high share of the skills can be automated by machines. That said, it is equally true (and likely) that new technologies can shift the skill composition of an occupation in a way that actually enhances the demand for human workers. Shifts in demands for skills within the labor market can even generate entirely new jobs. The point is that the traditional approach to thinking of education in terms of majors, courses and degrees does learners a disservice.
By contrast, our focus needs to be on the skills learners acquire, regardless of discipline or degree pathway. A predictable response to the rise of AI is to funnel more learners into STEM and other supposed AI-ready majors. But our conversations, along with existing research, suggest learners can benefit equally from majoring in liberal studies or art history so long as they are equipped with in-demand skills that cannot (yet) be substituted by smart machines.
We can no longer allow disciplines to “own” certain skills. Every student, across every area of study, must be equipped with both technical and transferable skills. Technical skills allow learners to perform occupation-specific tasks. Transferable skills—such as critical thinking, adaptability and creativity—transcend occupations and technologies and position learners for the “work of the future.” To nurture this transition, we need innovative approaches to packaging and delivering education and training. Institutional leaders can help by equipping faculty with professional development resources and incentives to break out of disciplinary silos. We also need to reconsider current approaches to institutional- and course-level assessment. Accreditors can help by pushing institutions to think beyond traditional metrics of institutional effectiveness.
From our conversations with experts, one realization is apparent: There are few corners of the workforce that will be left untouched by AI. Sure, AI is not (yet) able to unclog a drain, take wedding photos, install or repair jet engines, trim trees, or create a nurturing kindergarten classroom environment. But AI will, if it has not already, change the ways in which these jobs are performed. For example, AI-powered software can analyze plumbing system data to predict problems, such as water leaks, before they happen. AI tools can similarly analyze aircraft systems, sensors and maintenance records to predict aircraft maintenance needs before they become hazardous, minimizing aircraft downtime. There is a viable AI use case for every industry now. The key factor for thriving in the AI economy is, therefore, the ability to use AI effectively and critically regardless of one’s occupation or industry.
AI is good, but it is not yet perfect. Jobs still require human oversight. Discerning the quality of sources or synthesizing contradictory viewpoints to make meaningful judgments remain uniquely human skills that cut across all occupations and industries. To thrive in the present and future of work, we must embrace and nurture this skill set while effectively collaborating with AI technology. This effective collaboration itself is a skill.
To usher in this paradigm shift, we need federal- and state-level policymakers to prioritize AI user privacy and safety so tools can be trusted and deployed rapidly to classrooms across the country. It is also imperative that we make a generational investment in applied research in human-AI interaction so we can identify and scale best practices. In the classroom, students need comprehensive exposure to and experience with AI at the beginnings and ends of their programs. It is a valuable skill to work well with others, and in a modern era, it is equally necessary to work well with machines. Paraphrasing Jensen Huang, the CEO of Nvidia: Students are not going to lose their jobs to AI; they will lose their jobs to someone who uses AI.
Is artificial intelligence (AI) going to transform our universities? Or will it destroy the need for a tertiary education? Right now, it’s impossible to tell.
If you read the media, you’re likely to think things will end up at one extreme or the other. That’s because we are living in an age of AI hype, where exaggerated claims about the technology – both on the plus side from the biggest AI engineering firms, and on the downside from those concerned about a dystopian future – are dominating the conversation.
For those of us who aren’t computer scientists or software engineers with domain expertise, wouldn’t it be helpful to have a guide to help us unpack what’s going on and figure out how to engage with this technology that may prove to be world-altering?
If you’re a head of state or a billionaire, then you probably already have an AI advisor. For the rest of us, Arvind Narayanan and Sayash Kapoor, two computer scientists at Princeton University, have kindly written AI Snake Oil as a layman’s roadmap to the current and likely future trajectory of the technology. (Alongside the book the pair have launched a website that’s full of the most current commentary and analysis.)
Narayanan and Kapoor are concerned with the full gamut of AI, not just the ‘generative’ variety that has garnered so much attention since its ‘debut’ with the arrival of ChatGPT. They helpfully separate AI into three main streams: Predictive AI, Generative AI and Content Moderation AI. All three suffer from claims of exaggerated effectiveness, a lack of scientific evidence and fantastic claims about their future capabilities.
For the purposes of a higher education audience, it’s generative AI that’s of most interest, because that’s the technology that can simulate the intellectual output of an educated brain – whether in the form of text or visual imagery. They put genAI into its historical context: most of us don’t know that the neural network theory that underpins genAI goes back to the 1950s, and that it’s been through a series of cycles of hype and disappointment.
Sadly, the authors aren’t particularly interested in the impact of genAI on higher education, apart from noting off-handedly that the technology appears to be largely undetectable, and that financially-strapped universities that think the technology will deliver endless efficiency dividends may be sadly disappointed. At various points they mention how they encourage active engagement with AI to understand what it can and cannot do, all from the perspective of their lives at Princeton. That’s not particularly helpful given how outlandishly wealthy, privileged, and tiny that university is.
Also, the authors miss an opportunity to explore different types of genAI technologies, particularly those that may be designed to encourage learning versus others that improve human productivity by offloading cognitive effort. No doubt the latter are already transforming human work, but whether they have a place in higher education is a different question.
There is a concept in AI known as ‘alignment’, which refers to the risk that uncontrolled AI may, as it approaches more powerful levels of general intelligence, act against the interests of humans and harm (or even kill) us. It’s controversial, and the authors devote an entire chapter to how we should think about, and respond to, technology companies’ pursuit of artificial general intelligence (AGI).
From the perspective of higher education, our sector may be better served in the immediate term by thinking about alignment in terms of the interests of educational institutions and the (mostly American) technology companies that are at the vanguard of developing genAI. The culture of incrementalism that has traditionally served universities well may not be so effective when dealing with such a rapidly approaching paradigm shift in humans’ relationship with technology.
The conclusion of AI Snake Oil is a little surprising. The authors make clear that humanity’s relationship with AI will be determined by all of us –individuals and institutions, as well as regulators and politicians. No doubt there is an opportunity for universities and their leaders to take a leading role in shaping this conversation, using their institutional resources and cultural authority to help inform the public and guide us all toward a better relationship with ever more powerful computers.
We all need to be educated, informed, and willing to speak up – so that we don’t end up living in a world where AI is dominated by the largest and most powerful corporations the planet has ever seen. That will be the worst of all possible outcomes.
Studiosity is a learning technology company that works with 100+ universities globally and serving 2.2 million university students across the UK, Australia, New Zealand, Canada, and the Middle East. Jack founded Studiosity in Sydney in 2003 with a vision to make the highest quality academic study support accessible to every student, regardless of their geographic or socio-economic circumstances.
What does academic freedom mean in 2025?
We will explore this vital question with the help of Jeremy C. Young, the Freedom to Learn program director at PEN America (and excellent 2023 Forum guest).