Tag: teaching with technology

Designing the 2026 Classroom: Emerging Learning Trends in an AI-Powered Education System – Faculty Focus

Artificial intelligence (AI) is reshaping education faster than many can realize or believe. According to a 2025 report by Microsoft, 86% of education organizations now use generative AI – the highest adoption rate of any industry and an uncharacteristically rapid pace for a sector which has historically lagged behind in technology adoption.

Across educational organizations, AI is moving from experimentation to impact. Each year, more institutions increasingly accelerate their use of AI. The global AI education market reached $7.57 billion USD in 2025, and is projected to exceed $112 billion USD by 2034.

Looking forward to the classrooms of 2026, AI will expand its stance as a powerful service for learners and teachers alike. From the earliest stages of education, AI-driven platforms are helping provide real-time personalized English instruction, helping level the playing field for young learners in developing countries.

AI’s impact extends across primary, secondary, and higher education, supporting personalized learning that adapts to individual needs, delivers instant feedback, enhances student engagement and outcomes, and reduces administrative demands on teachers.

An AIPRM report of U.S. students, for instance, found a 62% increase in test scores among those using AI-powered instruction systems, attributed to the technology’s ability to identify and address knowledge gaps before they develop into larger challenges.

“Instead of being confined to set moments, instruction can respond as learning happens, guiding students through their thinking and helping them build understanding in real time”, says Rajen Sheth, Founder and CEO of AI-powered edtech company Kyron Learning. The startup delivers personalized learning at scale, and raised $14.6 million USD in a Series A funding round.

At the same time as AI adoption in classrooms surges, companies across the globe confront an unprecedented skills crunch, with growing demand for talent capable of leveraging technology to drive innovation and growth. These challenges are inseparable: as AI reshapes business, it is also transforming how individuals are prepared for work.

In such a fast-moving landscape, higher education institutions are embedding AI, experiential learning, and challenge-based approaches into their curricula, alongside the rise of new institutions dedicated to digital transformation, technology, and data rather than traditional academic disciplines.

Democratization of Learning

Families who relocated recently to English-speaking countries often experience difficulties teaching the language to their children. English proficiency is widely viewed as a critical driver of future educational and economic opportunity for young children, although in regions like Latin America access to high-quality, in-person instruction remains limited to a small sector of families who can afford private lessons.

At this early stage of learning, AI-powered language tools are increasingly filling this gap. Experts expect it to play an even more significant role in the coming year, expanding access to effective, engaging English-language education at scale. Luis Von Ahn, founder of Duolingo, suggested on the No Priors podcast that AI will take on much of the instruction while teachers evolve into mentors or facilitators.

Buddy.ai was launched in 2017 to address this need. After he moved to the U.S. from Siberia, co-founder Ivan Crewkov had witnessed his preschool daughter’s difficulties with adapting to an English-language society.

The application has a multimodal AI tutor, Buddy, that interacts with students in real time and features a voice recognition technology specifically designed for children that complies with COPPA regulations.

Despite the precedents set for this technology in academia, Buddy.ai was the first company to successfully bring this concept to market:

“This is a game changer for teachers, since, in the past, they could only give students the practice of speaking in person, which was almost impossible in crowded classrooms”, explained Crewkov to Entrepreneur Magazine.

Research consistently shows that this approach is more effective than traditional learning methods.

AI-Powered Instruction and the Future of Schools

As per an October 2025 report by the Center for Democracy and Technology (CDT), 85% of teachers and 86% of students used AI in the preceding school year.

“I’m not surprised to hear that 85% of teachers have used it in some way…anything that helps a teacher save time is going to get taken up”, noted Joseph South, Chief Innovation Officer for ISTE+ASCD, a non-profit that provides resources for educators about educational technology, while in conversation with Education Week.

The CDT report also noted that 69% of teachers said AI tools have improved their teaching methods, while 55% agreed that it has given them more time to interact directly with students.

As 2026 unfolds, the use of AI-powered instruction is expected to continue growing amid rising teacher workload and burnout: it reduces administrative demands and offers a promising way to ease staffing shortages and allow educators to focus on teaching and student engagement.

Sheth said, however, that the challenge for educators is scale: “Even the best instructors can’t give every student timely, individualized support 24/7. Kyron helps close that gap while keeping educators firmly in control.”

AI-powered instruction platforms, such as Kyron Learning, help bridge differences in learning styles, cultural backgrounds, and prior educational experiences, with studies showing up to 70% higher course completion rates compared to traditional approaches.

“Rooted in learning science, [Kyron] adapts to each learner’s responses, providing timely feedback, surfacing misconceptions early, and supporting diverse learning needs across environments,” Sheth explained.

Opportunities and Challenges Students Face with AI

Most people can recall a moment in their education journeys when a lack of understanding and frustration made them want to give up. In crowded classrooms, that feeling can be particularly discouraging – but with Kyron’s instruction, students remain engaged, learning with greater confidence.

Kyron Learning is now available to all educational institutions and learning solution providers, partnering with a variety of organizations, including K-12 school districts and universities such as Western Governors University in Utah and the Illinois Institute of Technology.

Regardless, concerns have been raised about whether AI-powered learning could hinder students’ ability to form meaningful relationships with teachers and develop critical thinking and research skills.

“As many hype up the possibilities for AI to transform education, our research shows AI use in schools comes with real risks, like large-scale data breaches, tech-fueled sexual harassment and bullying, and treating students unfairly,” said Elizabeth Laird, director of the equity in civic technology project at CDT, to Education Week.

The CDT report also outlined that 70% of teachers worry that AI weakens critical thinking and research skills, and over half of the students agree that using AI in class makes them feel less connected to their teachers.

Sheth disagrees: “At the core, education remains human. The mentorship, connection, and inspiration that great educators provide cannot be replicated”, he continued, explaining that Kyron’s value is, “in reinforcing those strengths by supporting learning at scale,” he assured.

“The future is educators empowered by AI, not overshadowed by it”, he added.

In 2026, learning how to use these learning tools will be crucial. Experts, in fact, have said that the best way for schools to manage AI risks is through teacher training and student literacy lessons.

By that measure, schools and districts are falling behind: although most teachers and students use AI, the CDT report shows that under half have received no training or guidance from their institutions.

“What schools need to do is help teachers and students use [AI tools] in the right and best ways,” South agreed.

Equipping Learners for a Technology-Powered Economy

According to a report by McKinsey, 92% of business leaders expect to boost AI spending over the next three years. Organizations using AI-powered training report clear gains in employee performance, engagement, and retention.

Positioning AI a strategic device and underscoring the need for higher-education institutions to prepare students for this AI-centric workforce is therefore critical.

Leading universities and institutions are already actively redefining their classrooms to align with the demands of an AI-driven economy. The Tecnológico de Monterrey (Tec), a private university based in Monterrey, Mexico, actively redefines the curricula to align with the demands of an AI-driven economy.

Tec has woven AI, experiential learning, and challenge-based education throughout its undergraduate, graduate, and professional programs.

At Tec, they emphasize real-world problem solving, interdisciplinary collaboration and strong-industry connections to expand digital skills training, certification and workforce opportunities for students– working with partners such as Salesforce, Udemy, AWS, and Oracle, in addition to other global networks like the World Economic Forum Foresight Network and the AI Global Education Network.

Tec’s business programs register an employability level of over 93% just 3 months after graduating.

An alternate institution that has recognized the need to prepare students for the digital economy is ISDI (Instituto Superior para el Desarrolo de Internet), a digital business school in Madrid, Spain that offers master’s degrees and executive programs in digital business, technology, and innovation to equip professionals with these skills.

ISDI utilizes applied learning, continuous reskilling, and close collaboration with industry, equipping students with both technical capabilities and the strategic thinking required to navigate AI-enabled workplaces.

Roughly 90% of ISDI graduates work in fields related to their studies within 6 months of completing their programs.

These institutions have understood that, in 2026, higher education will continue to adapt learning models to prepare students not only to use AI tools, but to thrive alongside them in the modern workforce.

“The value of human skills cannot be replicated by computers. The future belongs to those who can balance technological and human skills to solve problems”, wrote Bernard Marr, world-renowned futurist, author, and strategic advisor who specializes in AI data and digital transformation, in Forbes.

Grace Goldstone is a reporter for Latin America Reports. Originally from the UK, she currently lives in Medellín, Colombia. Grace holds a master’s degree in Politics from the University of Edinburgh and previously interned at the UK House of Commons. Her work has also appeared in WIRED en Español and Entrepreneur Magazine.

References

AIPRM. “AI in Education Statistics.” Accessed December 29, 2025.
https://www.aiprm.com/en-gb/ai-in-education-statistics/

Business Insider. “Duolingo CEO Says AI Will Transform Schools and Childcare.” Accessed December 29, 2025.
https://www.businessinsider.com/duolingo-ceo-schools-ai-future-childcare-2025-5

Center for Democracy & Technology. Hand in Hand: 2025 Polling on Artificial Intelligence and Education. October 2025.
https://cdt.org/wp-content/uploads/2025/10/FINAL-CDT-2025-Hand-in-Hand-Polling-100225-accessible.pdf

Conecta Tec. “Crear progreso con egresados: Visión de la Escuela de Negocios del Tec.” Accessed December 17, 2025.
https://conecta.tec.mx/es/noticias/nacional/educacion/crear-progreso-con-egresados-vision-de-la-escuela-de-negocios-del-tec

Engageli. “AI in Education Statistics.” Accessed December 15, 2025.
https://www.engageli.com/blog/ai-in-education-statistics

Entrepreneur en Español. “Entrevista: Así es como Buddy.ai se ha posicionado en educación.” November 07, 2024.
https://spanish.entrepreneur.com/emprendedores/entrevista-asi-es-como-buddyai-se-ha-posicionado-en/482535

ISDI. “ISDI Institutions Overview.” Accessed December 17, 2025.
https://www.educations.com/institutions/isdi

McKinsey & Company. “Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential at Work.” January 28, 2025.
https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

Microsoft Education Blog. “AI in Education Report: Insights to Support Teaching and Learning.” August 20, 2025.
https://windpress.info/en/press-release/851649/ai-in-education-report-insights-to-support-teaching-and-learning-microsoft-education-blog

MDPI. “Artificial Intelligence in Education.” Education Sciences 15, no. 2 (2025): Article 113, January 20 2025.
https://www.mdpi.com/2227-7102/15/2/113

Precedence Research. “AI in Education Market Size, Share, and Trends.” Accessed November 18, 2025.
https://www.precedenceresearch.com/ai-in-education-market

Springer. “Artificial Intelligence in Education: Promises and Implications for Teaching and Learning.” International Journal of Artificial Intelligence in Education. Accessed December 17, 2025.
https://link.springer.com/article/10.1007/s40593-015-0065-9

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January 21, 2026
Designing AI-Resistant Assignments in Educational Leadership Courses – Faculty Focus
When I began teaching graduate-level courses in educational leadership early in the COVID-19 pandemic, my classroom existed entirely online. I quickly learned that keeping students engaged through a screen required more than well-organized slides or polished lectures. It required activities, learning experiences, and assignments that drew students into authentic, applied work that connected theory to the real leadership challenges they would face. Since then, I have taught dozens of educational leadership courses, both online and in person, and I have seen the same pattern: the design of an assignment shapes not only what students submit, but how deeply they engage with the learning process. Now, with generative AI able to produce essays, summaries, and even comprehensive education plans in seconds, the stakes for assignment design have changed. If an assignment can be completed entirely by an AI tool without any meaningful student input, we risk undermining the very skills that matter most in leadership preparation.

Why AI-Resistant Assignments Matter

Some educators have begun using the term “AI-resistant assignments” to describe work that cannot be fully completed by a chatbot. These are not AI-proof, and I am not trying to exclude technology altogether. In fact, when used appropriately, AI can be a valuable support for brainstorming, organizing ideas, or refining language. But an AI-resistant assignment is one where the core of the work, including the evidence gathered, the decisions made, and the reflections offered, must come from the student’s own engagement with people, data, and context.

Other educators have reached similar conclusions. In a recent New York Times column, Grose (2025) profiled professors who are reimagining their courses to ensure students remain active participants in their own learning. The examples she shared came from the humanities, but the underlying lesson applies across fields: in an AI-integrated world, we have to create assignments that cannot be outsourced entirely to a machine. The University of Massachusetts Amherst Center for Teaching and Learning (n.d.) offers strategies for making assignments more resistant to AI completion, such as requiring site-specific context, process documentation, and collaborative components.

Further underscoring the need for evidence, the AutomatED newsletter issued a public challenge to educators to submit assignments they believed were “AI-immune” and then tested them directly with AI tools (Clay, 2023). The results demonstrated that only by actively trialing assignments against AI, not just assuming immunity, can instructors be confident in their design choices.

These realities make it clear that designing assignments for leadership preparation cannot be left to chance. Educators need intentional strategies that not only account for AI’s capabilities but also reinforce the kinds of professional thinking and application AI cannot replicate. The following five principles offer a practical foundation for developing assignments that engage students deeply, foster leadership competencies, and ensure authentic performance.

Five Principles for AI-Resistant Assignments

From my own practice and from published guidance, I have developed five principles for AI-resistant assignments in leadership preparation:
1. Ground in authentic context – Require site-specific or scenario-specific evidence, incorporate the policy and community realities of schools, and engage peers or stakeholders in the process. This can include interactive formats such as presentations (recorded or live), simulations, debates, classroom observations with low-inference notes, analysis of instructional practice, interviews with stakeholders, or structured peer feedback sessions.
1. Connect theory to practice – Anchor assignments in leadership frameworks such as the Professional Standards for Educational Leaders, Lencioni’s Organizational Health, or Kotter’s change model, and always include both application and contextualized reflection.
1. Make the learning process visible – Assess drafts and planning notes alongside final products, and encourage multimodal evidence such as charts, agendas, or observation notes.
1. Clarify the role of AI – Set explicit expectations for when AI can be used and when it cannot replace original, site-based analysis.
1. Center human presence (Saucier’s PEACE Framework) – Build assignments that highlight preparation, expertise, authenticity, care, and engagement — qualities that cannot be automated and remain central to effective teaching (Saucier, 2025). I make sure that my instructions are clear, grounded in tasks they will use in the field, and meaningful for them to complete in their future leadership positions.
Examples from Leadership Preparation

These principles translate into assignments that blend conceptual rigor with applied relevance:
- Community Introduction Town Hall – District leader candidates prepare and deliver a live presentation introducing themselves to the school community during an initial town hall meeting. The presentation includes school demographics, achievement data, community assets, and identified needs, and is followed by a brief Q&A. This assignment is explicitly aligned to the Professional Standards for Educational Leaders (PSEL) and requires candidates to connect their leadership vision to stakeholder priorities.
- Organizational Health Rubric and Evaluation – Students use research to build on Patrick Lencioni’s organizational health model to create a custom rubric tailored to the context of a school district. They then apply the rubric to conduct an assessment, gathering both qualitative and quantitative evidence, rating each dimension, and concluding with a prioritized set of recommendations for improvement.
- Clinical Internship Logs with Contextual Analysis and Showcase – Students maintain weekly logs documenting activities, decisions, and reflections from their clinical internship, ensuring each entry explicitly connects to course content and relevant standards such as the Professional Standards for Educational Leaders (PSEL). At the end of the course, they present a synthesis of their experiences in a live or recorded showcase, highlighting key takeaways, challenges addressed, and leadership growth.
- Practice Instructional Coaching Conversations – Prepare for and conduct a simulated coaching conversation using an observation or case scenario, applying a framework like Jim Knight’s Impact Cycle, followed by reflection on the exchange.
Implementation Strategies

When implementing these assignments, I sequence them so that simpler applications build into more complex, multi-stakeholder tasks, and I scaffold the skills of data collection, context analysis, and reflective practice. I apply UMass CTL’s transparency recommendations, making the purpose of each assignment explicit, clarifying AI use policies, and linking tasks directly to learning outcomes. I also keep the PEACE framework in mind in both my teaching and my assessment design.

Conclusion: Keeping Humans at the Center

AI-resistant assignment design not only maintains academic integrity; it prepares leadership candidates for the realities of their profession. They practice making decisions with incomplete information, balancing competing interests, and communicating effectively with stakeholders, all in contexts where AI can be a partner but never the whole answer. By aligning these assignments with standards such as the Professional Standards for Educational Leaders and the National Educational Leadership Preparation standards, we ensure graduates leave with the judgment, adaptability, and human skills their roles demand. In an age when AI can produce passable work in seconds, including comprehensive education plans, the temptation for both students and instructors is to let the machine take the lead. But leadership is not about producing text; it is about guiding people and systems toward a shared vision. As Grose (2025) observed, the most enduring learning happens when students must engage with real people and real problems. If we want to prepare leaders who can thrive, we must ensure that every leadership course includes at least one AI-resistant assignment, not as a constraint, but as a commitment to developing the human capacities that no algorithm can replace.

Andy Szeto, EdD, is an adjunct professor in educational leadership with experience teaching more than fifty graduate-level courses. He specializes in instructional leadership, school management, and AI integration in school leadership preparation, drawing from his professional background as a district leader and his ongoing work mentoring emerging school leaders.

References

Clay, G. (2023, April 3). Believe your assignment is AI-immune? Let’s put it to the test. AutomatED: Teaching Better with Tech. https://automatedteach.com/p/believe-assignment-aiimmune-lets-put-test

Grose, J. (2025, August 6). These college professors will not bow down to A.I. The New York Times. https://www.nytimes.com/2025/08/06/opinion/humanities-college-ai.html

Saucier, L. (2025, May 12). What can college instructors offer their students in the age of AI? Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/what-can-college-instructors-offer-their-students-in-the-age-of-ai/

University of Massachusetts Amherst Center for Teaching and Learning. (n.d.). How do I redesign assignments and assessments for an AI-impacted world? Retrieved August 9, 2025, from https://www.umass.edu/ctl/how-do-i-redesign-assignments-and-assessments-ai-impacted-world
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January 12, 2026
Designing AI-Resistant Assignments in Educational Leadership Courses – Faculty Focus
When I began teaching graduate-level courses in educational leadership early in the COVID-19 pandemic, my classroom existed entirely online. I quickly learned that keeping students engaged through a screen required more than well-organized slides or polished lectures. It required activities, learning experiences, and assignments that drew students into authentic, applied work that connected theory to the real leadership challenges they would face. Since then, I have taught dozens of educational leadership courses, both online and in person, and I have seen the same pattern: the design of an assignment shapes not only what students submit, but how deeply they engage with the learning process. Now, with generative AI able to produce essays, summaries, and even comprehensive education plans in seconds, the stakes for assignment design have changed. If an assignment can be completed entirely by an AI tool without any meaningful student input, we risk undermining the very skills that matter most in leadership preparation.

Why AI-Resistant Assignments Matter

Some educators have begun using the term “AI-resistant assignments” to describe work that cannot be fully completed by a chatbot. These are not AI-proof, and I am not trying to exclude technology altogether. In fact, when used appropriately, AI can be a valuable support for brainstorming, organizing ideas, or refining language. But an AI-resistant assignment is one where the core of the work, including the evidence gathered, the decisions made, and the reflections offered, must come from the student’s own engagement with people, data, and context.

Other educators have reached similar conclusions. In a recent New York Times column, Grose (2025) profiled professors who are reimagining their courses to ensure students remain active participants in their own learning. The examples she shared came from the humanities, but the underlying lesson applies across fields: in an AI-integrated world, we have to create assignments that cannot be outsourced entirely to a machine. The University of Massachusetts Amherst Center for Teaching and Learning (n.d.) offers strategies for making assignments more resistant to AI completion, such as requiring site-specific context, process documentation, and collaborative components.

Further underscoring the need for evidence, the AutomatED newsletter issued a public challenge to educators to submit assignments they believed were “AI-immune” and then tested them directly with AI tools (Clay, 2023). The results demonstrated that only by actively trialing assignments against AI, not just assuming immunity, can instructors be confident in their design choices.

These realities make it clear that designing assignments for leadership preparation cannot be left to chance. Educators need intentional strategies that not only account for AI’s capabilities but also reinforce the kinds of professional thinking and application AI cannot replicate. The following five principles offer a practical foundation for developing assignments that engage students deeply, foster leadership competencies, and ensure authentic performance.

Five Principles for AI-Resistant Assignments

From my own practice and from published guidance, I have developed five principles for AI-resistant assignments in leadership preparation:
1. Ground in authentic context – Require site-specific or scenario-specific evidence, incorporate the policy and community realities of schools, and engage peers or stakeholders in the process. This can include interactive formats such as presentations (recorded or live), simulations, debates, classroom observations with low-inference notes, analysis of instructional practice, interviews with stakeholders, or structured peer feedback sessions.
1. Connect theory to practice – Anchor assignments in leadership frameworks such as the Professional Standards for Educational Leaders, Lencioni’s Organizational Health, or Kotter’s change model, and always include both application and contextualized reflection.
1. Make the learning process visible – Assess drafts and planning notes alongside final products, and encourage multimodal evidence such as charts, agendas, or observation notes.
1. Clarify the role of AI – Set explicit expectations for when AI can be used and when it cannot replace original, site-based analysis.
1. Center human presence (Saucier’s PEACE Framework) – Build assignments that highlight preparation, expertise, authenticity, care, and engagement — qualities that cannot be automated and remain central to effective teaching (Saucier, 2025). I make sure that my instructions are clear, grounded in tasks they will use in the field, and meaningful for them to complete in their future leadership positions.
Examples from Leadership Preparation

These principles translate into assignments that blend conceptual rigor with applied relevance:
- Community Introduction Town Hall – District leader candidates prepare and deliver a live presentation introducing themselves to the school community during an initial town hall meeting. The presentation includes school demographics, achievement data, community assets, and identified needs, and is followed by a brief Q&A. This assignment is explicitly aligned to the Professional Standards for Educational Leaders (PSEL) and requires candidates to connect their leadership vision to stakeholder priorities.
- Organizational Health Rubric and Evaluation – Students use research to build on Patrick Lencioni’s organizational health model to create a custom rubric tailored to the context of a school district. They then apply the rubric to conduct an assessment, gathering both qualitative and quantitative evidence, rating each dimension, and concluding with a prioritized set of recommendations for improvement.
- Clinical Internship Logs with Contextual Analysis and Showcase – Students maintain weekly logs documenting activities, decisions, and reflections from their clinical internship, ensuring each entry explicitly connects to course content and relevant standards such as the Professional Standards for Educational Leaders (PSEL). At the end of the course, they present a synthesis of their experiences in a live or recorded showcase, highlighting key takeaways, challenges addressed, and leadership growth.
- Practice Instructional Coaching Conversations – Prepare for and conduct a simulated coaching conversation using an observation or case scenario, applying a framework like Jim Knight’s Impact Cycle, followed by reflection on the exchange.
Implementation Strategies

When implementing these assignments, I sequence them so that simpler applications build into more complex, multi-stakeholder tasks, and I scaffold the skills of data collection, context analysis, and reflective practice. I apply UMass CTL’s transparency recommendations, making the purpose of each assignment explicit, clarifying AI use policies, and linking tasks directly to learning outcomes. I also keep the PEACE framework in mind in both my teaching and my assessment design.

Conclusion: Keeping Humans at the Center

AI-resistant assignment design not only maintains academic integrity; it prepares leadership candidates for the realities of their profession. They practice making decisions with incomplete information, balancing competing interests, and communicating effectively with stakeholders, all in contexts where AI can be a partner but never the whole answer. By aligning these assignments with standards such as the Professional Standards for Educational Leaders and the National Educational Leadership Preparation standards, we ensure graduates leave with the judgment, adaptability, and human skills their roles demand. In an age when AI can produce passable work in seconds, including comprehensive education plans, the temptation for both students and instructors is to let the machine take the lead. But leadership is not about producing text; it is about guiding people and systems toward a shared vision. As Grose (2025) observed, the most enduring learning happens when students must engage with real people and real problems. If we want to prepare leaders who can thrive, we must ensure that every leadership course includes at least one AI-resistant assignment, not as a constraint, but as a commitment to developing the human capacities that no algorithm can replace.

Andy Szeto, EdD, is an adjunct professor in educational leadership with experience teaching more than fifty graduate-level courses. He specializes in instructional leadership, school management, and AI integration in school leadership preparation, drawing from his professional background as a district leader and his ongoing work mentoring emerging school leaders.

References

Clay, G. (2023, April 3). Believe your assignment is AI-immune? Let’s put it to the test. AutomatED: Teaching Better with Tech. https://automatedteach.com/p/believe-assignment-aiimmune-lets-put-test

Grose, J. (2025, August 6). These college professors will not bow down to A.I. The New York Times. https://www.nytimes.com/2025/08/06/opinion/humanities-college-ai.html

Saucier, L. (2025, May 12). What can college instructors offer their students in the age of AI? Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/what-can-college-instructors-offer-their-students-in-the-age-of-ai/

University of Massachusetts Amherst Center for Teaching and Learning. (n.d.). How do I redesign assignments and assessments for an AI-impacted world? Retrieved August 9, 2025, from https://www.umass.edu/ctl/how-do-i-redesign-assignments-and-assessments-ai-impacted-world
Source link
January 12, 2026
Lessons Learned from Students Using AI Inappropriately in My Class – Faculty Focus

Generative AI is here and is creating challenges in higher education (Balch, 2023). As instructors, we are struggling with the need to teach our students necessary and marketable skills for their evolving world (e.g., Balch & Blanck, 2025; Butulis, 2023; Parks & Oslick, 2024; Schoeder, 2024), while maintaining academic rigor and integrity. Our students are aware of and are using generative AI in many ways (Kichizo Terry, 2023), and generative AI may change the way that we and our students perceive the role of instructors in higher education (Saucier, 2025). I share my recent experiences with and reflections on my students using generative AI in my classes below in the hopes that the lessons I have learned will help other instructors navigate similar experiences with students in their classes.

At the end of the semester, I received an email from a student in my general psychology course to ask me what he should do about a paper he wanted to turn in. The paper was an article summary and reflection paper that students can complete in my course in lieu of participating in research. The research participation requirement, and thus these papers, do not count for points – the research requirement just has to be completed (I will note here that this is a department policy for all general psychology courses – not a policy I personally created for my course). He ran his paper through an AI checker “just for fun” and it reported his paper was 43% written by AI. He wondered how to proceed because he said he did not use AI to write his papers and did not want to get into trouble. Knowing that AI checkers are not that reliable, I told him that if he had not used AI to write his paper, then he would be fine. He submitted the paper (and a couple of others) and I quickly forgot about his email in the chaos of the end of the semester.

In processing the papers submitted by the students in my class with an enrollment of 206 students, I later discovered an issue. The paper assignment asks students to retrieve, read, and write a summary and reflection on research articles published in the current calendar year from one of two specified journals. The first part of the assignment asks them to provide the reference for the article they used for their paper. Due to my questionable decision not to spend much time on APA format in my course, many of the students provided references in various states of incompletion and disrepair. Consequently, I copied and pasted the article information they did provide into Google Scholar to check to see if the articles used for each paper were in fact from one of the specified journals for the current calendar year. During this tedious task, I found that some articles were not from the specified journals, and some were not from the current calendar year. This was not surprising. What was surprising is that I found that some of the articles (including the three used by my student above) did not exist.

Over many email exchanges and Zoom meetings over the next several days with two students who used non-existent articles for their papers, I discovered that each had used generative AI to complete their papers. Three times, the student above put the directions from my posted assignment into generative AI, and each time generative AI obligingly produced something the student thought was an article from one of specified journal in the current calendar year. The articles were authored by “Smith”, “Johnson”, and, my personal favorite, “Doe.” The student then clarified that he requested a summary of each article, not the actual articles to help him write the papers in his “own words.” What was interesting to me was, that in reviewing the papers submitted, I do believe the student wrote the papers as I directed, but used the article summaries provided by AI rather than the full articles. I fully acknowledge that I cannot perfectly determine by reading them if papers were written by students or by AI, but the ideas, language, and grammar errors in the papers lead me to believe my student on that point. My student did acknowledge he knew this was wrong, but was overwhelmed by his workload at the end of the semester and made a bad decision for papers worth no points. The other student told me that he uploaded articles into generative AI and asked for summaries that fit the assignment directions, and said that the AI botched the references which was why the papers did not exist. Given that the articles the student later provided me that he said he uploaded to AI had different authors and topics from what he reported on his originally submitted papers, I suspect the student was not completely honest with me.

Both students seemed genuinely shocked that AI would retrieve articles for them that did not exist. Both students acknowledged that they knew what they were doing was not allowed and that they had violated our university’s honor code. Both students noted that end of the semester stress and the low stakes of the assignment were factors in their decisions to employ AI. Especially given that the papers they had used AI for were worth no points in my course, I treated these as teachable moments. I discussed with each student the issues with their using AI and they seemed receptive to this conversation. Each promised to not use AI for coursework ever again. I am not optimistic enough to think that this is a binding agreement, but I do think both students were impacted by this experience. I allowed each student the chance to redo their papers without the use of AI.

Lessons Learned

These experiences were unsettling for me and caused me to think deeply about AI and how I could adjust my courses to avoid future situations like these. These are a few of the lessons I will take with me from these situations:

First, I will have a useful AI policy in my courses that helps students better understand what is and is not allowable.

Every course should have a clearly stated AI policy – and my course did. My AI policy stated that students are prohibited from using AI for any coursework unless I explicitly tell them they may do so. I thought this provided enough guidance for my students (and flexibility for me if I ever wanted my students to use it for something), but the first of my students above saw no ethical difference in using AI to find research articles than in using a database like Google Scholar or PsycInfo. Rather than generally stating to “use library resources,” providing explicit directions to use those specific library resources would have been valuable. Also, discussing how AI may or may not be used for “idea generation” (and what that means) would be valuable, versus using it to complete coursework.

Second, I will discuss why I’m not allowing AI for at least some of the coursework in my class.

While AI and the skills to use it effectively and appropriately are important, they are not important for everything. I will discuss with my students the necessity of their independently learning content knowledge and skills without the use of AI. Relatedly, I will discuss with my students that AI often is not able to do the specified tasks reliably (e.g., finding relevant articles that actually exist). I will explain to my students that my policies and course decisions are designed to help them learn, not to provide barriers to their success. This will highlight that my purpose is to engage and support my students in their learning (Saucier, 2019; Saucier, 2022).

Third, I will have clear and actionable plans for actions and consequences if students violate my stated AI policy.

One of the issues I encountered was what to do with the students who violated my AI policy. When reporting cases to our university Honor and Integrity system, we are asked to recommend a sanction. I was not sure what an appropriate sanction would be in these situations in which students used AI inappropriately to complete papers worth no points. I could have recommended that the students fail the course due to an academic integrity violation, but this seemed too harsh to me (and I acknowledge some readers may disagree) without my having indicated in my syllabus that this penalty would be sought in cases like these. My future AI policies will be more explicit in how I pursue consequences when the policies are violated.

Fourth, I will try to support my students by reducing end of semester stress in my courses.

As part of empathetic course design (Saucier, Jones, Schiffer, & Renken, 2022), I recommend spacing out assessments. I recommend using more lower stakes and fewer higher stakes assessments. I recommend not having a high proportion of course points due in the last weeks of class. I recommend using the minimum effective dose of assessment, such as by setting maximums (e.g., no more than two pages) rather than minimums (e.g., at least three pages) on assignments – which is a decision that also streamlines and positively affects my experience in processing my students’ work.

Conclusion

Generative AI is here and our students are using it in our courses. We should anticipate having to deal with our students using AI in ways that may surprise us. Accordingly, our policies and teaching practices should be designed to help them (and us!) navigate the situations in which our students use AI in unauthorized and inappropriate ways. This will make their experiences better as learners and our experiences better as teachers.

N.B. I uploaded my conclusion to ChatGPT and asked if it agreed. It did, concluding that, “The goal should be to help students become savvy, ethical users of AI while maintaining the integrity of the learning process.” Well said.

Donald A. Saucier, Ph.D. (2001, University of Vermont) is a University Distinguished Teaching Scholar and Professor of Psychological Sciences at Kansas State University. Saucier has published more than 100 peer-reviewed journal articles and is a Fellow of the Society for Personality and Social Psychology, the Society for the Psychological Study of Social Issues, the Society for Experimental Social Psychology, and the Midwestern Psychological Association. His awards and honors include the University Distinguished Faculty Award for Mentoring of Undergraduate Students in Research, the Presidential Award for Excellence in Undergraduate Teaching, and the Society for the Psychological Study of Social Issues Teaching Resource Prize. Saucier is also the Faculty Associate Director of the Teaching and Learning Center at Kansas State University and offers a YouTube channel called “Engage the Sage” that describes his teaching philosophy, practices, and experiences.

References

Balch, D. E. (2023, August 25^th). Artificial intelligence: The rise of ChatGPT and its implications. Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/artificial-intelligence-the-rise-of-chatgpt-and-its-implications/

Balch, D. E., & Blanck, R. (2025, March 31^st). AI-powered teaching: Practical tools for community college faculty. Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/ai-powered-teaching-practical-tools-for-community-college-faculty/

Butulis, M. (2023, December 6^th). Embracing artificial intelligence in the classroom. Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/embracing-artificial-intelligence-in-the-classroom/

Kichizio Terry, O. (2023, May 12^th). I’m a student. You have no idea how much we’re using ChatGPT. Chronicles of Higher Education. https://www.chronicle.com/article/im-a-student-you-have-no-idea-how-much-were-using-chatgpt

Parks, M., & Oslick, M. E. (2024, May 22^nd). Empowering student learning: Navigating artificial intelligence in the college classroom. Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/empowering-student-learning-navigating-artificial-intelligence-in-the-college-classroom/

Saucier, D. A. (2019, September 9^th). Bringing PEACE to the classroom. Faculty Focus. https://www.facultyfocus.com/articles/philosophy-of-teaching/bringing-peace-to-the-classroom/

Saucier, D. A. (2022, February 23^rd). Bringing PEACE to support all students. Inside Higher Ed. https://www.insidehighered.com/views/2022/02/23/professors-should-learn-about-respond-students-unique-experiences-opinion

Saucier, D. A. (2025, May 12^th). What can college instructors offer their students in the age of AI?. Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/what-can-college-instructors-offer-their-students-in-the-age-of-ai/

Saucier, D. A., Jones, T. L., Schiffer, A. A., & Renken, N. D. (2022). The empathetic course design perspective. Applied Economics Teaching Resources, 4(4), 101-111.

Source link

December 10, 2025
Reimagining Education: A Future of Equity, Innovation, and Collaboration – Faculty Focus

Education is often referred to as the great equalizer, yet disparities in access, resources, and opportunities continue to hinder student success. As we look to the future, the challenge is not just to improve education but to reimagine it—ensuring that every learner, regardless of background, has access to meaningful, high-quality learning experiences. Research suggests that achieving educational equity leads to greater economic growth, stronger communities, and a more innovative workforce (Hanushek & Woessmann, 2016).

A Vision for the Future

Imagine a world where education is truly equitable, where every student has the resources, support, and opportunities they need to thrive. In this future, innovation drives personalized learning, collaboration strengthens communities, and a shared commitment to student success unites educators at all levels. Research underscores that equitable education improves social mobility and reduces poverty rates, benefiting both individuals and society (National Academies of Sciences, Engineering, & Medicine, 2019).

Yet, achieving this vision requires acknowledging that equity does not mean sameness. It means recognizing that students arrive with different strengths, challenges, and lived experiences. The future of education must be flexible enough to honor these differences while holding all learners to high expectations. This shift requires courage from educators and policymakers alike, as well as the willingness to rethink long-standing traditions that no longer serve today’s learners.

Innovation: Reshaping Learning for All

Technology has the power to transform education, but true innovation goes beyond digital tools—it redefines how we teach and learn. Future classrooms will be dynamic, adapting to each student’s needs with AI-driven tutoring, immersive virtual environments, and competency-based learning. A report from the Georgetown University Center on Education and the Workforce (Carnevale et al., 2019) highlights how technology-enhanced, personalized learning models improve student engagement and achievement. Additionally, competency-based education has been shown to increase student retention and academic success, particularly for nontraditional learners (Kelly & Columbus, 2020).

But innovation is not solely about technology. It is about creating learning environments where curiosity, creativity, and problem-solving flourish. For example, project-based learning and experiential education give students opportunities to apply classroom knowledge to real-world issues. When students design solutions for local environmental challenges or partner with community organizations, they see the relevance of education in action. These approaches not only increase motivation but also prepare learners for the collaborative, interdisciplinary challenges of the modern workforce.

Collaboration: Strengthening the Educational Ecosystem

No single educator, institution, or policy can create lasting change alone. The future of education depends on partnerships—between teachers, families, communities, and industries. Schools that collaborate with local businesses, healthcare providers, and social services create holistic support systems that enhance student success (Lochner, 2011). These partnerships help address non-academic barriers to learning, such as food insecurity, mental health, and career readiness. Studies show that schools with strong community engagement see improved student outcomes and higher graduation rates (Bryk et al., 2010). Community engagement provides students with access to mentorship, enrichment opportunities, and wraparound support services that help them stay on track. For instance, partnerships with local businesses can open doors to internships and job shadowing, while collaborations with healthcare providers ensure that students have access to essential physical and mental health resources. Research indicates that these integrated support systems not only boost academic performance but also foster a sense of belonging and purpose, both of which are critical to long-term success (Sanders, 2017).

Collaboration also requires rethinking the relationship between K–12 and higher education. Too often, these systems operate in silos, leaving students underprepared for college or careers. By creating seamless pathways that connect high school coursework with postsecondary programs and workforce needs, educators can ensure smoother transitions and stronger outcomes for students. Apprenticeships, dual enrollment programs, and career academies are powerful examples of how collaboration can bridge these gaps.

Commitment: Building a System That Works for Everyone

Equity in education requires intentional commitment. Addressing systemic barriers, investing in underserved communities, and ensuring that all students—regardless of socioeconomic status, race, or ability—have access to the same high-quality learning experiences is crucial. Research confirms that equitable school funding and early childhood education significantly improve long-term student success and economic outcomes (National Academies of Sciences, Engineering, & Medicine, 2019). Furthermore, well-funded schools with equitable resource distribution produce higher-achieving graduates who contribute more to society (Hanushek & Woessmann, 2016).

This commitment also extends to teacher support. Educators are at the heart of transformation, and their professional development, well-being, and sense of agency are essential for meaningful change. Schools that invest in teacher mentorship, continuous training, and leadership opportunities see stronger student outcomes and greater innovation in the classroom.

The Path Forward

The journey to a more equitable education system begins today. By embracing innovation, fostering collaboration, and committing to systemic change, we can create a future where every student can reach their full potential. The challenge is great, but the promise of a brighter tomorrow is within our reach—if we dare to reimagine what’s possible.

Michele Poulos has dedicated more than twenty-five years to education, with experience spanning elementary, secondary, and post-secondary settings. She holds a Bachelor’s degree in Elementary Education, a Master’s degree in Psychology, and is currently pursuing her doctoral degree in Human and Learning Science. Throughout her career, she has taught and learned in both traditional classrooms and fully online environments, giving her a unique perspective on how students thrive across modalities. Michele currently serves as the Director of Online Education at Pima Medical Institute, where she oversees online programs, faculty development, and strategic initiatives designed to enhance student learning and success. Her professional accomplishments have been recognized nationally, as she was inducted into Marquis Who’s Who in America for four consecutive years (2022–2025). Her passion for education is matched by her commitment to family life. She resides in Naples, Florida, with her husband and their two children—ten-year-old Trenton and eight-year-old Eliana—who continually inspire her dedication to both teaching and lifelong learning.

References

Bryk, A. S., Sebring, P. B., Allensworth, E., Luppescu, S., & Easton, J. Q. (2010). Organizing schools for improvement: Lessons from Chicago. University of Chicago Press.

Carnevale, A. P., Garcia, T. I., & Gulish, A. (2019). Career pathways: Five ways to connect college and careers.Georgetown University Center on Education and the Workforce. https://cew.georgetown.edu/

Hanushek, E. A., & Woessmann, L. (2016). Knowledge capital, growth, and the East Asian miracle. Science, 351(6271), 344-345. https://doi.org/10.1126/science.aad7796

Kelly, A. P., & Columbus, R. (2020). Innovation and the future of higher education. American Enterprise Institute.

Lochner, L. (2011). Non-production benefits of education: Crime, health, and good citizenship. NBER Working Paper No. 16722. https://doi.org/10.3386/w16722

National Academies of Sciences, Engineering, and Medicine. (2019). The promise of early childhood development in Latin America and the Caribbean. National Academies Press. https://doi.org/10.17226/25617

Sanders, M. G., & Galindo, C. (2017). Transforming educational experiences in low-income communities: A qualitative case study of social capital in a full-service community school. American Educational Research Journal, 54(1_suppl), 140S–163S. https://doi.org/10.3102/0002831216676571

Source link

December 8, 2025
Reimagining Education: A Future of Equity, Innovation, and Collaboration – Faculty Focus

Education is often referred to as the great equalizer, yet disparities in access, resources, and opportunities continue to hinder student success. As we look to the future, the challenge is not just to improve education but to reimagine it—ensuring that every learner, regardless of background, has access to meaningful, high-quality learning experiences. Research suggests that achieving educational equity leads to greater economic growth, stronger communities, and a more innovative workforce (Hanushek & Woessmann, 2016).

A Vision for the Future

Imagine a world where education is truly equitable, where every student has the resources, support, and opportunities they need to thrive. In this future, innovation drives personalized learning, collaboration strengthens communities, and a shared commitment to student success unites educators at all levels. Research underscores that equitable education improves social mobility and reduces poverty rates, benefiting both individuals and society (National Academies of Sciences, Engineering, & Medicine, 2019).

Yet, achieving this vision requires acknowledging that equity does not mean sameness. It means recognizing that students arrive with different strengths, challenges, and lived experiences. The future of education must be flexible enough to honor these differences while holding all learners to high expectations. This shift requires courage from educators and policymakers alike, as well as the willingness to rethink long-standing traditions that no longer serve today’s learners.

Innovation: Reshaping Learning for All

Technology has the power to transform education, but true innovation goes beyond digital tools—it redefines how we teach and learn. Future classrooms will be dynamic, adapting to each student’s needs with AI-driven tutoring, immersive virtual environments, and competency-based learning. A report from the Georgetown University Center on Education and the Workforce (Carnevale et al., 2019) highlights how technology-enhanced, personalized learning models improve student engagement and achievement. Additionally, competency-based education has been shown to increase student retention and academic success, particularly for nontraditional learners (Kelly & Columbus, 2020).

But innovation is not solely about technology. It is about creating learning environments where curiosity, creativity, and problem-solving flourish. For example, project-based learning and experiential education give students opportunities to apply classroom knowledge to real-world issues. When students design solutions for local environmental challenges or partner with community organizations, they see the relevance of education in action. These approaches not only increase motivation but also prepare learners for the collaborative, interdisciplinary challenges of the modern workforce.

Collaboration: Strengthening the Educational Ecosystem

No single educator, institution, or policy can create lasting change alone. The future of education depends on partnerships—between teachers, families, communities, and industries. Schools that collaborate with local businesses, healthcare providers, and social services create holistic support systems that enhance student success (Lochner, 2011). These partnerships help address non-academic barriers to learning, such as food insecurity, mental health, and career readiness. Studies show that schools with strong community engagement see improved student outcomes and higher graduation rates (Bryk et al., 2010). Community engagement provides students with access to mentorship, enrichment opportunities, and wraparound support services that help them stay on track. For instance, partnerships with local businesses can open doors to internships and job shadowing, while collaborations with healthcare providers ensure that students have access to essential physical and mental health resources. Research indicates that these integrated support systems not only boost academic performance but also foster a sense of belonging and purpose, both of which are critical to long-term success (Sanders, 2017).

Collaboration also requires rethinking the relationship between K–12 and higher education. Too often, these systems operate in silos, leaving students underprepared for college or careers. By creating seamless pathways that connect high school coursework with postsecondary programs and workforce needs, educators can ensure smoother transitions and stronger outcomes for students. Apprenticeships, dual enrollment programs, and career academies are powerful examples of how collaboration can bridge these gaps.

Commitment: Building a System That Works for Everyone

Equity in education requires intentional commitment. Addressing systemic barriers, investing in underserved communities, and ensuring that all students—regardless of socioeconomic status, race, or ability—have access to the same high-quality learning experiences is crucial. Research confirms that equitable school funding and early childhood education significantly improve long-term student success and economic outcomes (National Academies of Sciences, Engineering, & Medicine, 2019). Furthermore, well-funded schools with equitable resource distribution produce higher-achieving graduates who contribute more to society (Hanushek & Woessmann, 2016).

This commitment also extends to teacher support. Educators are at the heart of transformation, and their professional development, well-being, and sense of agency are essential for meaningful change. Schools that invest in teacher mentorship, continuous training, and leadership opportunities see stronger student outcomes and greater innovation in the classroom.

The Path Forward

The journey to a more equitable education system begins today. By embracing innovation, fostering collaboration, and committing to systemic change, we can create a future where every student can reach their full potential. The challenge is great, but the promise of a brighter tomorrow is within our reach—if we dare to reimagine what’s possible.

Michele Poulos has dedicated more than twenty-five years to education, with experience spanning elementary, secondary, and post-secondary settings. She holds a Bachelor’s degree in Elementary Education, a Master’s degree in Psychology, and is currently pursuing her doctoral degree in Human and Learning Science. Throughout her career, she has taught and learned in both traditional classrooms and fully online environments, giving her a unique perspective on how students thrive across modalities. Michele currently serves as the Director of Online Education at Pima Medical Institute, where she oversees online programs, faculty development, and strategic initiatives designed to enhance student learning and success. Her professional accomplishments have been recognized nationally, as she was inducted into Marquis Who’s Who in America for four consecutive years (2022–2025). Her passion for education is matched by her commitment to family life. She resides in Naples, Florida, with her husband and their two children—ten-year-old Trenton and eight-year-old Eliana—who continually inspire her dedication to both teaching and lifelong learning.

References

Bryk, A. S., Sebring, P. B., Allensworth, E., Luppescu, S., & Easton, J. Q. (2010). Organizing schools for improvement: Lessons from Chicago. University of Chicago Press.

Carnevale, A. P., Garcia, T. I., & Gulish, A. (2019). Career pathways: Five ways to connect college and careers.Georgetown University Center on Education and the Workforce. https://cew.georgetown.edu/

Hanushek, E. A., & Woessmann, L. (2016). Knowledge capital, growth, and the East Asian miracle. Science, 351(6271), 344-345. https://doi.org/10.1126/science.aad7796

Kelly, A. P., & Columbus, R. (2020). Innovation and the future of higher education. American Enterprise Institute.

Lochner, L. (2011). Non-production benefits of education: Crime, health, and good citizenship. NBER Working Paper No. 16722. https://doi.org/10.3386/w16722

National Academies of Sciences, Engineering, and Medicine. (2019). The promise of early childhood development in Latin America and the Caribbean. National Academies Press. https://doi.org/10.17226/25617

Sanders, M. G., & Galindo, C. (2017). Transforming educational experiences in low-income communities: A qualitative case study of social capital in a full-service community school. American Educational Research Journal, 54(1_suppl), 140S–163S. https://doi.org/10.3102/0002831216676571

Source link

December 8, 2025
AI In Academic Publishing: Disruption or Evolution? – Faculty Focus

Artificial Intelligence (AI) is driving one of the most significant transformations in academic publishing since the advent of peer review. There has been a steady increase in the use of AI in research manuscripts since 2022, when OpenAI launched ChatGPT. Recent data demonstrate that up to 22% of computer science papers indicate the usage of large language models (LLMs). Another study on LLM use in scientific publishing revealed that 16.9% of peer review texts contained AI content.

The integration of AI tools in every stage of publication has been met with appreciation and criticism in equal measure. Proponents advocate AI as a catalyst that boosts efficiency by automating mundane tasks such as grammar editing, reference formatting, and initial screening. However, critics warn against possible pitfalls in quality and ethical considerations. The question remains: is AI disrupting traditional publishing models, or is it fostering a natural evolution within the academic enterprise?

Rise of AI in Research and Publication

AI has become embedded throughout the research lifecycle, from idea generation to final manuscript submission. This pervasive adoption is reshaping researchers’ approach to their work; they use applications for grammar checks, plagiarism detection, format compliance, and even assessment of their research significance.

Recent studies show that LLMs have an unmatched ability to generate prose and summarize content, enabling researchers to write literature reviews and experimental descriptions more efficiently. In fact, many researchers utilize features of LLMs to assist them with brainstorming, rephrasing, and clarifying their arguments.

Besides writing assistance, AI-driven platforms can streamline other laborious tasks. For instance, searching massive databases to find relevant citations and assessing the context and reliability of references. Likewise, available text translation and summarization tools support 30+ languages— a feature that breaks down linguistic barriers and allows for international collaboration in research.

One perspective piece published in PLOS Biology notes: “A primary reason that science has not yet become fully multilingual is that translation can be slow and costly. Artificial intelligence (AI), however, may finally allow us to overcome this problem, as it can provide useful, often free or affordable, support in language editing and translation”.

The integration of AI tools in research and publication has fundamentally shifted research workflows. Automation technologies have quickened the pace of writing and made academic writing and publishing more accessible.

Ethical Implications and Risks

The increased efficiency and renewed research approach also come with significant ethical concerns. AI systems operate by learning patterns in existing data and can amplify hidden biases. If an AI tool is trained on past editorial outcomes, it may score submissions from well-known institutions or English-speaking authors more highly.

Stanford researchers found that even when non-native English researchers use LLMs to refine their submissions, peer reviewers still show bias against them. Another study revealed that AI text-detection tools often misidentify non-native English writing as machine-generated. In other words, if there are two authors with equal merits, they may face unequal scrutiny if one uses slightly different phrasing.

Beyond systemic biases, there is concern that AI integration into the peer review process can lead to over-reliance on its functionalities. An analysis of peer review highlighted that overdependence of editors and reviewers on AI-generated suggestions can result in deterioration in review quality, and not to mention, factual mistakes that slip through evaluation.

Even as AI is used for initial screening, the recommendations made by such tools about a manuscript’s quality or reviewer selection may be opaque or even include hallucinated reasoning. In the absence of transparency, it is difficult to identify and correct misjudgments. This accountability challenge can seriously undermine trust in the editorial process.

However, the most concerning risk is the potential of AI to create a feedback loop of maintaining the status quo. AIs are trained on existing published literature and are designed to evaluate new submissions based on the present data. Given this data, it is likely that the AI system may inadvertently suppress new and innovative ideas that do not coincide with established patterns.

The Irreplaceable Role of Human Editorial Judgment

Despite these technological advances, the fundamental responsibility for maintaining scientific integrity ultimately rests with human editors and reviewers. Academic publishers serve as gatekeepers of knowledge, shaping what research reaches the broader scientific community and, by extension, informing public understanding and policy decisions. This role carries immense responsibility. Editorial decisions can accelerate breakthrough discoveries or inadvertently stifle groundbreaking research that challenges conventional thinking.

Rather than delegating these critical judgements to algorithms, the academic publishing community must recognize this moment as a call to elevate editorial standards and practices. Editors must recommit to rigorous, nuanced evaluation that prioritizes scientific merit over efficiency metrics. The stakes are too high– the advancement of human knowledge and credibility of scientific enterprise itself– to entrust these decisions to systems that, however sophisticated, lack the contextual understanding, ethical reasoning, and innovative thinking that human expertise provides.

Dr. Su Yeong Kim is a Professor of Human Development and Family Sciences at the University of Texas at Austin. A leading figure in research on immigrant families and adolescent development, she earned the honor of being a Fellow on multiple national psychology associations and is the Editor of the Journal of Research on Adolescence. Dr. Kim has authored and published more than 160 works and is recognized with Division 45 of American Psychological Association’s Distinguished Career Contributions to Research Award. Her research, funded by the National Institutes of Health, National Science Foundation, and other bodies, covers bilingualism, language brokering, and cultural stressors in immigrant-origin youth. Dr. Kim is an enthusiastic mentor and community advocate, as well as a member of UT’s Provost’s Distinguished Leadership Service Academy.

References

Liang, Weixin, et al. “Quantifying Large Language Model Usage in Scientific Papers.” Nature Human Behaviour (2025). https://doi.org/10.1038/s41562-025-02273-8.

“How Much Research Is Being Written by Large Language Models?” Stanford Institute for Human-Centered Artificial Intelligence (HAI). https://hai.stanford.edu/news/how-much-research-being-written-large-language-models

Doskaliuk, B., et al. “Artificial Intelligence in Peer Review: Enhancing Efficiency …” J Korean Med Sci 40, no. 7 (2025). https://pubmed.ncbi.nlm.nih.gov/39995259/

Kim, H., J. C. Little, J. Li, B. Patel, and D. Kalderon. “Hedgehog-Stimulated Phosphorylation at Multiple Sites Activates Ci by Altering Ci–Ci Interfaces without Full Suppressor of Fused Dissociation.” PLOS Biology 23, no. 4 (2025): e3003105. https://doi.org/10.1371/journal.pbio.3003105

“How Language Bias Persists in Scientific Publishing Despite AI Tools.” Stanford Institute for Human-Centered Artificial Intelligence (HAI). https://hai.stanford.edu/news/how-language-bias-p e rsists-in-scientific-publishing-despite-ai-tools

“What Are AI Hallucinations?” Google Cloud. https://cloud.google.com/discover/what-are-ai-hallucinations

Source link

November 26, 2025
AI In Academic Publishing: Disruption or Evolution? – Faculty Focus

Artificial Intelligence (AI) is driving one of the most significant transformations in academic publishing since the advent of peer review. There has been a steady increase in the use of AI in research manuscripts since 2022, when OpenAI launched ChatGPT. Recent data demonstrate that up to 22% of computer science papers indicate the usage of large language models (LLMs). Another study on LLM use in scientific publishing revealed that 16.9% of peer review texts contained AI content.

The integration of AI tools in every stage of publication has been met with appreciation and criticism in equal measure. Proponents advocate AI as a catalyst that boosts efficiency by automating mundane tasks such as grammar editing, reference formatting, and initial screening. However, critics warn against possible pitfalls in quality and ethical considerations. The question remains: is AI disrupting traditional publishing models, or is it fostering a natural evolution within the academic enterprise?

Rise of AI in Research and Publication

AI has become embedded throughout the research lifecycle, from idea generation to final manuscript submission. This pervasive adoption is reshaping researchers’ approach to their work; they use applications for grammar checks, plagiarism detection, format compliance, and even assessment of their research significance.

Recent studies show that LLMs have an unmatched ability to generate prose and summarize content, enabling researchers to write literature reviews and experimental descriptions more efficiently. In fact, many researchers utilize features of LLMs to assist them with brainstorming, rephrasing, and clarifying their arguments.

Besides writing assistance, AI-driven platforms can streamline other laborious tasks. For instance, searching massive databases to find relevant citations and assessing the context and reliability of references. Likewise, available text translation and summarization tools support 30+ languages— a feature that breaks down linguistic barriers and allows for international collaboration in research.

One perspective piece published in PLOS Biology notes: “A primary reason that science has not yet become fully multilingual is that translation can be slow and costly. Artificial intelligence (AI), however, may finally allow us to overcome this problem, as it can provide useful, often free or affordable, support in language editing and translation”.

The integration of AI tools in research and publication has fundamentally shifted research workflows. Automation technologies have quickened the pace of writing and made academic writing and publishing more accessible.

Ethical Implications and Risks

The increased efficiency and renewed research approach also come with significant ethical concerns. AI systems operate by learning patterns in existing data and can amplify hidden biases. If an AI tool is trained on past editorial outcomes, it may score submissions from well-known institutions or English-speaking authors more highly.

Stanford researchers found that even when non-native English researchers use LLMs to refine their submissions, peer reviewers still show bias against them. Another study revealed that AI text-detection tools often misidentify non-native English writing as machine-generated. In other words, if there are two authors with equal merits, they may face unequal scrutiny if one uses slightly different phrasing.

Beyond systemic biases, there is concern that AI integration into the peer review process can lead to over-reliance on its functionalities. An analysis of peer review highlighted that overdependence of editors and reviewers on AI-generated suggestions can result in deterioration in review quality, and not to mention, factual mistakes that slip through evaluation.

Even as AI is used for initial screening, the recommendations made by such tools about a manuscript’s quality or reviewer selection may be opaque or even include hallucinated reasoning. In the absence of transparency, it is difficult to identify and correct misjudgments. This accountability challenge can seriously undermine trust in the editorial process.

However, the most concerning risk is the potential of AI to create a feedback loop of maintaining the status quo. AIs are trained on existing published literature and are designed to evaluate new submissions based on the present data. Given this data, it is likely that the AI system may inadvertently suppress new and innovative ideas that do not coincide with established patterns.

The Irreplaceable Role of Human Editorial Judgment

Despite these technological advances, the fundamental responsibility for maintaining scientific integrity ultimately rests with human editors and reviewers. Academic publishers serve as gatekeepers of knowledge, shaping what research reaches the broader scientific community and, by extension, informing public understanding and policy decisions. This role carries immense responsibility. Editorial decisions can accelerate breakthrough discoveries or inadvertently stifle groundbreaking research that challenges conventional thinking.

Rather than delegating these critical judgements to algorithms, the academic publishing community must recognize this moment as a call to elevate editorial standards and practices. Editors must recommit to rigorous, nuanced evaluation that prioritizes scientific merit over efficiency metrics. The stakes are too high– the advancement of human knowledge and credibility of scientific enterprise itself– to entrust these decisions to systems that, however sophisticated, lack the contextual understanding, ethical reasoning, and innovative thinking that human expertise provides.

Dr. Su Yeong Kim is a Professor of Human Development and Family Sciences at the University of Texas at Austin. A leading figure in research on immigrant families and adolescent development, she earned the honor of being a Fellow on multiple national psychology associations and is the Editor of the Journal of Research on Adolescence. Dr. Kim has authored and published more than 160 works and is recognized with Division 45 of American Psychological Association’s Distinguished Career Contributions to Research Award. Her research, funded by the National Institutes of Health, National Science Foundation, and other bodies, covers bilingualism, language brokering, and cultural stressors in immigrant-origin youth. Dr. Kim is an enthusiastic mentor and community advocate, as well as a member of UT’s Provost’s Distinguished Leadership Service Academy.

References

Liang, Weixin, et al. “Quantifying Large Language Model Usage in Scientific Papers.” Nature Human Behaviour (2025). https://doi.org/10.1038/s41562-025-02273-8.

“How Much Research Is Being Written by Large Language Models?” Stanford Institute for Human-Centered Artificial Intelligence (HAI). https://hai.stanford.edu/news/how-much-research-being-written-large-language-models

Doskaliuk, B., et al. “Artificial Intelligence in Peer Review: Enhancing Efficiency …” J Korean Med Sci 40, no. 7 (2025). https://pubmed.ncbi.nlm.nih.gov/39995259/

Kim, H., J. C. Little, J. Li, B. Patel, and D. Kalderon. “Hedgehog-Stimulated Phosphorylation at Multiple Sites Activates Ci by Altering Ci–Ci Interfaces without Full Suppressor of Fused Dissociation.” PLOS Biology 23, no. 4 (2025): e3003105. https://doi.org/10.1371/journal.pbio.3003105

“How Language Bias Persists in Scientific Publishing Despite AI Tools.” Stanford Institute for Human-Centered Artificial Intelligence (HAI). https://hai.stanford.edu/news/how-language-bias-p e rsists-in-scientific-publishing-despite-ai-tools

“What Are AI Hallucinations?” Google Cloud. https://cloud.google.com/discover/what-are-ai-hallucinations

Source link

November 26, 2025
The AI Teammate: Three Roles to Build Student AI Fluency – Faculty Focus
AI is now embedded in teaching and learning. As educators, how do we help students benefit from AI without slipping into dependency, surface-level work, or ethical misconduct? I’ve found a helpful way to clarify conversations with students (and with myself) by thinking of AI as a teammate (or teammates) with clearly defined roles: the Tasker, the Draftsmith, and the Facilitator.

Imagine a meeting of a team of people: someone handles logistics, someone sketches ideas, someone pushes discussion. Framing AI this way has made it easier to talk about students remaining responsible for how and to what purposes they use AI. My approach draws on recent scholarship about AI reshaping how teams work (Dell’Acqua et al., 2025).

The AI Teammates

The Tasker

The Tasker deals with repetitive or procedural work such as managing our calendars, formatting citations, or cleaning datasets. These tasks often are tedious but essential and using AI frees time that can be focused on deeper thinking. To use a Tasker, we can set up a Robotic Process Automation (RPA) with AI, use embedded AI tools such as Copilot in MS:Word or Claude in MS:Excel or Google sheets, or create what is currently referred to as an LLM “Agent.” As Farri & Rosani (2025) describe in their HBR guide to generative AI for managers, we can use AI to lighten the load on procedural chores, affording us the time and energy to engage more with deeper thinking and learning.

Because AI output may contain mistakes, students must learn to identify the levels of risks they believe are acceptable for the given task before turning to AI and must validate its output. The level of validation required may depend on the risk level, needed accuracy and quality, and current knowledge of the tool’s metrics.

We approach the Tasker differently than we do the Draftsmith and Facilitator: we can set our RPA or Agent up to run and only involve ourselves in its applications until we’re sure it can work independently and accurately, or we can trigger the Tasker each time we need it. Students must learn to set aside sufficient validation time before designing a Tasker.

The Draftsmith

The Draftsmith can spot passive voice in students’ writing and explain why it’s problematic, convert an outline into a draft of a PowerPoint, and produce study tools such as multiple choice or essay questions, podcasts, and videos from their course notes. My engineering students have prompted their Draftsmiths using a prompt library I created specifically for them to help them convert their design clients’ stated needs into project requirements. Bussgang (2025) has pointed out how small steps like these can dramatically boost productivity without sacrificing learning.

While AI may generate text or ideas, the final product must be the student’s own (Duffy, 2025). I teach my students that, much to their dismay, they must develop their own writers’ voice before using AI and that this process can take years and many writing efforts! Our writer’s voice presents us to our bosses, colleagues, and clients given that much of our communication at work is through emails. College is the best time to invest in this effort because it’s rare to get opportunities later to do so.

From students’ comments, I believe they use AI in the Draftsmith role more frequently than the other two roles. Few realize the Draftsmith increases cognitive load when they use it correctly and decreases their learning when they use it incorrectly!

It increases the load because they must provide the AI with significant context for it to provide anything other than banal, common prose that flattens their own voice (Purohit, 2025) or “creative” prose that is meaningless. AI use decreases their learning when they use it to write first and second drafts because writing sharpens our thinking and embeds learned concepts into our long-term memory.

The Facilitator

Instead of using AI to generate content (the Draftsmith), the Facilitator is a use of AI as a thinking partner (Solis, 2024 and HBSP’s ManageMentor course 2025). A student might ask AI to play devil’s advocate, suggest counterarguments, or pose probing questions when their paper’s argument needs strengthening. AI might help simulate a stakeholder’s perspective with whom the student role plays to prepare for a meeting or consider how their ideas might land with their student group peers. My students have used AI in the Facilitator role to pressure test their design ideas, review their research plans, and prepare to give presentations. When students use the Facilitator, they’re reflecting (Harbridge, 2025) and thereby deepening their learning.

To get the best value from AI as a Facilitator, the student must approach it with the expectation that it will take considerable time. The Facilitator provides meaningful interaction only when the student gives it their full attention, asks probing questions, provides significant context, and is patient. They may need to iterate, restating their prompts or providing more information, and to ask the LLM to be candid and critical. They may need to step away from the LLM, perhaps try a different one or come back on another day, when it goes off in the wrong direction.

We have become too used to scanning an LLM’s output. Just as I would not expect a student who came to me for career advice to start scrolling on their phone while I’m talking, I must teach them to give their full attention to the Facilitator.

What I’m suggesting here is not a list of use cases. Instead, I’m providing students with an approach to using AI where they plan before typing into the chat box, see its best use as a Facilitator that partners with them in learning and development, and recognize that validation and reading output will be time and cognitively intensive.

What This Means for Teaching

I’ve found that when I teach these roles to students before assigning homework, they make better choices regarding when and how to use AI. Here are some tactics I’ve tried that have netted positive results:
- Design assignments that invite different roles. For example, ask students to begin with AI as a Tasker (perhaps organizing sources or cleaning a dataset), then shift to using the Draftsmith to help them find passive voice or other syntax issues in a paper, and finally draw on the Facilitator to find the holes in their arguments before submitting a final paper. Intentionally stating the role each assignment calls for and requiring them to state when they’re using the Tasker, Draftsmith, or Facilitator solidifies their understanding that their approach to AI differs by role.
- Be clear about where AI helps and where it doesn’t. AI can help with structure, suggestions, and ideation, but it cannot replace revision, critical thinking, or the development of one’s own voice. This must be intentionally taught by including questions requiring students to reflect on what worked and where the AI led them astray in any assignments allowing AI use.
- Encourage disclosure. I talk extensively about “Total AI Transparency”, ensuring students that I will note any use of AI in my communications with them and I expect the same from them. Trust has broken down as both students and instructors believe the other is using AI when they’re not and failing to recognize when they are. Owning up to my own use and being clear on why I used AI for that task encourages students to do the same.
Closing Thoughts

We need to steer students away from extremes of either refusing to use AI at all or letting it rob them of learning. By distinguishing the roles of Tasker, Draftsmith, and Facilitator, we clarify our expectations of students. More importantly, we help them become more deliberate in their approach, preparing them to be AI-fluent.

Illysa Izenberg is an Associate Teaching Professor for the Center for Leadership Education in the Whiting School of Engineering at The Johns Hopkins University. She has been teaching management and business ethics to graduate and undergraduate students via Face-to-Face, online, and blended courses since 2006. Izenberg earned her MBA from the Harvard Graduate School of Business and is the winner of both the Alumni and Pond Excellence in Teaching Awards (2016 and 2020 respectively).

References

Bussgang, J. (2025, May 20). 3 small steps to 10x your productivity with AI this week. The Experimentation Machine. https://experimentationmachine.com/p/10x-this-week

Duffy, A. (2025, April 11). Your CEO just said ‘Use AI or else.’ Here’s what to do next. Every. https://every.to/p/your-ceo-just-said-use-ai-or-else-here-s-what-to-do-next

Farri, E., & Rosani, G. (2025). HBR guide to generative AI for managers. Harvard Business Review Press.

Gross, A. (2025). AI Crash Course [Online course]. SectionAI. https://www.sectionschool.com/courses/ai-crash-course-workshop

Harbridge, R. (2025). AI for Team Leaders [Online course]. SectionAI.

Harvard Business Publishing. (2025). Help your team harness generative AI (Lesson 1). In Leading with generative AI [Online course]. Harvard ManageMentor.

Purohit, R. (Host). (2025, June 18). The man inside the minds of the people building AGI [Audio podcast episode]. In AI & I. Every.

Solis, B. (2024, November). Train your brain to work creatively with Gen AI. Harvard Business School Publishing.
Source link
November 17, 2025
The AI Teammate: Three Roles to Build Student AI Fluency – Faculty Focus
AI is now embedded in teaching and learning. As educators, how do we help students benefit from AI without slipping into dependency, surface-level work, or ethical misconduct? I’ve found a helpful way to clarify conversations with students (and with myself) by thinking of AI as a teammate (or teammates) with clearly defined roles: the Tasker, the Draftsmith, and the Facilitator.

Imagine a meeting of a team of people: someone handles logistics, someone sketches ideas, someone pushes discussion. Framing AI this way has made it easier to talk about students remaining responsible for how and to what purposes they use AI. My approach draws on recent scholarship about AI reshaping how teams work (Dell’Acqua et al., 2025).

The AI Teammates

The Tasker

The Tasker deals with repetitive or procedural work such as managing our calendars, formatting citations, or cleaning datasets. These tasks often are tedious but essential and using AI frees time that can be focused on deeper thinking. To use a Tasker, we can set up a Robotic Process Automation (RPA) with AI, use embedded AI tools such as Copilot in MS:Word or Claude in MS:Excel or Google sheets, or create what is currently referred to as an LLM “Agent.” As Farri & Rosani (2025) describe in their HBR guide to generative AI for managers, we can use AI to lighten the load on procedural chores, affording us the time and energy to engage more with deeper thinking and learning.

Because AI output may contain mistakes, students must learn to identify the levels of risks they believe are acceptable for the given task before turning to AI and must validate its output. The level of validation required may depend on the risk level, needed accuracy and quality, and current knowledge of the tool’s metrics.

We approach the Tasker differently than we do the Draftsmith and Facilitator: we can set our RPA or Agent up to run and only involve ourselves in its applications until we’re sure it can work independently and accurately, or we can trigger the Tasker each time we need it. Students must learn to set aside sufficient validation time before designing a Tasker.

The Draftsmith

The Draftsmith can spot passive voice in students’ writing and explain why it’s problematic, convert an outline into a draft of a PowerPoint, and produce study tools such as multiple choice or essay questions, podcasts, and videos from their course notes. My engineering students have prompted their Draftsmiths using a prompt library I created specifically for them to help them convert their design clients’ stated needs into project requirements. Bussgang (2025) has pointed out how small steps like these can dramatically boost productivity without sacrificing learning.

While AI may generate text or ideas, the final product must be the student’s own (Duffy, 2025). I teach my students that, much to their dismay, they must develop their own writers’ voice before using AI and that this process can take years and many writing efforts! Our writer’s voice presents us to our bosses, colleagues, and clients given that much of our communication at work is through emails. College is the best time to invest in this effort because it’s rare to get opportunities later to do so.

From students’ comments, I believe they use AI in the Draftsmith role more frequently than the other two roles. Few realize the Draftsmith increases cognitive load when they use it correctly and decreases their learning when they use it incorrectly!

It increases the load because they must provide the AI with significant context for it to provide anything other than banal, common prose that flattens their own voice (Purohit, 2025) or “creative” prose that is meaningless. AI use decreases their learning when they use it to write first and second drafts because writing sharpens our thinking and embeds learned concepts into our long-term memory.

The Facilitator

Instead of using AI to generate content (the Draftsmith), the Facilitator is a use of AI as a thinking partner (Solis, 2024 and HBSP’s ManageMentor course 2025). A student might ask AI to play devil’s advocate, suggest counterarguments, or pose probing questions when their paper’s argument needs strengthening. AI might help simulate a stakeholder’s perspective with whom the student role plays to prepare for a meeting or consider how their ideas might land with their student group peers. My students have used AI in the Facilitator role to pressure test their design ideas, review their research plans, and prepare to give presentations. When students use the Facilitator, they’re reflecting (Harbridge, 2025) and thereby deepening their learning.

To get the best value from AI as a Facilitator, the student must approach it with the expectation that it will take considerable time. The Facilitator provides meaningful interaction only when the student gives it their full attention, asks probing questions, provides significant context, and is patient. They may need to iterate, restating their prompts or providing more information, and to ask the LLM to be candid and critical. They may need to step away from the LLM, perhaps try a different one or come back on another day, when it goes off in the wrong direction.

We have become too used to scanning an LLM’s output. Just as I would not expect a student who came to me for career advice to start scrolling on their phone while I’m talking, I must teach them to give their full attention to the Facilitator.

What I’m suggesting here is not a list of use cases. Instead, I’m providing students with an approach to using AI where they plan before typing into the chat box, see its best use as a Facilitator that partners with them in learning and development, and recognize that validation and reading output will be time and cognitively intensive.

What This Means for Teaching

I’ve found that when I teach these roles to students before assigning homework, they make better choices regarding when and how to use AI. Here are some tactics I’ve tried that have netted positive results:
- Design assignments that invite different roles. For example, ask students to begin with AI as a Tasker (perhaps organizing sources or cleaning a dataset), then shift to using the Draftsmith to help them find passive voice or other syntax issues in a paper, and finally draw on the Facilitator to find the holes in their arguments before submitting a final paper. Intentionally stating the role each assignment calls for and requiring them to state when they’re using the Tasker, Draftsmith, or Facilitator solidifies their understanding that their approach to AI differs by role.
- Be clear about where AI helps and where it doesn’t. AI can help with structure, suggestions, and ideation, but it cannot replace revision, critical thinking, or the development of one’s own voice. This must be intentionally taught by including questions requiring students to reflect on what worked and where the AI led them astray in any assignments allowing AI use.
- Encourage disclosure. I talk extensively about “Total AI Transparency”, ensuring students that I will note any use of AI in my communications with them and I expect the same from them. Trust has broken down as both students and instructors believe the other is using AI when they’re not and failing to recognize when they are. Owning up to my own use and being clear on why I used AI for that task encourages students to do the same.
Closing Thoughts

We need to steer students away from extremes of either refusing to use AI at all or letting it rob them of learning. By distinguishing the roles of Tasker, Draftsmith, and Facilitator, we clarify our expectations of students. More importantly, we help them become more deliberate in their approach, preparing them to be AI-fluent.

Illysa Izenberg is an Associate Teaching Professor for the Center for Leadership Education in the Whiting School of Engineering at The Johns Hopkins University. She has been teaching management and business ethics to graduate and undergraduate students via Face-to-Face, online, and blended courses since 2006. Izenberg earned her MBA from the Harvard Graduate School of Business and is the winner of both the Alumni and Pond Excellence in Teaching Awards (2016 and 2020 respectively).

References

Bussgang, J. (2025, May 20). 3 small steps to 10x your productivity with AI this week. The Experimentation Machine. https://experimentationmachine.com/p/10x-this-week

Duffy, A. (2025, April 11). Your CEO just said ‘Use AI or else.’ Here’s what to do next. Every. https://every.to/p/your-ceo-just-said-use-ai-or-else-here-s-what-to-do-next

Farri, E., & Rosani, G. (2025). HBR guide to generative AI for managers. Harvard Business Review Press.

Gross, A. (2025). AI Crash Course [Online course]. SectionAI. https://www.sectionschool.com/courses/ai-crash-course-workshop

Harbridge, R. (2025). AI for Team Leaders [Online course]. SectionAI.

Harvard Business Publishing. (2025). Help your team harness generative AI (Lesson 1). In Leading with generative AI [Online course]. Harvard ManageMentor.

Purohit, R. (Host). (2025, June 18). The man inside the minds of the people building AGI [Audio podcast episode]. In AI & I. Every.

Solis, B. (2024, November). Train your brain to work creatively with Gen AI. Harvard Business School Publishing.
Source link
November 17, 2025