Category: responsible AI use

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
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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.

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December 10, 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
Teaching with AI: From Prohibition to Partnership for Critical Thinking – Faculty Focus
What if the AI tools we are trying to limit and caution against were actually essential (or beneficial) to enhancing the critical thinking skills we are afraid of losing? Since the 2023 proliferation of generative AI, faculty have been inundated with warnings that human intelligence is being eroded by AI. As a result, some faculty have adopted policies banning or severely limiting AI use driven by concerns of academic integrity and the fear of students bypassing essential learning.

However, banning AI will not prevent students from using it, whether for nefarious or appropriate purposes. Instead, it may deny students a chance to practice and engage with AI in an educational setting where they and faculty can explore its full potential collaboratively. This kind of restrictive thinking is based on two flawed assumptions: that AI cannot support student thinking and that students will only use AI to cheat. The challenge is not to police every use, but to reframe our approach from one of prohibition to one of collaborative partnership.

This shift in perspective allows faculty to systematically integrate AI into courses in a developmentally appropriate manner. By centering policies on learning, we can encourage students to take an active, self-regulated approach to their education. This reframes the focus from dishonesty to autonomous learning, emphasizing academic values while scaffolding meaningful assignments that challenge student thinking.

Integrating AI into the curriculum requires a developmental approach, much like teaching toddlers. Expecting a first-year student to rely on AI for essential skills development is like asking a toddler to color within the lines—it’s developmentally inappropriate. Instead, our policies should align with a student’s progression. In lower-level courses, the focus must be on foundational skill-building including learning how to use AI. For upper-level and graduate students, we can empower them to autonomously evaluate AI’s role in their learning and whether or not AI is developing or replacing learning. Meanwhile, mid-level courses can provide a scaffolded transition, with specific instructions on how and when to use AI.

It is important to consider students’ prior knowledge of AI as well. While many are comfortable using technology and AI, they may lack metacognitive awareness of how their use affects learning. Understanding students’ technology usage is crucial when designing courses. Ultimately, use your best judgment—some graduate courses may require a cautious approach, while entry-level courses might benefit from a more permissive policy, especially since students within the same course can have a wide range of AI abilities.

How to Integrate AI Developmentally into Your Courses
- Lower-Level Courses: Focus on building foundational skills, which includes guided instruction on how to use AI responsibly. This moves the strategy beyond mere prohibition.
- Mid-Level Courses: Use AI as a scaffold where faculty provide specific guidelines on when and how to use the tool, preparing students for greater independence.
- Upper-Level/Graduate Courses: Empower students to evaluate AI’s role in their learning. This enables them to become self-regulated learners who make informed decisions about their tools.
- Balanced Approach: Make decisions about AI use based on the content being learned and students’ developmental needs.
Now that you have a framework for how to conceptualize including AI into your courses here are a few ideas on scaffolding AI to allow students to practice using technology and develop cognitive skills.

To introduce AI into your course, create a prompt that asks students to have a conversation with an AI about a concept you will be discussing. This anticipatory set can prime student thinking and encourage them to use AI in a conversational manner, moving beyond simply asking for answers. You can then discuss the AI’s responses—exploring bias, hallucinations, and the depth of its answers—which naturally leads to a conversation about crafting better prompts. This simple, ungraded exercise allows all students to participate, provides valuable practice, and serves a clear learning purpose.

Another example of a nongraded, purposeful use of AI is for providing feedback on learning. When students are writing papers, you can create custom AI agents to provide feedback on different parts of the writing process, from idea development to final submission. These agents can be designed to follow assignment criteria without writing any portion of the work. If students choose to use the agent, you can ask them to share their feedback conversations to assess the quality of the feedback and to write about how they incorporated it to develop their ideas—adding a crucial element of metacognition.

This method is also ideal for reinforcing and mastering skill development. For example, in a counseling course, students can practice articulating confidentiality and its limits to a fictional client created by AI. Once this foundational skill is mastered, the agent can be instructed to demonstrate signs of self-harm, allowing the student to practice assessing client safety and deciding whether to break confidentiality. As with the other examples, you can ask students to share their conversations for your feedback, have them critique their own performance, and provide a rationale for their approach. The agent itself can even provide feedback at the end of the session.

Finally, a higher level of AI integration is to have students create their own custom learning AI agent. By this point, students will have had multiple chances to improve their prompt writing, practice using AI for learning (not bypassing skills), and evaluate how AI supports their development. Creating a personalized study agent would be an ideal way for students to be active in their learning and assess the areas they need to develop. Faculty could provide guidelines and ask students to share how they are using AI positively.

How to Scaffold AI into Your Courses
- Start with low-stakes, ungraded activities.
- Then use AI to provide meaningful, real-time feedback.
- Create opportunities for skill reinforcement and mastery.
- Finally, empower students to become creators, not just users.
To truly prepare students for life after graduation, institutions and faculty must be willing to provide training on new technologies with a lens of learning. Faculty must move beyond fear and prohibition and engage directly with these tools. By exploring AI’s potential faculty can transform their teaching from a place of restriction to one of collaborative partnership.

By taking a development and scaffolded approach to AI implementation students can benefit from its potential. If this approach is embraced, the question is no longer “Should we allow AI in the classroom?” The more productive question becomes “How can we teach our students to become discerning and effective creators of a world shaped by AI?”

Michael Kiener, PhD, CRC, is a professor at Maryville University of St. Louis in their Clinical Mental Health Counseling program. For the past 10 years he has coordinated their Scholarship of Teaching and Learning Program, where faculty participate in a yearlong program with a goal of improved student learning. In 2012 and 2024 he received the Outstanding Faculty Award for faculty who best demonstrate excellence in the integration of teaching, scholarship and/or service. He has over thirty publications including a co-authored book on strength-based counseling and journal articles on career decision making, action research, counseling pedagogy, and active and dynamic learning strategies.
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November 5, 2025
Regurgitative AI: Why ChatGPT Won’t Kill Original Thought – Faculty Focus

Parents who grew up in the ’80s and ’90s know the feeling: you’re listening to your kid’s playlist, and suddenly a song hits you with a wave of uncanny familiarity. Despite the claims by your teen that it is the latest and greatest, you know that it is just a repackaging of one of your favorite tunes from the past. I am noticing a similar trend with generative AI. It is inherently regurgitative: reshaping and repackaging ideas and thoughts that are already out there.

Fears abound as to the future of higher education due to the rise of generative AI. Articles from professors in many different fields predict that AI is going to destroy the college essay or even eliminate the need for professors altogether. Their fears are well founded. Seeing the advances that generative AI has made in just the past few months, I am constantly teetering between immense admiration and abject terror. My Chatbot does everything for me, from scheduling how to get my revise and resubmit done in three months to planning my wardrobe for the fall semester. I fear becoming too self-reliant on it. Am I losing myself? Am I turning my ChatGPT into a psychological crutch? And if I am having these thoughts, what effect is generative AI having on my students?

Remix vs. Originality: Girl Talk or Beyonce

Grappling with the strengths and weaknesses of my own AI usage, I feel I have discovered what might be the saving grace of humanity (feel free to nominate me for the Nobel Peace Prize if you wish). As I hinted earlier, AI is more like a DJ remixing the greatest hits of society rather than an innovative game changer. My ChatGPT is more like Girl Talk (who you have probably never heard of. Just ask your AI) than Beyonce (who you most definitely have heard of). Not that there’s anything wrong with Girl Talk. Their mashups are amazing and require a special kind of talent. Just like navigating AI usage requires a certain balance of skills to create a usable final product. But no matter how many pieces of music from other artists you mash together, you will not eventually turn into a groundbreaking, innovative musician. Think Pat Boone vs. The Beatles, Sha Na Na vs. David Bowie, Milli Vanilli vs. Prince, MC Hammer vs. Lauryn Hill.

What AI Gets Wrong in Writing

As a mathematician and a novelist, I see this glaring weakness in both of these very different disciplines. I’ll start with writing. ChatGPT is especially helpful in coming up with strange character or planet names for my science fiction novels. It will also help me create a disease or something else I need to drive the plot further. And, of course, it can help me find an errant comma or fix a fragmented sentence. But that is about it. If I ask it to write an entire chapter, for example, it will come up with the most boring, derivative, and bland excuse for prose I have ever seen. It will attempt my humor but fail miserably. It sometimes makes my stomach turn, it’s so bad.

A study from the Wharton School found that ChatGPT reduces the diversity of ideas in a pool of ideas. Thus, it diminishes the diversity of the overall output, narrowing the scope of novel ideas. Beyond that, I find that when I use ChatGPT to brainstorm, I typically don’t use its suggestions. Those suggestions just spark new ideas and help me come up with something different and more me.

For example, I asked ChatGPT to write a joke for its bad brainstorming practice of using the same core ideas over and over again. It said:

Joke: That’s not brainstorming—it’s a lazy mime troupe echoing each other.

That’s lame. I would never say that. But another joke it gave me sparked the music sampling analogy I opened this article with.

In any case, because of generative AI’s inability to actually generate anything new, I have hope that the college essay, like the fiction novel, will not die. Over-reliance on AI may indeed debilitate the essay, perhaps causing it to go on life support forcing students and faculty to drag its lifeless body across the finish line of graduation. But there is still hope.

I remember one of my favorite English teachers in middle school required that we keep a journal. Each day she asked us to write something, anything in our journal, even if it was only a paragraph or just a sentence. Something about putting pen to paper sparked my creativity. It also sparked a lifelong notebook addiction. And even though I consider myself somewhat of a techie and a huge AI enthusiast, to this day I still use notebooks for the first draft of my novels.

It is clear to me that ChatGPT will never be able to write my novels in my voice. I don’t claim to be a great novelist. I just feel that some of my greatest work hasn’t been written yet. While ChatGPT may be able to write a poem about aardvarks in the style of Robert Frost or a ballad about Evariste Galois in the style of Carole King, it can’t write my next novel, because it doesn’t yet exist. And even when it tries to imitate my voice and my style, predicting what I will write next, it does a poor job.

The Research Paper Dilemma: AI vs. Process

A research paper is inherently different from a creative work of fiction, however. ChatGPT does do a pretty good job of gathering information on a topic from several sources and synthesizing it into a coherent paper. You just have to make sure to check for the errant hallucinated reference. And honestly, when are our students ever going to be asked to write a 15-page research paper on Chaucer without any resources? And if they are, ChatGPT can probably produce that product better than an undergraduate student can. But the process, I would argue, is more important than the final product.

In his Inside Higher Ed paper Writing the Research Paper Slowly, JT Torres recommends a scaffolding process to writing the research paper. This method focuses on the process of writing a paper, exploring and reading sources, taking notes, organizing those notes into a ‘scientific story’ and creating an outline. Teaching students the process of writing the paper instead of focusing on the end product results in students feeling more confident that they can not only complete the task required but transfer those skills to another subject. Recognizing these limitations pushed me to rethink how I design assignments.

Using AI in the Classroom

Knowing that generative AI can do somethings (but not all things) better than a human has made me a more intentional professor. Now when I create assignments, I think: Can ChatGPT do this better than an undergraduate student? If so, then what am I really trying to teach? Here are a few strategies I use:

Method #1: Assess Your Assessments with AI in Mind

When designing an assignment, ask yourself whether it is testing a skill that AI already performs well. If so, consider shifting your focus to why that skill matters, or how students can go beyond AI’s capabilities.

Method #2: Use AI Where It Adds Value – Remove It Where It Does Not

In some cases, it makes sense to integrate AI directly into the assignment (e.g., generating code, automating data analysis). In others, the objective may be to build a human-only skill like personal expression or creative voice. I decide case by case whether AI should be a part of the process or explicitly excluded.

Method #3: Clarify Whether You are Teaching Theory or Application

When I am teaching tests, I have to ask myself: Am I assessing whether students understand the theory behind the test or whether they can run one using software? If it’s the latter, using AI to generate code might be appropriate. But if it’s the former, I’ll require manual calculations or a written explanation.

Method #4: Add a Reflection to any AI Supported Assignment

Any assignment where they are allowed to use AI, they also have to write a reflection about how they used AI and whether it was helpful or not. This encourages metacognition and reduces overreliance.

Method #5: Require Students to Share Their Prompts and Revisions

Having students share the prompts they used in completing the assignment teaches them about transparency and the need for iteration in their interaction with an AI. Students should not just be cutting and pasting the first response from ChatGPT. They need to learn how to take a response, analyze it then refine their prompt to get a better result. This helps them develop prompt engineering skills and realize that ChatGPT is not just a magic answer machine.

AI and the Limits of Innovation in Research

What about academic research in general? How is AI helping or hindering? Given that generative AI merely remixes the greatest hits of human history rather than creating anything new, I think its role in academic research is limited. Academic breakthroughs start with unasked questions. Generative AI works within the confines of existing data. It can’t sense the frontier because it doesn’t know there is a frontier. It can’t sample past answers of a question that hasn’t been asked yet. About a year ago, I was trying to get my AI to write a section of code for my research and it kept failing. I spent a week trying to get it to do what I wanted. I realized it was having such a difficult time because I was asking it to do something that hadn’t been done before. Finally, I gave up and wrote the piece of code myself, and it only took me about half an hour. Sure, the coding capabilities have gotten better over the past year, but the core principle remains the same. AI still struggles to innovate. It can’t do what hasn’t already been done. Also because of ‘creative flattery’ it wants to make you happy so it will try to do what you tell it to do even if it can’t. The product will be super convincing, but it can still be wrong.

I recently asked AI to write a theoretical proof that shows polygonal numbers are Benford distributed (Spoiler: They are not). Then I had it help me write a convincing journal-ready article. The only problem is it also wrote me a theoretical proof that Polygonal numbers are NOT Benford distributed as well. I submitted the former to a leading mathematics journal to see what would happen. Guess what, they caught it. A human was able to detect the ‘AI Slop’. This shows me, that (1) there will always be a need for human gatekeepers and (2) ‘creative flattery’ is extremely dangerous in a research setting and confirms the need for human review. The chatbot tries too hard to please, thus reinforcing what the user already thinks even if that means proving or disproving the exact same thing. Academic research thrives on novel questions and unpredictable answers, which AI is incapable of doing since it inherently just regurgitates what is already out there.

Helping Students See AI’s Blind Spots

The Benford Polygonal Numbers experiment is an important example of how we need to educate our students about AI usage in an academic setting. The Time.com article Why A.I. is Getting Less Reliable, Not More states that despite its progress over the years, AI can still resemble sophisticated misinformation machines. Students need to know how to navigate this.

One of my favorite assignments in my Statistics course is what I call:

Method #6: “Beat ChatGPT” – A Concept Mastery Challenge

Students must craft a statistics question that the chatbot gets wrong, explain why the chatbot got it wrong and then provide the correct answer. A tweak of this activity would be to take AI generated content and human written then compare and critique tone, clarity, or originality.

Remixing isn’t Creation

AI-generated content is like a song built entirely from remixed samples. Sampling has its place in music (and in writing) but when everything starts to sound the same, our ears and brains begin to tune out. A great remix can breathe new life into a classic, but we still crave the shock of the new. This is why people lost their minds the first time they heard Beyonce’s Lemonade or Kendrick Lamar’s To Pimp a Butterfly – not because they followed a formula, but because they bent the rules and made something we’d never heard before. AI, for all its value, doesn’t break the rules. It follows them. That is the difference between innovation and imitation. It is also the reason why AI, in its current capacity, will not kill original thought.

Sybil Prince Nelson, PhD, is an assistant professor of mathematics and data science at Washington and Lee University, where she also serves as the institution’s inaugural AI Fellow. She holds a PhD in Biostatistics and has over two decades of teaching experience at both the high school and college levels. She is also a published fiction author under the names Sybil Nelson and Leslie DuBois.

References

Hsu, Hua. 2025. “The End of the English Paper.” The New Yorker, July 7, 2025. https://www.newyorker.com/magazine/2025/07/07/the-end-of-the-english-paper.

Warner, John. 2024. “Get Ready for Faculty Bot-ification.” Inside Higher Ed, December 11, 2024. https://www.insidehighered.com/opinion/columns/just-visiting/2024/12/11/great-ready-faculty-bot-ification.

Meincke, Lea, Gideon Nave, and Christian Terwiesch. 2025. “ChatGPT Decreases Idea Diversity in Brainstorming.” Nature Human Behaviour 9: 1107–1109. https://doi.org/10.1038/s41562-025-02173-x.

Torres, J. T. 2021. “Writing the Research Paper Slowly.” Inside Higher Ed, May 5, 2021. https://www.insidehighered.com/advice/2021/05/05/benefits-new-approach-student-research-papers-opinion.

Sonnenfeld, Jeffrey, and Joanne Lipman. 2024. “Why A.I. Is Getting Less Reliable, Not More.” Time, June 20, 2024. https://time.com/7302830/why-ai-is-getting-less-reliable/.

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October 29, 2025
The Importance of Connection in the Age of AI – Faculty Focus

As educators, we must stay current. What could be more current than Artificial Intelligence? Our students are using this tool at an unprecedented rate, and every technological tool we have is guided by it. We are taking classes to learn how to use it in the classroom and how to teach our students to use it. Grammarly is editing this very article! You are not alone if you feel a bit reticent to jump on the bandwagon. Will it ultimately replace us as educators? As people?

“Artificial Intelligence”. The issue is embedded right in the name: Artificial means not real. Is it here to stay, and can we even fight it? One of our generation’s premier philosophers, Dr. Yuval Harari, said that if we hope to survive, we better fight it (2015). Technology has been hypothesized to be an evolutionary mismatch (Li & Colarelli, 2017). This term implies that behaviors that once supported a species have become injurious. An example of a mismatch is sugary foods. Our nomadic ancestors struggled to procure enough daily calories to sustain life. When they found sugary food, they filled up on it and stored it. In our modern day, too much sugar leads to issues related to early mortality.

Technology can be used as a tool to keep us connected. Unfortunately, it has also slowly evolved into a system that answers every question, educates, and can now act as a companion, moving us slowly away from one another, like the frog in the hot water who realizes too late it is boiling.

The Hidden Costs of Disconnection

What is the cost of this instant ‘answerer of all questions’ and constant companion? It is hard to quantify, but the loss of human interaction is vast and far-reaching. Humans, with their higher cognitive functioning, can live on their own and survive thanks to technology. But should they? The longest social science studies suggest that healthy aging is directly related to meaningful and supportive relationships (Bosworth, & Schaie, 1997; Waldinger, & Schulz, 2023).

Additionally, humans are creatures who are, to simplify, guided by neural stimulation. When we are stressed, or trying to manage life alone, the stress hormone cortisol increases (Doane, L. & Adam E. 2009). High cortisol levels are directly related to inflammation, aging, and many other ailments, and we know that loneliness leads to early mortality (Holt-Lunstead & Layton, 2010). The most efficient home-grown remedy to combat an increase in cortisol is face-to-face meaningful engagement, which will release oxytocin, called the ‘love drug’. Our bodies, meant to be social, will release oxytocin when we engage, which will help to mitigate the system that manages cortisol. Unfortunately, artificial intelligence does not release meaningful amounts of oxytocin, and no pills exist to take because they do not cross the blood-brain barrier (Young-Kuchenbecker, Pressman, Celniker, Grewen, Sumida, Jonathan, Everett, & Slavich, 2021). We are left with the innate and evolutionary need for connection.

If we are to believe Darwin, then the fittest will make it, and most of us know what fitness entails. Fitness is about the mind, the body, and the demands (either placed on you or by you) of your environment. Recent MIT findings suggest that AI has a deleterious impact on our memory and has a high cognitive cost. Participants in the study could not even quote their own work (Kosmyna, Hauptmann, Yuan, Situ, Liao, Beresnitzky, Braunstein, & Maes, 2025).). As we are living longer than any previous generation, our sophisticated society necessitates that we maintain our cognitive fitness for as long as possible. AI certainly appears to be a mismatch in healthy long-term aging.

Educators as Builders of Connection

As educators, our job is to teach the topic at hand along with the soft skills of connection, engagement, community, teamwork, and the power that can be harnessed by more than one mind. Our college-age students suffer the most from loneliness and all the physical and psychological challenges inherent in that experience (Caccioppo & Caccioppo, 2018). In a classroom study, my students investigated loneliness on our campus and found that out of 100 students, 99 of them reported feeling lonely, and it influenced their use of technology (2024).

Artificial Intelligence brings information to our fingertips that might otherwise be unobtainable. It can teach, educate, partner, and save us a lot of time, but we need to learn to use it as a tool and not have it use us. We used to ask questions of experts, older or wiser, which invited connection. Now we ask our device questions, which invites disconnection in that how we phrase our questions to AI will determine the breadth and depth of the answer. In our digital age, the user curates their information (Kjerstin & Wells, 2016). Without another person to offer insight and possible opposing views, the user will often be left with tremendous confirmation bias.

Evolution has taught us that an organism has the best chance of survival if it is connected to others. The Pando in Utah is an ideal example. In Fish Lake National Forest’s 106-acre area, almost 50,000 aspen trees are interconnected with one root system. What infects one tree infects them all. So, how can we help our students connect and use AI effectively while keeping the detrimental effects of AI at bay?

This educator has gone back to a bit of paper and pencil. In the classroom, the students work in small groups that vary weekly on a homework assignment. They can partner for a test and have several out-of-class projects that require a little time to have a conversation. One of the assignments is to record a video of their group talking over a sensitive topic, one that they might not have been comfortable discussing in class. Yes, it takes a bit more time to plan and to grade. However, most of us are teaching a topic that does not lead to a qualifying test or credential so we can afford to cut a bit of material in lieu of helping our lonely students. If the results of the MIT study are to be believed (Kosmyna, 2025), if we don’t do something different, those same students will leave without the knowledge we hoped they would gain or the comfort of connection a classroom can provide. We can do better and need to if we, as educators, are to stay relevant.

Jennifer Smith, PhD, CFLE, is an Assistant Teaching Professor at Kansas State University in the Department of Psychological Sciences. Jennifer received her bachelor’s degree in both psychology and human development from the University of Wisconsin and her master’s degree in counseling from Lakeland University. Additionally, she obtained her PhD in Lifespan Human Development from Kansas State University, with her dissertation focusing on the intersection of technology and relationships. Jennifer is also a CFLE (Certified Family Life Educator) from the National Council on Family Relations. She describes her perspective on all things as “contextual” and approaches her teaching through this lens. Jennifer loves teaching above all else. Her teaching philosophy is “empathic teaching engenders curious learners.”! When not with students, she enjoys traveling with her husband of 30 years, time with her two daughters, serving in her community and naps with her cats!

References

Bosworth, H. & Schaie, K. (1997). The Relationship of Social Environment, Social Networks, and Health Outcomes in The Seattle Longitudinal Study: Two Analytical Approaches. The journals of gerontology. Series B, Psychological sciences and social sciences. 52. P197-205. 10.1093/geronb/52B.5.P197.

Cacioppo, J. T., & Cacioppo, S. (2018). The growing problem of loneliness. The Lancet; the Lancet, 391(10119), 426. doi:10.1016/S0140-6736(18)30142-9

Doane, L. & Adam E.( 2009) Loneliness and cortisol: momentary, day-to-day, and trait associations. Psychoneuroendocrinology. 2010 Apr;35(3):430-41. doi: 10.1016/j. PMID: 19744794; PMCID: PMC2841363.

Harari, Y. N. (2015). Sapiens: A brief history of humankind. HarperCollins Publishers.

Holt-Lunstad, J., Smith, T., & Layton, J. (2010). Social relationships and mortality risk: A meta-analytic review (social relationships and mortality). PLoS Medicine, 7(7), e1000316. doi:10.1371/journal.pmed.1000316

Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task. ArXiv. https://arxiv.org/abs/2506.08872

Li, N., van Vugt, M. & Colarelli, S. (2017) The evolutionary mismatch hypothesis: Implications for psychological science. Current Directions in Psychological Science, 27, 38-44. doi:10.1177/0963721417731378.

Kjerstin, T., Wells, C. (2016) Curated Flows: A Framework for Mapping Media Exposure in the Digital Age, Communication Theory, Vol. 26, (3), p. 309–328. https://doi.org/10.1111/comt.12087

Waldinger, R., & Schulz, M. (2023). The Good Life: Lessons from the World’s Longest Scientific Study of Happiness. Simon & Schuster. 

Young Kuchenbecker, S., Pressman, S. D., Celniker, J., Grewen, K. M., Sumida, K. D., Jonathan, N., Everett, B., & Slavich, G. M. (2021). Oxytocin, cortisol, and cognitive control during acute and naturalistic stress. Stress (Amsterdam, Netherlands), 24(4), 370–383. https://doi.org/10.1080/10253890.2021.1876658

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October 13, 2025