Category: Teaching with Technology

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

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

A Vision for the Future

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

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

Innovation: Reshaping Learning for All

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

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

Collaboration: Strengthening the Educational Ecosystem

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

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

Commitment: Building a System That Works for Everyone

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

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

The Path Forward

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

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

References

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

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

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

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

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

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

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

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December 8, 2025
Hearing Your Student Evaluations Differently? – Faculty Focus
Not long after I had received most of my student’s mid-semester survey results, I came across an AI tool that would create a song. The tool is Suno (suno.com) and it uses AI to create both the music and the lyrics. For some odd reason, I wondered if it might create a song based on by student survey results?

I needed lyrics for the song, so I took the survey results and placed them into Google’s Gemini to develop a summary of the results. The summary was not poetic nor lyrical, so I asked Gemini to write some song lyrics based on the survey summary. Silly I know, but that is how this story started. I then added the lyrics to Suno and chose ska/reggae as the style of music. It was pretty fun to hear about my class though a happy, upbeat song. The students in the class got a kick out of it too and the faculty I shared it with got a good laugh.

And there it was, a song about my class and how the students felt about it.

Having spent the last 20 years as an instructional designer, I have worked with faculty enough to know how difficult institutional surveys can be. Often, the most disgruntled students point to course deficiencies and that is what the faculty remember. Then there are deciles and scores and comparisons and the worry that these numbers will negatively affect the tenure process or rehiring as a part time faculty. In general, there is a negative vibe around these surveys and how they are used. I wondered if those single page pdfs could be shared in a different way? Maybe someone singing the results to them? Maybe just a friendly voice letting them know about the things they did well and the things they might improve upon.

I thought about how different the experience of a voice might be when delivering the information. Might it “feel” better? Worse?

I had been playing with Google’s recently released NotebookLM (notebooklm.google.com) and I wondered what raw survey data might sound like in the “podcast” feature. I started with just my self-created mid-course evaluations and it was really interesting to hear two “people” talk about my class. I wondered what other “sources” I might give NotebookLM to fill out the conversation and how I might customize the podcast.

At first, I added the course goals and objectives. But as you can also add YouTube videos, so I added my seven-minute course introduction from YouTube. The class was online, so I added the QM standards and because the class was about pedagogy and technology, I added the ISTE standards. Adding these other “sources” really started to flesh out the podcast as the successes and challenges were framed against existing standards in teaching and learning. Listening too it kept me present on the topic of improving my class and now I was hearing how my student feedback stacked up against common learning practices.

Soon it was the end of the course and I had asked the students for final evaluation with a few broad questions. They all wrote quite a bit about their experiences in the course, and I added it to the sources in NotebookLM. I also added a video about open pedagogy by David Wiley and another from Stephen Downes. I included a piece of writing about care and inclusivity in higher education. By adding all of those, the podcast was becoming very detailed and full of ideas for improvement backed up by the solid resources I had provided.

I added an AI generated summary of John Dewey’s thinking about learning and another about the same from Carl Rogers. In this way, I was getting the information to include my heroes and favorites in the field. The rationale the podcasters presented for course improvement was backed up by a lot of good thinking! While I started with just the mid-quarter evaluations, as of today, the list of sources looks like this:
- Mid-Quarter Student Survey
- Course Final Reflections by Students
- Institutional Course Evaluation
- QM Higher Education Standards
- ISTE Standards for Teachers and Students
- High Impact Practices in Higher Education
- Course Description and Objectives
- College Program Goals and Objectives
- Care and Inclusion in Higher Education
- Carl Rogers Theory of Education
- Introduction to Open Pedagogy – Stephen Downes
- John Dewey’s Theory of Teaching & Learning
- Open Pedagogy – David Wiley
- All the Course Announcements
- Class Introduction Video
- Reflections on Teaching with Google Sites
Then, perhaps the most deeply impactful thing I did was I customized the prompt to make the podcasters speak directly to me, Todd.

Up to this point the podcasters were simply talking generically about a class, now they were talking to me directly by name. It made it feel far more personal. It was more personal. And it was kind of creepy, but it made a difference.

One thing I learned was that even if the speakers are AI generated, it feels good to hear positive things about your course and especially if the kind words are backed up with how some of your heroes would agree with what you are doing. The podcasters always start out with praise and that feels better than the charts and decile numbers on the university student survey.

I keep trying to get the podcast to really focus on improvement and thus far I have already made a few adjustments to the class based on suggestions they made. Those suggestions were alluded to in the student narratives, but the whole of resources I had included in the NotebookLM made those comments seem different. I don’t know how to explain that. Maybe it was that in the student narrative it was one or two sentences among many and in the podcast, it was worded differently, and more detail was given. The human voice is amazingly powerful, even when it is not actually human.

Where am I today? Well, I started seeing if I could transform the two podcasters text into a single person giving me the same advice. I am interested to see if a single voice feels different? It has met with limited success as all of this has been done with free tools. There are time limits and character limits on the free versions of many of the tools.

Google’s NotebookLM has a beta feature that allows you to ask the podcasters questions. I have learned that if you state your name, they refer to you directly. I have also learned that the answers they provide are pretty amazing and that you can really drill down to pretty specific detail around assignments and course structure.

I have also been sharing this with other faculty, and I have had nothing but good feedback about the experience. One noted that they thought the podcast would make a great advertisement for their course. They also said they had wished that they had the tool when they were writing their tenure package as it combined a good deal of disparate pieces of information into a single space. Another said that hearing the student feedback was a more positive experience compared to reading the data. I also heard about the concerns they have. Concerns like privacy and the AI tools making mistakes. To that end I would say, don’t put student names in the AI tools and make sure to listen carefully to how the sources are expressed.

And now, NotebookLM allows you to create narrated PowerPoint type presentations based on your notebook resources!

If you think this might be a way to improve your teaching, NotebookLM is a free tool and you can give this a try right now. https://notebooklm.google.com/

Listen to the evaluations discussed above:

Ska/Reggae song about the class: https://suno.com/s/xLwnhEqgRuahr9I6

Most recent version of podcast about the class: https://on.soundcloud.com/KhLdMLmifT1OIgYlyG

A video from NotebookLM about same topic:

Todd Conaway is an instructional designer at the University of Washington | Bothell with eighteen years of experience working with faculty in and around digital pedagogy. He was a high school English teacher and has been teaching a variety of courses at the community college and university level for twenty-five years.
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December 3, 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.
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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.
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November 17, 2025
Beyond Breakout Rooms: Deepening Learning with Collaborative Whiteboards – Faculty Focus
Distance learning is here to stay. Both students and educators were required to quickly pivot to distance platforms during the COVID-19 pandemic without adequate preparation or training (Basilotta-Gomez-Pablos et al. 2022). Many programs elected to keep distance and hybrid learning options for students, with excellent reason. These platforms improve convenience, access, and inclusivity, gaining fast traction. Now, many institutions have an opportunity, or arguably a responsibility, to provide educators with the support needed to be successful while teaching in distance modalities; concurrently, educators are responsible for seeking resources and training to help them leverage technologies to thrive in this distance space (Crompton and Sykora 2021).

The use of technology in distance platforms is important; educators need to navigate a learning platform but don’t necessarily need to become digital technology experts (Crompton and Sykora 2021). So, what is possibly most important? Finding tools that are simple to use and have a high impact. Many educators still report that they do not feel equipped to apply new technologies, although today’s adult learners prefer novel and engaging technology tools (Borte, Nesje, and Lillejord 2023). Therefore, us educators should discuss, introduce, and share these technologies as we discover them to figure this out as a team.

In this article, we will explore how collaborative technologies, specifically collaborative whiteboards, can help bring life to adult learning theories in synchronous learning classrooms.

Students in distance learning platforms often have fewer opportunities to work collaboratively, outside of a break-out-room model, providing challenges for meaningfully applying adult learning theories. Theoretically speaking, collaboration and social engagement are essential components of adult learning, specifically to create a sense of community with learners, which can be more challenging in distance learning classes (Barbetta 2023; Shea, Richardson, and Swan 2022). Additionally, engaging students in more cognitively demanding ways, such as creating, analyzing, or developing information, is especially important to achieve higher-level learning outcomes (Vargas et al. 2024). Therefore, if we can strategically find ways to use digital technologies grounded in adult learning theory, we can strengthen the learning experience, enhance learning outcomes, and bridge the gap between theory and content delivery.

Many digital technologies and platforms exist and are met with opportunities and challenges. Time constraints and cost are common barriers (Borte, Nesje, and Lillejord 2023); therefore, free tools with relatively low preparation are prioritized. The Microsoft Collaborative Whiteboard app, which can be integrated within a Microsoft Teams Meeting space as a screen share, allows students to edit a document or template for more naturalistic collaborations simultaneously. Because it can be integrated into the meeting space, students do not need to download an app, leave the meeting, or switch to a new browser to participate. The form can be prepared before a lesson to allow instructors flexibility in their role and level of scaffolding.

Real-World Examples

A collaborative whiteboard can be applied to various programs, topic areas, or overall aims, and it may also be used as a formative assessment of content delivery. The examples included in this article were used in two separate synchronous occupational therapy courses, one focused on a neuroscience recap of the cranial nerves and one on adapting a therapeutic activity for different levels of traumatic brain injury rehabilitation. Pay special attention to the variance between the levels of learning targeted and how this is reflected in the collaborative efforts of the students.

Basic-level Whiteboard

This synchronous whiteboard was used at the beginning of the class session to serve as a formative assessment of the understanding of basic concepts of content. The 24 students were asked to match the function’s cranial nerve name, number, and a representative emoji. This was a more simplistic board that allowed students to reorganize the information presented at a basic knowledge-attainment level collaboratively. It was helpful as a formative assessment to adapt the rest of the lesson according to the students’ level of understanding, and their pace and accuracy of completion. In this case, the students moved their pointers to arrange the information simultaneously, with only a few attempts to collaborate verbally. This activity provided a collaborative effort to complete a joint, goal-oriented task.

Before

After

Advanced-level Whiteboard

The synchronous whiteboard in this example was created for a higher level of application-based learning, where 11 students were asked to alter components of an occupation and treatment modality, baking cookies, to apply to each unique level of traumatic brain injury recovery. This activity demanded greater levels of problem-solving and, therefore, greater levels of collaboration within the class. Many discussions and opportunities for problem-solving and aims for collective approval arose. To close the learning activity, I led a group debrief to discuss each level, provided immediate feedback, and facilitated discussions transferable to other real-world applications. In this example, the directions are in the middle, and the students filled in the sticky notes around the perimeter.

Make it Meaningful

We need buy-in and motivation in higher education. This begins with identifying real-world challenges and ends with reflection. The strategic use of digital technologies, specifically when combined with theoretical reasoning and adult learning principles, can improve classroom experiences through a greater sense of community while targeting higher levels of learning. If we are intentional with this design, educators can increase student engagement and facilitate higher levels of learning in synchronous online classrooms. Sprinkle goal-oriented and real-world relevant material on top, and you have a recipe for a meaningful outcome.

Looking Ahead

With institutions more widely embracing long-term implementation of distance education programs, faculty require ongoing support, training, and access to resources for sustained confidence and success. Dissemination of practical examples of use by peer educators and texts outlining an approach to identify and develop materials will benefit ongoing efforts to increase competency. In other words, we must keep each other informed on educational tech-gems we find to collectively improve our system.

How to Whiteboard: Quick Start Guide
1. Open Microsoft Teams
1. Click on the Whiteboard tab in your meeting space
1. Upload an editable template or create a blank canvas
1. Once in your meeting, share your screen and select the whiteboard app
1. Scaffold your task with prompts and questions
Jaimee Fielder, OTD, OTR is an Assistant Professor of Occupational Therapy at the University of St. Augustine for Health Sciences. She earned her Master of Science in Occupational Therapy from Touro University Nevada, Post-Professional Doctorate in Occupational Therapy from Texas Woman’s University, and is now pursuing a Doctorate in Education from the University of St. Augustine for Health Sciences, with a dissertation focused on educational technology and active learning in higher education classrooms.

References

Barbetta, Patricia M. 2023. “Technologies as Tools to Increase Active Learning During Online Higher-Education Instruction.” Journal of Educational Technology Systems 51(3): 317–339.

Basilotta-Gomez-Pablos, Verónica, Matarranz, María, Casado-Aranda, Luis A., and Otto, Andreas. 2022. “Teachers’ Digital Competencies in Higher Education: A Systematic Literature Review.” International Journal of Educational Technology in Higher Education 19(8).

Børte, Kristi, Nesje, Kjersti, and Lillejord, Sølvi. 2023. “Barriers to Student Active Learning in Higher Education.” Teaching in Higher Education 28(3): 597–615.

Crompton, Helen, and Sykora, Christopher. 2021. “Developing Instructional Technology Standards for Educators: A Design-Based Research Study.” Computers and Education Open 2: 100044.

Shea, Peter, Richardson, Jennifer, and Swan, Karen. 2022. “Building Bridges to Advance the Community of Inquiry Framework for Online Learning.” Educational Psychologist 57(3): 148–161.

Vargas, Jesús H., Ojeda, Edison C. C., Zapata, Carlos A. C., Flores, Karen A. A., Vela, Juan A. H., and Espinoza, Yessenia E. D. 2024. “Analysis of Significant Learning in Higher Education: Usefulness of Fink’s Taxonomy: A Systematic Review.” Journal of International Crisis and Risk Communication Research 7(S7): 1341.
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November 12, 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
Chatbots in Higher Education: Benefits, Challenges, and Strategies to Prevent Misuse – Faculty Focus

Artificial intelligence (AI) platforms, especially chatbots like ChatGPT and Gemini, are influencing many areas of higher education. Students and instructors can interact with these tools to get real-time, personalized help with nearly any academic task at hand, including helping students study or revamping course content for instructors. There is no question that the educational potential is significant, but so too are the concerns about academic integrity and the consequences of students relying too heavily on these tools. While it’s important to weigh the pros and cons of chatbots, using the right strategies and technologies can help avoid those issues and still allow faculty and students to benefit from this burgeoning technology.

Use Cases for Chatbots in Higher Education Teaching

First, let’s consider uses among faculty. They can use chatbots to help improve most areas of teaching and learning while still saving time.

Chatbots can assist with different phases of course design, including writing learning objectives and developing and repurposing content. Chatbots can also be used in evaluating student performance data if your institution offers a secure chatbot (just be sure to anonymize the data). Instructors can also use chatbots to review the rules and instructions for their assignments and tests to identify vague or subjective text and suggest clearer, more objective wording. 

But one of the most impactful uses is adapting course content into formats that support students with different learning needs. Chatbots can adapt, simplify and organize text for students with learning or cognitive disabilities, generate alternative text for multimedia content, and they can even adjust their responses based on student emotions (for example, offering words of encouragement when a student is frustrated or discouraged).

Instructors may be tempted to use chatbots to grade written coursework as well, but for now, it is important to note that the technology still struggles with the nuances of grading and feedback. However, chatbots can still help by pulling out the main ideas and revealing whether a student’s essay meets basic criteria, which gives instructors a clearer starting point before reading.

You’ll see a lot of jargon and articles that make prompt writing seem complicated, but you don’t need to be an expert to write effective prompts. It’s a skill you can pick up quickly after a few tries, especially once you find what works for you and start creating your own templates to use in your courses.

Students and Chatbots: Finding a Balance

There are many ways that students can use chatbots to support learning while steering clear of misusing them for tests and assignments. Ensuring ethical and beneficial adoption of AI requires clear strategies and guidelines along with the right technology.

Improving Academic Performance

Recent research indicates that chatbots can improve learning and academic outcomes, but balance is key. When students rely on them too heavily, any benefits, like increased engagement, motivation and reflection are reduced or eliminated. When used appropriately, AI chatbots can tutor students to help them better understand and comprehend the information, which also increases engagement. Chatbots give students instant, personalized help with tasks such as summarizing content and checking grammar, and they also create a nonjudgmental space where students feel more comfortable asking questions.

Cognitive Load Reduction and Stress Management

Chatbots are ideal for offloading tasks such as summarizing a lecture transcript or highlighting key points in an assignment. With students under pressure to complete assignments, leaning on AI can also reduce stress and anxiety by giving them more time to focus on the important elements of an assignment. 

Similarly, chatbots can help students visualize information by generating charts, graphs or images to support their ideas. They can also provide adaptive support for students with disabilities or language barriers by reading, translating, simplifying or reformatting content to meet their needs. This reduces cognitive load, allows students to focus on the assignment itself and builds confidence in their ability to complete the work.

Strategies for Reducing Chatbot Misuse

Because of the speed and ease of use for chatbots, students may begin to use them as a shortcut to get work done instead of a supplemental learning tool. That’s a big issue. It bypasses critical thinking, contextual understanding and collaboration. This not only cheats the student out of learning but creates academic integrity issues for higher education institutions to address and stay ahead of.

Cheating is common in higher education, and with recent surveys indicating that more than 85% of students use AI daily to help with schoolwork, there’s a good chance of overlap. Students have mixed opinions when it comes to using AI for homework, with around 40% of students acknowledging that using AI for research and writing assistance should be acceptable, but there should also be limitations and ethical use.

Communicating with students about academic integrity codes of conduct, AI use, and cheating policies and ramifications are the first steps in setting expectations. But delivering assignments in ways that reduce opportunities for the use of chatbots or that lay out specific guardrails for chatbot use can also be helpful.

Reducing Chatbot Cheating

Cheating in higher education will never completely go away, and efforts to maintain and manage tools and channels used for cheating must be ongoing. As more students leverage chatbots for both approved and unapproved use, educational institutions can work to help students understand the appropriate way to leverage technology within the learning environment.

While educators struggle to detect AI use reliably, AI detection tools can identify unedited chatbot-generated content with reasonable accuracy. But their effectiveness drops when students edit or modify responses or use AI paraphrasing tools to rewrite the content. They’re more useful as a gut check, not definitive proof that the text was AI-generated.

Remote proctoring during assessments and written assignments adds another barrier to cheating. Ideally, the proctoring solution should leverage AI test monitoring together with live human proctors, which gives faculty the control to prevent AI use and the flexibility to allow approved resources. For example, instructors can proctor exams or essays by restricting access to all unauthorized websites and software, including chatbots, while still providing access to specific materials like case studies or tools like Word, Excel and Google Docs.

Another option is to use scaffolded, realistic assignments and assessments that focus on real-world application and requiring students to connect course content to their personal experiences or context. This makes it more difficult for chatbots to generate accurate or meaningful responses. For example, assessments could ask students to develop a scenario based on a local community issue, work on collaborative projects that include peer reviews or create video responses that reference specific class lectures or materials. This approach helps evaluate students’ true understanding of materials and that they are developing practical skills.

Set the Stage Now for an AI-Inclusive Future

AI chatbots are powerful tools that can make education more personalized, accessible and engaging. However, their misuse can undermine academic integrity and dilute learning. The solution, in most cases, isn’t to ban them outright, but to integrate them responsibly throughout teaching and learning processes. Striking this balance isn’t always easy, but it’s necessary to preserve the value of learning while preparing students for a future where AI will almost certainly be a part of their work.

Tyler Stike is the Director of Content at Honorlock and a doctoral student in educational technology at the University of Florida. In his role at Honorlock, he develops a wide range of content on online education, assessment, and accessibility. He is interested in how affective states influence learning and performance, and plans to research how AI can support adaptive learning experiences that help students manage those states.

References

W. Dai et al., “Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT,” 2023 IEEE International Conference on Advanced Learning Technologies (ICALT), Orem, UT, USA, 2023, pp. 323-325, doi: 10.1109/ICALT58122.2023.00100.

Strike, Tyler. “AI Prompting Examples, Templates, and Tips For Educators.” Resources for Education & Assessment, March 27, 2025, https://honorlock.com/blog/education-ai-prompt-writing/

Sánchez-Vera, Fulgencio. 2025. “Subject-Specialized Chatbot in Higher Education as a Tutor for Autonomous Exam Preparation: Analysis of the Impact on Academic Performance and Students’ Perception of Its Usefulness” Education Sciences 15, no. 1: 26. https://doi.org/10.3390/educsci15010026

Kelly, Rhea. “Survey: 86% of Students Already Use AI in Their Studies” Campus Technology, August 24, 2024 https://campustechnology.com/articles/2024/08/28/survey-86-of-students-already-use-ai-in-their-studies.aspx

McKearin, Cheryl. “Report on Student Attitudes Towards AI in Academia,” Learning Technology Solutions, April 5, 2024, https://learning.uic.edu/news-stories/report-on-student-attitudes-towards-ai-in-academia/

Kofinas, A. K., Tsay, C.-H., & Pike, D. (2025). The impact of generative AI on academic integrity of authentic assessments within a higher education context. British Journal of Educational Technology, 00, 1–28. https://doi.org/10.1111/bjet

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October 6, 2025