Tag: genAI

Framework for GenAI in Graduate Career Development (opinion)

In Plato’s Phaedrus, King Thamus feared writing would make people forgetful and create the appearance of wisdom without true understanding. His concern was not merely about a new tool, but about a technology that would fundamentally transform how humans think, remember and communicate. Today, we face similar anxieties about generative AI. Like writing before it, generative AI is not just a tool but a transformative technology reshaping how we think, write and work.

This transformation is particularly consequential in graduate education, where students develop professional competencies while managing competing demands, research deadlines, teaching responsibilities, caregiving obligations and often financial pressures. Generative AI’s appeal is clear; it promises to accelerate tasks that compete for limited time and cognitive resources. Graduate students report using ChatGPT and similar tools for professional development tasks, such as drafting cover letters, preparing for interviews and exploring career options, often without institutional guidance on effective and ethical use.

Most AI policies focus on coursework and academic integrity; professional development contexts remain largely unaddressed. Faculty and career advisers need practical strategies for guiding students to use generative AI critically and effectively. This article proposes a four-stage framework—explore, build, connect, refine—for guiding students’ generative AI use in professional development.

Professional Development in the AI Era

Over the past decade, graduate education has invested significantly in career readiness through dedicated offices, individual development plans and co-curricular programming—for example, the Council of Graduate Schools’ PhD Career Pathways initiative involved 75 U.S. doctoral institutions building data-informed professional development, and the Graduate Career Consortium, representing graduate-focused career staff, grew from roughly 220 members in 2014 to 500-plus members across about 220 institutions by 2022.

These investments reflect recognition that Ph.D. and master’s students pursue diverse career paths, with fewer than half of STEM Ph.D.s entering tenure-track positions immediately after graduation; the figure for humanities and social sciences also remains below 50 percent over all.

We now face a different challenge: integrating a technology that touches every part of the knowledge economy. Generative AI adoption among graduate students has been swift and largely unsupervised: At Ohio State University, 48 percent of graduate students reported using ChatGPT in spring 2024. At the University of Maryland, 77 percent of students report using generative AI, and 35 percent use it routinely for academic work, with graduate students more likely than undergraduates to be routine users; among routine student users, 38 percent said they did so without instructor guidance.

Some subskills, like mechanical formatting, will matter less in this landscape; higher-order capacities—framing problems, tailoring messages to audiences, exercising ethical discernment—will matter more. For example, in a 2025 National Association of Colleges and Employers survey, employers rank communication and critical thinking among the most important competencies for new hires, and in a 2024 LinkedIn report, communication was the most in-demand skill.

Without structured guidance, students face conflicting messages: Some faculty ban AI use entirely, while others assume so-called digital natives will figure it out independently. This leaves students navigating an ethical and practical minefield with high stakes for their careers. A framework offers consistency and clear principles across advising contexts.

We propose a four-stage framework that mirrors how professionals actually learn: explore, build, connect, refine. This approach adapts design thinking principles, the iterative cycle of prototyping and testing, to AI-augmented professional development. Students rapidly generate options with AI support, test them in low-stakes environments and refine based on feedback. While we use writing and communication examples throughout for clarity, this framework applies broadly to professional development.

Explore: Map Possibilities and Surface Gaps

Exploring begins by mapping career paths, fellowship opportunities and professional norms, then identifying gaps in skills or expectations. A graduate student can ask a generative AI chatbot to infer competencies from their lab work or course projects, then compare those skills to current job postings in their target sector to identify skills they need to develop. They can generate a matrix of fellowship opportunities in their field, including eligibility requirements, deadlines and required materials, and then validate every detail on official websites. They can ask AI to describe communication norms in target sectors, comparing the tone and structure of academic versus industry cover letters—not to memorize a script, but to understand audience expectations they will need to meet.

Students should not, however, rely on AI-generated job descriptions or program requirements without verification, as the technology may conflate roles, misrepresent qualifications or cite outdated information and sources.

Build: Learn Through Iterative Practice

Building turns insight into artifacts and habits. With generative AI as a sounding board, students can experiment with different résumé architectures for the same goal, testing chronological versus skills-based formats or tailoring a CV for academic versus industry positions. They can generate detailed outlines for an individual development plan, breaking down abstract goals into concrete, time-bound actions. They can devise practice tasks that address specific growth areas, such as mock interview questions for teaching-intensive positions or practice pitches tailored to different funding audiences. The point is not to paste in AI text; it is to lower the barriers of uncertainty and blank-page intimidation, making it easier to start building while keeping authorship and evidence squarely in the student’s hands.

Connect: Communicate and Network With Purpose

Connecting focuses on communicating with real people. Here, generative AI can lower the stakes for high-pressure interactions. By asking a chatbot to act the part of various audience members, students can rehearse multiple versions of a tailored 60-second elevator pitch, such as for a recruiter at a career fair, a cross-disciplinary faculty member at a poster session or a community partner exploring collaboration. Generative AI can also simulate informational interviews if students prompt the system to ask follow-up questions or even refine user inputs.

In addition, students can leverage generative AI to draft initial outreach notes to potential mentors that the students then personalize and fact-check. They can explore networking strategies for conferences or professional association events, identifying whom to approach and what questions to ask based on publicly available information about attendees’ work.

Even just five years ago, completing this nonexhaustive list of networking tasks might have seemed an impossibility for graduate students with already crammed agendas. Generative AI, however, affords graduate students the opportunity to become adept networkers without sacrificing much time from research and scholarship. Crucially, generative AI creates a low-risk space to practice, while it is the student who ultimately supplies credibility and authentic voice. Generative AI cannot build genuine relationships, but it can help students prepare for the human interactions where relationships form.

Refine: Test, Adapt and Verify

Refining is where judgment becomes visible. Before submitting a fellowship essay, for example, a student can ask the generative AI chatbot to simulate likely reviewer critiques based on published evaluation criteria, then use that feedback to align revisions to scoring rubrics. They can A/B test two AI-generated narrative approaches from the build stage with trusted readers, advisers or peers to determine which is more compelling. Before a campus talk, they can ask the chatbot to identify jargon, unclear transitions or slides with excessive text, then revise for audience accessibility.

In each case, verification and ownership are nonnegotiable: Students must check references, deadlines and factual claims against primary sources and ensure the final product reflects their authentic voice rather than generic AI prose. A student who submits an AI-refined essay without verification may cite outdated program requirements, misrepresent their own experience or include plausible-sounding but fabricated details, undermining credibility with reviewers and jeopardizing their application.

Cultivate Expert Caution, Not Technical Proficiency

The goal is not to train students as prompt engineers but to help them exercise expert caution. This means teaching students to ask: Does this AI-generated text reflect my actual experience? Can I defend every claim in an interview? Does this output sound like me, or like generic professional-speak? Does this align with my values and the impression I want to create? If someone asked, “Tell me more about that,” could I elaborate with specific details?

Students should view AI as a thought partner for the early stages of professional development work: the brainstorming, the first-draft scaffolding, the low-stakes rehearsal. It cannot replace human judgment, authentic relationships or deep expertise. A generative AI tool can help a student draft three versions of an elevator pitch, but only a trusted adviser can tell them which version sounds most genuine. It can list networking strategies, but only actual humans can become meaningful professional connections.

Conclusion

Each graduate student brings unique aptitudes, challenges and starting points. First-generation students navigating unfamiliar professional cultures may use generative AI to explore networking norms and decode unstated expectations. International students can practice U.S. interview conventions and professional correspondence styles. Part-time students with limited campus access can get preliminary feedback before precious advising appointments. Students managing disabilities or mental health challenges can use generative AI to reduce the cognitive load of initial drafting, preserving energy for higher-order revision and relationship-building.

Used critically and transparently, generative AI can help students at all starting points explore, build, connect and refine their professional paths, alongside faculty advisers and career development professionals—never replacing them, but providing just-in-time feedback and broader access to coaching-style support.

The question is no longer whether generative AI belongs in professional development. The real question is whether we will guide students to use it thoughtfully or leave them to navigate it alone. The explore-build-connect-refine framework offers one path forward: a structured approach that develops both professional competency and critical judgment. We choose guidance.

Ioannis Vasileios Chremos is program manager for professional development at the University of Michigan Medical School Office of Graduate and Postdoctoral Studies.

William A. Repetto is a postdoctoral researcher in the Department of English and the research office at the University of Delaware.

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October 20, 2025
Online Course Gives College Students a Foundation on GenAI

As more employers identify uses for generative artificial intelligence in the workplace, colleges are embedding tech skills into the curriculum to best prepare students for their careers.

But identifying how and when to deliver that content has been a challenge, particularly given the varying perspectives different disciplines have on generative AI and when its use should be allowed. A June report from Tyton Partners found that 42 percent of students use generative AI tools at least weekly, and two-thirds of students use a singular generative AI tool like ChatGPT. A survey by Inside Higher Ed and Generation Lab found that 85 percent of students had used generative AI for coursework in the past year, most often for brainstorming or asking questions.

The University of Mary Washington developed an asynchronous one-credit course to give all students enrolled this fall a baseline foundation of AI knowledge. The optional class, which was offered over the summer at no cost to students, introduced them to AI ethics, tools, copyright concerns and potential career impacts.

The goal is to help students use the tools thoughtfully and intelligently, said Anand Rao, director of Mary Washington’s center for AI and the liberal arts. Initial results show most students learned something from the course, and they want more teaching on how AI applies to their majors and future careers.

How it works: The course, IDIS 300: Introduction to AI, was offered to any new or returning UMW student to be completed any time between June and August. Students who opted in were added to a digital classroom with eight modules, each containing a short video, assigned readings, a discussion board and a quiz assignment. The class was for credit, graded as pass-fail, but didn’t fulfill any general education requirements.

Course content ranged from how to use AI tools and prompt generative AI output to academic integrity, as well as professional development and how to critically evaluate AI responses.

“I thought those were all really important as a starting point, and that still just scratches the surface,” Rao said.

The course is not designed to make everyone an AI user, Rao said, “but I do want them to be able to speak thoughtfully and intelligently about the use of tools, the application of tools and when and how they make decisions in which they’ll be able to use those tools.”

At the end of the course, students submitted a short paper analyzing an AI tool used in their field or discipline—its output, use cases and ways the tool could be improved.

Rao developed most of the content, but he collaborated with campus stakeholders who could provide additional insight, such as the Honor Council, to lay out how AI use is articulated in the honor code.

The impact: In total, the first class enrolled 249 students from a variety of majors and disciplines, or about 6 percent of the university’s total undergrad population. A significant number of the course enrollees were incoming freshmen. Eighty-eight percent of students passed the course, and most had positive feedback on the class content and structure.

In postcourse surveys, 68 percent of participants indicated IDIS 300 should be a mandatory course or highly recommended for all students.

“If you know nothing about AI, then this course is a great place to start,” said one junior, noting that the content builds from the basics to direct career applications.

What’s next: Rao is exploring ways to scale the course in the future, including by developing intermediate or advanced classes or creating discipline-specific offerings. He’s also hoping to recruit additional instructors, because the course had some challenges given its large size, such as conducting meaningful exchanges on the discussion board.

The center will continue to host educational and discussion-based events throughout the year to continue critical conversations regarding generative AI. The first debate, centered on AI and the environment, aims to evaluate whether AI’s impact will be a net positive or negative over the next decade, Rao said.

The university is also considering ways to engage the wider campus community and those outside the institution with basic AI knowledge. IDIS 300 content will be made available to nonstudents this year as a Canvas page. Some teachers in the local school district said they’d like to teach the class as a dual-enrollment course in the future.

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September 22, 2025
How To Teach With AI Transparency Statements – Faculty Focus

I remember talking to my students about GenAI for the first time, almost three years ago. We covered the uses and limitations of some tools. We talked about the danger of losing our own voice. We talked about the importance of writing in our own words, even if the end product isn’t as neat as AI-generated prose. I remember taking over the conversation, asserting what GenAI is good for and what it’s bad at. I fell into a pattern of announcing “this is fine” and “don’t do that.”

At the time, it was the best I could do.

Since then, I have spent a lot of time refining my approach to GenAI in the classroom. I realized that my initial approach – though sound at the time – deprived my students of agency. I allowed my students to share their ideas about the technology. But then, I stepped in to make all the decisions that really mattered.

My approach now is very different. I ask my students to design their own writing processes, implement them, and reflect on them. Some of my students work GenAI into the process, while others prefer to leave it alone. I empower my students to decide whether and how GenAI should play a role in their work. At the same time, I want them to be mindful of how easy it is to give our voices away when we use this technology.

The key to making this all work is AI Transparency Statements.

We can use these statements to encourage our students to use GenAI responsibly and to think more deeply about their relationships to this technology, while also building a culture of trust and accountability. They are invaluable.

In the rest of this article, I will cover the two actions I ask students to complete in their AI Transparency Statements. Feel free to replicate and adapt them.

Step 1: Students describe how they used GenAI, according to our AI scale

As mentioned above, my students design multi-step processes for completing their projects. In the first part of the AI Transparency Statement, I ask my students to go back to those steps and self-report if and how they used AI. And depending on the context, I sometimes ask them to submit chat transcripts with their statement.

I refer them to the AI Assessment Scale (AIAS), which was developed by Leon Furze, Mike Perkins, Jasper Roe, and Jason MacVaugh. This scale assigns numbers for levels of GenAI usage, ranging from 1 to 5. A 1 means the student used no GenAI. A 2 means that the student used GenAI for pre-task activities like planning and brainstorming. A 3 means that the student used GenAI for assistance in completing the task itself. For example, some students use GenAI programs to get feedback. Or they use it for rephrasing some sentences or a paragraph. A 4 means that the student used GenAI to complete the task, with their direction. A 5 means that the student is using GenAI to find innovative solutions to a problem and to boost their own creativity.

Often, professors treat the AIAS as a faculty-facing tool. For example, we might tell students to stick to level 2 for an essay outline: they can use a GenAI program for brainstorming and getting started, but we want them to create the outline themselves.

But for me, the real power of the AIAS is as a student-facing tool. We can ask students to give the different parts of their process a rating, and then walk through why they gave them that rating. This encourages responsible AI usage, as well as meta-awareness.

Step 2: Students defend their use or non-use of GenAI

Once students assign AIAS numbers to their process, I then ask them to defend their use or non-use of GenAI. Why did they feel that level-3 usage of GenAI was appropriate for that part in the process? What would have been lost if they went down to a level-2 usage or up to a level-4 usage?

As they defend their use or non-use of GenAI, I want them to think about power and personal voice. Did that specific use of GenAI empower them or did it take their power away?

The goal, here, is to encourage students to link their decisions to larger questions about personal empowerment. After all, the responsible use of AI isn’t just about upholding academic integrity. It’s about making decisions that don’t give away our voice.

Final Thoughts

Using AI Transparency Statements doesn’t AI-proof assignments, if such a thing were even possible. Rather, these are a chance to highlight the importance of self-reflection and intentionality as we interact with these systems — not only in classes, but in our everyday lives.

Without these skills, it’s incredibly easy to lose ourselves.

Jason Gulya is a Professor of English and Communications at Berkeley College, where he chairs the AI and Academic Integrity Committee and serves on the AI Task Force. His first book was titled “Allegory in Enlightenment Britain: Literary Abominations” (Palgrave Macmillan, 2022). He also co-wrote a book titled “Artificial Intelligence, Real Literacy.” (2025) with Paul Matthews. He’s currently researching the rise of GenAI, process-based teaching, and alternative assessment.

Reference

Furze, Leon. “Updating the AI Assessment Scale,” https://leonfurze.com/2024/08/28/updating-the-ai-assessment-scale/

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September 10, 2025
From Policing to Pedagogy: Navigating AI’s Transformative Power – Faculty Focus

The rapid integration of generative artificial intelligence (GenAI) into higher education has reignited a familiar moral panic around academic dishonesty. While much of the immediate institutional response has centered on detection and enforcement, this reaction echoes a familiar pattern—one that treats academic dishonesty as an individual moral failure rather than a symptom of broader systemic issues (Bertram Gallant, 2008). Students, faced with overloaded schedules, mounting debt, and an unforgiving emphasis on grades, often make pragmatic decisions about where to invest their time and effort gravitating toward efficiency and certainty over exploration and risk. In this environment, the ethical clarity around “cheating” becomes murky—not because students don’t understand right from wrong, but because the structures around them reward efficiency and penalize risk. AI tools such as ChatGPT or Grammarly simply fit into this equation as time-saving devices—much like calculators, Google, or even essay mills before them. What has shifted is not the motivation, but the accessibility. A student’s use of AI is a reflection of the transactional nature of how learning is often experienced in contemporary institutions.

The proliferation of AI tools has not so much introduced a new problem as it has exposed the fragility of existing educational structures. Before we focus on what AI changes, we must confront what it reveals: that higher education has long incentivized a culture of performance over learning. Research has shown that when students perceive academic work as irrelevant, overly procedural, or divorced from their goals and identities, they disengage—regardless of whether AI is available (Ambrose et al., 2010). In this light, the question is not “How do we catch them?” but rather, “What kind of learning environments have we built that make outsourcing feel logical or even necessary?” This reframing shifts the pedagogical conversation from one of compliance and control to one of capacity-building and curiosity. If we can use this moment to ask how to foster a culture of learning that values growth over grades, process over product, and understanding over output, then AI may serve not as a threat to education but as a catalyst for its reinvention.

Why Students Turn to AI

When students turn to generative AI to complete assignments, they are not necessarily demonstrating a lack of understanding or unethical behavior—they are often responding rationally to the conditions of their academic lives. In surveys and interviews, students routinely cite overwhelming workloads, unclear expectations, time scarcity, and mental health struggles as reasons they seek shortcuts or support tools (McCabe, Treviño, & Butterfield, 2001; Pascoe, Hetrick, & Parker, 2020). AI fits seamlessly into a landscape where efficiency is often more highly rewarded than curiosity. Faced with tasks that feel repetitive, decontextualized, or performative, many students learn that their survival depends on doing just enough, not necessarily doing it deeply.

When a student uses AI to write a discussion post or summarize an article, the act is not just a form of evasion—it’s often a judgment about the value of the task itself. If the assignment doesn’t ask them to do something personally meaningful, intellectually stimulating, or clearly useful for their future goals, it becomes an obligation to manage rather than a learning opportunity to embrace. This disconnect is further amplified by the unspoken norms that teach students to prioritize grades, speed, and performance over exploration, failure, and growth (Margolis, 2001). In such a climate, using AI can feel less like cheating and more like optimization. Students aren’t necessarily trying to game the system—they’re playing the game as they perceive it was designed.

The challenge, then, is not to eliminate AI from the learning process, but to design learning experiences that make authentic engagement the easier, more meaningful choice. Rather than dismissing AI use as evidence of student laziness or misconduct, educators might ask: What does the pattern of AI usage tell us about how students experience our courses? What if the real issue isn’t student behavior but assignment design and instructional culture? By attending to these questions, we shift the focus from enforcement to empathy—from trying to control student actions to trying to understand their motivations and constraints.

Process Over Product

If generative AI makes it easier for students to produce work without deeply engaging in the learning process, it invites faculty to reconsider what their assessments are really measuring. Too often, traditional assessments prioritize polished outputs—essays, quizzes, presentations—over the messy, iterative, and uncertain processes that lead to genuine understanding. These conventional formats may reward conformity and correctness, but they do little to cultivate the flexibility, reflection, and critical thinking required in a world shaped by automation and ambiguity.

In contrast, AI offers a lens through which educators can reassess their learning goals: Are students being asked to think or to comply? To demonstrate mastery or to demonstrate growth? Increasingly, the competencies that matter most in both life and work—such as collaboration, problem framing, ethical reasoning, and the ability to adapt—are not easily captured through static, time-bound tasks (National Academies of Sciences, Engineering, and Medicine, 2018). Nor are they easily outsourced to AI. These are precisely the skills that thrive when assessment shifts from evaluating finished products to examining students’ thinking and decision-making processes along the way.

Emerging research on assessment design suggests that when students are asked to reflect on how they used AI, evaluate its limitations, compare its outputs to their own, or build on AI-generated responses with original insights, they begin to see these tools not as shortcuts but as thought partners (Mollick & Mollick, 2023; Popenici & Kerr, 2017). This approach not only fosters academic integrity, but also helps students develop the judgment, discernment, and contextual understanding that AI cannot replicate. Moreover, process-oriented assessments are more inclusive. They allow students with diverse learning styles, linguistic backgrounds, and levels of prior knowledge to demonstrate learning in multiple ways. They also encourage metacognition—students thinking about their own thinking—which is known to enhance long-term retention and transfer of learning (Ambrose et al., 2010). Rethinking assessment does not mean lowering standards; it means realigning them with the kinds of learning we claim to value. When assessments emphasize authentic engagement over mere output, they become more resilient to automation—and more relevant to the lives students are preparing to lead.

Disciplinary Innovation

While discussions about AI in education often remain abstract or policy-focused, meaningful change is already taking root within disciplines as faculty reimagine how to integrate AI into the logic of their fields. Rather than banning its use outright, many instructors are embedding AI into course design in ways that mirror authentic disciplinary practices. The goal is not just to accommodate new tools, but to cultivate students’ judgment in using them—aligning learning with the actual thinking, creating, and problem-solving expected in professional contexts.

Creative Disciplines

In design, media production, and the arts, instructors are moving beyond static portfolio reviews and embracing reflective, process-based documentation. Students are asked to keep iterative journals that detail how their ideas evolved, what role AI tools played in generating or refining concepts, and how creative decisions were made at each stage. This encourages fluency with new tools while reinforcing core disciplinary values: originality, intentionality, and critique. As Sullivan (2010) and McArthur & White (2021) argue, creative practice is inherently dialogic, and integrating AI into that dialogue allows students to develop their own voices in conversation with machine-generated suggestions.

Simulating Complexity

In fields such as public administration, business, and marketing, instructors are shifting from case-study write-ups to scenario-based simulations and role-playing exercises. These assessments ask students to evaluate data, propose interventions, justify decisions, and communicate across stakeholder perspectives. AI-generated content may be introduced as one data source among many, prompting students to critique its assumptions, identify potential biases, or integrate it with qualitative insights. This simulates real-world complexity and ethical ambiguity—core aspects of professional judgment that cannot be easily automated (Farrell, 2023; Wiggins, 2016).

Modeling Scientific Reasoning

In the sciences, instructors are piloting assignments that blend AI-assisted analysis with human critique. For example, students might be given an AI-generated interpretation of experimental data and asked to assess its validity, identify methodological flaws, or propose alternative explanations. These tasks promote skills fundamental to scientific reasoning: skepticism, data literacy, and peer review. As Wieman (2017) and Holmes, Wieman, & Bonn (2015) have shown, such approaches encourage deeper engagement with the epistemological foundations of science—what counts as evidence, how claims are justified, and why precision matters.

Building AI Fluency Across the Curriculum

To meet the demands of a world increasingly shaped by automation, higher education must move beyond treating AI as an external threat and begin cultivating AI fluency as a foundational component of 21st-century learning. AI fluency goes beyond knowing how to use tools like ChatGPT or Midjourney—it encompasses the ability to understand how AI systems work, critically evaluate their outputs, use them ethically, and reflect on their broader social and disciplinary implications (Long & Magerko, 2020; Luckin et al., 2016).

Integrating AI fluency into the curriculum means designing assignments that treat AI not as a banned shortcut or unexamined assistant, but as a subject of inquiry. Students should be taught to interrogate the design of AI tools, including how they are trained, what kinds of data they rely on, and how they reflect or reinforce existing social biases. Courses across disciplines can include assignments that ask students to compare AI-generated outputs with human-created work, evaluate their quality and assumptions, or explore how different prompts lead to different results. These tasks cultivate what Holmes, Bialik, & Fadel (2019) describe as “cognitive partnership”—the ability to think with, through, and about technology, rather than simply accepting its outputs.

Building AI fluency involves normalizing reflective practice. Students should be encouraged to articulate how and why they used AI tools in the completion of an assignment: What choices did they make? What did the AI get right—or wrong? How did it shape their thinking? This kind of metacognitive work not only deepens learning, but also demystifies AI as a black box and empowers students to use it responsibly (Mollick & Mollick, 2023).

Crucially, AI fluency should not be siloed within STEM or computer science. Every field—from history to healthcare, literature to law—will be transformed by AI in some way, and students in every discipline deserve the opportunity to engage critically with those transformations. Developing AI fluency as a form of literacy positions students to become not just competent users of technology, but thoughtful citizens capable of shaping its role in society.

The task before educators is not merely to regulate AI use, but to reimagine what it means to be educated in an age of intelligent machines. By embedding AI fluency into course outcomes, assessment practices, and institutional values, higher education can help students navigate this new landscape with agency, insight, and integrity.

The text was generated in collaboration with Gemini (2.5 Flash), Google’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

References

Ambrose, Susan A., Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman. How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco: Jossey-Bass, 2010.

Bertram Gallant, Tricia. Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative. San Francisco: Jossey-Bass, 2008.

Farrell, Paul. “Integrating AI into Public Administration Education: Risks and Rewards.” Journal of Public Affairs Education 29, no. 1 (2023): 45–58.

Holmes, Wayne, Maya Bialik, and Charles Fadel. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019.

Holmes, N. G., Carl E. Wieman, and Doug A. Bonn. “Teaching Critical Thinking.” Proceedings of the National Academy of Sciences 112, no. 36 (2015): 11199–204.

Long, David, and Brian Magerko. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. New York: ACM, 2020.

Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. Intelligence Unleashed: An Argument for AI in Education. London: Pearson, 2016.

Margolis, Eric. The Hidden Curriculum in Higher Education. New York: Routledge, 2001.

McArthur, Janine A., and Betty White. “Redesigning Creativity: AI, Agency, and Reflective Practice in the Visual Arts.” International Journal of Art & Design Education 40, no. 2 (2021): 437–51.

McCabe, Donald L., Linda Klebe Treviño, and Kenneth D. Butterfield. “Cheating in Academic Institutions: A Decade of Research.” Ethics & Behavior 11, no. 3 (2001): 219–32.

Mollick, Ethan, and Lilach Mollick. “Assigning AI: Seven Approaches for Students with Prompts.” Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed July 8, 2025).

National Academies of Sciences, Engineering, and Medicine. How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: The National Academies Press, 2018.

Pascoe, Michaela C., Rosanna M. Hetrick, and Alexandra G. Parker. “The Impact of Stress on Students in Secondary School and Higher Education.” International Journal of Adolescence and Youth 25, no. 1 (2020): 104–12.

Popenici, Stefan C., and Sharon Kerr. “Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.” Research and Practice in Technology Enhanced Learning 12, no. 1 (2017): 1–13.

Sullivan, Graeme. Art Practice as Research: Inquiry in the Visual Arts. Thousand Oaks, CA: SAGE Publications, 2010.

Wieman, Carl E. “The Similarities between Research in Education and Research in the Hard Sciences.” Educational Researcher 46, no. 6 (2017): 319–29.

Wiggins, Grant. “A True Test: Toward More Authentic and Equitable Assessment.” Educational Leadership 70, no. 6 (2016): 28–33.

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September 5, 2025
From Policing to Pedagogy: Navigating AI’s Transformative Power – Faculty Focus

The rapid integration of generative artificial intelligence (GenAI) into higher education has reignited a familiar moral panic around academic dishonesty. While much of the immediate institutional response has centered on detection and enforcement, this reaction echoes a familiar pattern—one that treats academic dishonesty as an individual moral failure rather than a symptom of broader systemic issues (Bertram Gallant, 2008). Students, faced with overloaded schedules, mounting debt, and an unforgiving emphasis on grades, often make pragmatic decisions about where to invest their time and effort gravitating toward efficiency and certainty over exploration and risk. In this environment, the ethical clarity around “cheating” becomes murky—not because students don’t understand right from wrong, but because the structures around them reward efficiency and penalize risk. AI tools such as ChatGPT or Grammarly simply fit into this equation as time-saving devices—much like calculators, Google, or even essay mills before them. What has shifted is not the motivation, but the accessibility. A student’s use of AI is a reflection of the transactional nature of how learning is often experienced in contemporary institutions.

The proliferation of AI tools has not so much introduced a new problem as it has exposed the fragility of existing educational structures. Before we focus on what AI changes, we must confront what it reveals: that higher education has long incentivized a culture of performance over learning. Research has shown that when students perceive academic work as irrelevant, overly procedural, or divorced from their goals and identities, they disengage—regardless of whether AI is available (Ambrose et al., 2010). In this light, the question is not “How do we catch them?” but rather, “What kind of learning environments have we built that make outsourcing feel logical or even necessary?” This reframing shifts the pedagogical conversation from one of compliance and control to one of capacity-building and curiosity. If we can use this moment to ask how to foster a culture of learning that values growth over grades, process over product, and understanding over output, then AI may serve not as a threat to education but as a catalyst for its reinvention.

Why Students Turn to AI

When students turn to generative AI to complete assignments, they are not necessarily demonstrating a lack of understanding or unethical behavior—they are often responding rationally to the conditions of their academic lives. In surveys and interviews, students routinely cite overwhelming workloads, unclear expectations, time scarcity, and mental health struggles as reasons they seek shortcuts or support tools (McCabe, Treviño, & Butterfield, 2001; Pascoe, Hetrick, & Parker, 2020). AI fits seamlessly into a landscape where efficiency is often more highly rewarded than curiosity. Faced with tasks that feel repetitive, decontextualized, or performative, many students learn that their survival depends on doing just enough, not necessarily doing it deeply.

When a student uses AI to write a discussion post or summarize an article, the act is not just a form of evasion—it’s often a judgment about the value of the task itself. If the assignment doesn’t ask them to do something personally meaningful, intellectually stimulating, or clearly useful for their future goals, it becomes an obligation to manage rather than a learning opportunity to embrace. This disconnect is further amplified by the unspoken norms that teach students to prioritize grades, speed, and performance over exploration, failure, and growth (Margolis, 2001). In such a climate, using AI can feel less like cheating and more like optimization. Students aren’t necessarily trying to game the system—they’re playing the game as they perceive it was designed.

The challenge, then, is not to eliminate AI from the learning process, but to design learning experiences that make authentic engagement the easier, more meaningful choice. Rather than dismissing AI use as evidence of student laziness or misconduct, educators might ask: What does the pattern of AI usage tell us about how students experience our courses? What if the real issue isn’t student behavior but assignment design and instructional culture? By attending to these questions, we shift the focus from enforcement to empathy—from trying to control student actions to trying to understand their motivations and constraints.

Process Over Product

If generative AI makes it easier for students to produce work without deeply engaging in the learning process, it invites faculty to reconsider what their assessments are really measuring. Too often, traditional assessments prioritize polished outputs—essays, quizzes, presentations—over the messy, iterative, and uncertain processes that lead to genuine understanding. These conventional formats may reward conformity and correctness, but they do little to cultivate the flexibility, reflection, and critical thinking required in a world shaped by automation and ambiguity.

In contrast, AI offers a lens through which educators can reassess their learning goals: Are students being asked to think or to comply? To demonstrate mastery or to demonstrate growth? Increasingly, the competencies that matter most in both life and work—such as collaboration, problem framing, ethical reasoning, and the ability to adapt—are not easily captured through static, time-bound tasks (National Academies of Sciences, Engineering, and Medicine, 2018). Nor are they easily outsourced to AI. These are precisely the skills that thrive when assessment shifts from evaluating finished products to examining students’ thinking and decision-making processes along the way.

Emerging research on assessment design suggests that when students are asked to reflect on how they used AI, evaluate its limitations, compare its outputs to their own, or build on AI-generated responses with original insights, they begin to see these tools not as shortcuts but as thought partners (Mollick & Mollick, 2023; Popenici & Kerr, 2017). This approach not only fosters academic integrity, but also helps students develop the judgment, discernment, and contextual understanding that AI cannot replicate. Moreover, process-oriented assessments are more inclusive. They allow students with diverse learning styles, linguistic backgrounds, and levels of prior knowledge to demonstrate learning in multiple ways. They also encourage metacognition—students thinking about their own thinking—which is known to enhance long-term retention and transfer of learning (Ambrose et al., 2010). Rethinking assessment does not mean lowering standards; it means realigning them with the kinds of learning we claim to value. When assessments emphasize authentic engagement over mere output, they become more resilient to automation—and more relevant to the lives students are preparing to lead.

Disciplinary Innovation

While discussions about AI in education often remain abstract or policy-focused, meaningful change is already taking root within disciplines as faculty reimagine how to integrate AI into the logic of their fields. Rather than banning its use outright, many instructors are embedding AI into course design in ways that mirror authentic disciplinary practices. The goal is not just to accommodate new tools, but to cultivate students’ judgment in using them—aligning learning with the actual thinking, creating, and problem-solving expected in professional contexts.

Creative Disciplines

In design, media production, and the arts, instructors are moving beyond static portfolio reviews and embracing reflective, process-based documentation. Students are asked to keep iterative journals that detail how their ideas evolved, what role AI tools played in generating or refining concepts, and how creative decisions were made at each stage. This encourages fluency with new tools while reinforcing core disciplinary values: originality, intentionality, and critique. As Sullivan (2010) and McArthur & White (2021) argue, creative practice is inherently dialogic, and integrating AI into that dialogue allows students to develop their own voices in conversation with machine-generated suggestions.

Simulating Complexity

In fields such as public administration, business, and marketing, instructors are shifting from case-study write-ups to scenario-based simulations and role-playing exercises. These assessments ask students to evaluate data, propose interventions, justify decisions, and communicate across stakeholder perspectives. AI-generated content may be introduced as one data source among many, prompting students to critique its assumptions, identify potential biases, or integrate it with qualitative insights. This simulates real-world complexity and ethical ambiguity—core aspects of professional judgment that cannot be easily automated (Farrell, 2023; Wiggins, 2016).

Modeling Scientific Reasoning

In the sciences, instructors are piloting assignments that blend AI-assisted analysis with human critique. For example, students might be given an AI-generated interpretation of experimental data and asked to assess its validity, identify methodological flaws, or propose alternative explanations. These tasks promote skills fundamental to scientific reasoning: skepticism, data literacy, and peer review. As Wieman (2017) and Holmes, Wieman, & Bonn (2015) have shown, such approaches encourage deeper engagement with the epistemological foundations of science—what counts as evidence, how claims are justified, and why precision matters.

Building AI Fluency Across the Curriculum

To meet the demands of a world increasingly shaped by automation, higher education must move beyond treating AI as an external threat and begin cultivating AI fluency as a foundational component of 21st-century learning. AI fluency goes beyond knowing how to use tools like ChatGPT or Midjourney—it encompasses the ability to understand how AI systems work, critically evaluate their outputs, use them ethically, and reflect on their broader social and disciplinary implications (Long & Magerko, 2020; Luckin et al., 2016).

Integrating AI fluency into the curriculum means designing assignments that treat AI not as a banned shortcut or unexamined assistant, but as a subject of inquiry. Students should be taught to interrogate the design of AI tools, including how they are trained, what kinds of data they rely on, and how they reflect or reinforce existing social biases. Courses across disciplines can include assignments that ask students to compare AI-generated outputs with human-created work, evaluate their quality and assumptions, or explore how different prompts lead to different results. These tasks cultivate what Holmes, Bialik, & Fadel (2019) describe as “cognitive partnership”—the ability to think with, through, and about technology, rather than simply accepting its outputs.

Building AI fluency involves normalizing reflective practice. Students should be encouraged to articulate how and why they used AI tools in the completion of an assignment: What choices did they make? What did the AI get right—or wrong? How did it shape their thinking? This kind of metacognitive work not only deepens learning, but also demystifies AI as a black box and empowers students to use it responsibly (Mollick & Mollick, 2023).

Crucially, AI fluency should not be siloed within STEM or computer science. Every field—from history to healthcare, literature to law—will be transformed by AI in some way, and students in every discipline deserve the opportunity to engage critically with those transformations. Developing AI fluency as a form of literacy positions students to become not just competent users of technology, but thoughtful citizens capable of shaping its role in society.

The task before educators is not merely to regulate AI use, but to reimagine what it means to be educated in an age of intelligent machines. By embedding AI fluency into course outcomes, assessment practices, and institutional values, higher education can help students navigate this new landscape with agency, insight, and integrity.

The text was generated in collaboration with Gemini (2.5 Flash), Google’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

References

Ambrose, Susan A., Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman. How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco: Jossey-Bass, 2010.

Bertram Gallant, Tricia. Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative. San Francisco: Jossey-Bass, 2008.

Farrell, Paul. “Integrating AI into Public Administration Education: Risks and Rewards.” Journal of Public Affairs Education 29, no. 1 (2023): 45–58.

Holmes, Wayne, Maya Bialik, and Charles Fadel. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019.

Holmes, N. G., Carl E. Wieman, and Doug A. Bonn. “Teaching Critical Thinking.” Proceedings of the National Academy of Sciences 112, no. 36 (2015): 11199–204.

Long, David, and Brian Magerko. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. New York: ACM, 2020.

Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. Intelligence Unleashed: An Argument for AI in Education. London: Pearson, 2016.

Margolis, Eric. The Hidden Curriculum in Higher Education. New York: Routledge, 2001.

McArthur, Janine A., and Betty White. “Redesigning Creativity: AI, Agency, and Reflective Practice in the Visual Arts.” International Journal of Art & Design Education 40, no. 2 (2021): 437–51.

McCabe, Donald L., Linda Klebe Treviño, and Kenneth D. Butterfield. “Cheating in Academic Institutions: A Decade of Research.” Ethics & Behavior 11, no. 3 (2001): 219–32.

Mollick, Ethan, and Lilach Mollick. “Assigning AI: Seven Approaches for Students with Prompts.” Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed July 8, 2025).

National Academies of Sciences, Engineering, and Medicine. How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: The National Academies Press, 2018.

Pascoe, Michaela C., Rosanna M. Hetrick, and Alexandra G. Parker. “The Impact of Stress on Students in Secondary School and Higher Education.” International Journal of Adolescence and Youth 25, no. 1 (2020): 104–12.

Popenici, Stefan C., and Sharon Kerr. “Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.” Research and Practice in Technology Enhanced Learning 12, no. 1 (2017): 1–13.

Sullivan, Graeme. Art Practice as Research: Inquiry in the Visual Arts. Thousand Oaks, CA: SAGE Publications, 2010.

Wieman, Carl E. “The Similarities between Research in Education and Research in the Hard Sciences.” Educational Researcher 46, no. 6 (2017): 319–29.

Wiggins, Grant. “A True Test: Toward More Authentic and Equitable Assessment.” Educational Leadership 70, no. 6 (2016): 28–33.

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September 5, 2025
A Values-Based Approach to Using Gen AI – Faculty Focus

As a learning designer in a centralized teaching and learning unit, it’s my job to explore new technologies, particularly AI. But I’ll admit, I didn’t jump right in. As Generative AI (GenAI, a subset of AI that can produce content, ideas, and data by learning from existing data and patterns) has become prevalent, so have a wide range of concerns about its usage: authenticity, critical thinking, bias, representation, plagiarism, and sustainability, to name a few.

For a while, I felt paralyzed. How am I supposed to make decisions about this emerging technology that I don’t fully understand? But scholars like Maha Bali reminded me that we need to engage with AI in order to discuss its implications and influence its outcomes. As leaders, educators, users, and creators, we have choices. So, here’s how I have been thinking through when and how I’m going to use GenAI in a way that aligns with my values.

I value originality.

I create original work as a learning designer, as well as personally as a writer and artist, and I don’t want GenAI to train on my work. If I use GenAI, I will use a proprietary tool from my university (e.g., CoPilot), a tool with a paid subscription (like ChatGPT Plus), or another tool that won’t use my work.

I value transparency.

Working in digital learning, I am a partner on academic content. I respect my instructors’ hard-earned expertise and original work, and I won’t use their work with GenAI tools without their permission. In addition, if instructors’ content appears on platforms that use GenAI, I will inform them where and how it is used. As GenAI use increases in our course development processes, I will discuss the advantages and disadvantages of using specific tools, and we will work together to decide whether and how GenAI will be applied.

I value collaboration.

I work with amazing instructors to create online courses and other digital learning experiences. With a team of experts, I guide instructors through the process of brainstorming, outlining, and developing academic materials that we organize into a guided, accessible experience. My teammates and I bring a broad range of skills to this complex work, and we collaborate to create unique and valuable learning experiences. GenAI can help us with parts of this process, but I won’t use GenAI to replace our work with material that lacks the rich human experience and expertise that informs effective teaching and learning.

I value scholarship.

Higher education centers on the exchange, synthesis, and development of ideas. We build on others’ work while crediting them for their contributions. We do this to support our arguments, demonstrate our scholarship, and contribute to an ongoing conversation. While I can use GenAI for research, I can’t depend on it to accurately quote or cite others. Therefore, I won’t assume what GenAI writes or cites is accurate, and I will do my own research. In addition, if I use AI in my work (or in collaborative work), I will cite it.

I value sustainability.

Estimates vary on how much energy GenAI uses, and it depends on what you’re using it for. We do know that simple text queries don’t use as much energy as generating images or video requires. Is it worth using GenAI to create an image of your dog as an astronaut, or a dress composed of hundreds of kittens? As much as I love cute animals, I will minimize using GenAI to create images or videos, particularly for nonessential tasks. Additionally, I will weigh the impact of saving significant time and energy on essential tasks against the potential sustainability costs.

I value critical thinking.

I don’t want the easy answer. I want the complexities, the nuance, the human experience that makes an article or study or creative work unique. I won’t rely on GenAI to create something simplistic, biased, or inaccurate. If I use GenAI, I will use it to generate ideas, answer simple questions, or create an initial outline or template. From there, I will edit and rewrite what it provides, checking for bias, representation, and accuracy. Further, I will advise instructors on how to guide their learners in taking a similar approach.

I’m far from an expert in GenAI, and it’s a moving target. But deciding how I will engage with it has given me some peace of mind in a chaotic, ever-changing world. My best wishes on your own AI journey.

Heather Hans, MLIS is a senior learning designer at Duke University. She is energized by learner-centered design, good UX, and expanding access. She enjoys travel, wildlife gardening, and pie.

References

Bali, M. (2024, June 16). What Can We Refuse in AI? Reflecting Allowed. https://blog.mahabali.me/pedagogy/critical-pedagogy/what-can-we-refuse-in-ai/

Hillier, M. (2023, February 20). Why does ChatGPT generate fake references? TECHE. https://teche.mq.edu.au/2023/02/why-does-chatgpt-generate-fake-references/

Brown University Library. (2025, February 5). Generative Artificial Intelligence: Citation and Attribution with AI Tools. LibGuides at Brown University. https://libguides.brown.edu/c.php?g=1338928&p=9868287

Nicoletti, L., & Bass, D. (2023, June 9). Humans are biased. Generative AI is even worse. Bloomberg.com. https://www.bloomberg.com/graphics/2023-generative-ai-bias/

Duke Learning Innovation & Lifetime Education. (2024, August 12). Generative AI and Teaching at Duke. Duke Learning Innovation & Lifetime Education. https://lile.duke.edu/ai-and-teaching-at-duke-2/

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June 11, 2025
Data shows growing GenAI adoption in K-12
Key points:

K-12 GenAI adoption rates have grown–but so have concerns

A new era for teachers as AI disrupts instruction

With AI coaching, a math platform helps students tackle tough concepts

For more news on GenAI, visit eSN’s AI in Education hub

Almost 3 in 5 K-12 educators (55 percent) have positive perceptions about GenAI, despite concerns and perceived risks in its adoption, according to updated data from Cengage Group’s “AI in Education” research series, which regularly evaluates AI’s impact on education.

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Tags data, Education, educators, instruction, K-12, math, news, Research, students, teachers

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April 18, 2025
Will GenAI narrow or widen the digital divide in higher education?
by Lei Fang and Xue Zhou

This blog is based on our recent publication: Zhou, X, Fang, L, & Rajaram, K (2025) ‘Exploring the digital divide among students of diverse demographic backgrounds: a survey of UK undergraduates’ Journal of Applied Learning and Teaching, 8(1).

Introduction – the widening digital divide

Our recent study (Zhou et al, 2025) surveyed 595 undergraduate students across the UK to examine the evolving digital divide across all forms of digital technologies. Although higher education is expected to narrow this divide and build students’ digital confidence, our findings revealed the opposite. We found that the gap in digital confidence and skills between widening participation (WP) and non-WP students widened progressively throughout the undergraduate journey. While students reported peak confidence in Year 2, this was followed by a notable decline in Year 3, when the digital divide became most pronounced. This drop coincides with a critical period when students begin applying their digital skills in real-world contexts, such as job applications and final-year projects.

Based on our study (Zhou et al, 2025), while universities offer a wide range of support such as laptop loans, free access to remote systems, extracurricular digital skills training, and targeted funding to WP students, WP students often do not make use of these resources. The core issue lies not in the absence of support, but in its uptake. WP students are often excluded from the peer networks and digital communities where emerging technologies are introduced, shared, and discussed. From a Connectivist perspective (Siemens, 2005), this lack of connection to digital, social, and institutional networks limits their awareness, confidence, and ability to engage meaningfully with available digital tools.

Building on these findings, this blog asks a timely question: as Generative Artificial Intelligence (GenAI) becomes embedded in higher education, will it help bridge this divide or deepen it further?

GenAI may widen the digital divide — without proper strategies

While the digital divide in higher education is already well-documented in relation to general technologies, the emergence of GenAI introduces new risks that may further widen this gap (Cachat-Rosset & Klarsfeld, 2023). This matters because students who are GenAI-literate often experience better academic performance (Sun & Zhou, 2024), making the divide not just about access but also about academic outcomes.

Unlike traditional digital tools, GenAI often demands more advanced infrastructure — including powerful devices, high-speed internet, and in many cases, paid subscriptions to unlock full functionality. WP students, who already face barriers to accessing basic digital infrastructure, are likely to be disproportionately excluded. This divide is not only student-level but also institutional. A few well-funded universities are able to subscribe to GenAI platforms such as ChatGPT, invest in specialised GenAI tools, and secure campus-wide licenses. In contrast, many institutions, particularly those under financial pressure, cannot afford such investments. These disparities risk creating a new cross-sector digital divide, where students’ access to emerging technologies depends not only on their background, but also on the resources of the university they attend.

In addition, the adoption of GenAI currently occurs primarily through informal channels via peers, online communities, or individual experimentation rather than structured teaching (Shailendra et al, 2024). WP students, who may lack access to these digital and social learning networks (Krstić et al, 2021), are therefore less likely to become aware of new GenAI tools, let alone develop the confidence and skills to use them effectively. Even when they do engage with GenAI, students may experience uncertainty, confusion, or fear about using it appropriately especially in the absence of clear guidance around academic integrity, ethical use, or institutional policy. This ambiguity can lead to increased anxiety and stress, contributing to wider concerns around mental health in GenAI learning environments.

Another concern is the risk of impersonal learning environments (Berei & Pusztai, 2022). When GenAI are implemented without inclusive design, the experience can feel detached and isolating, particularly for WP students, who often already feel marginalised. While GenAI tools may streamline administrative and learning processes, they can also weaken the sense of connection and belonging that is essential for student engagement and success.

GenAI can narrow the divide — with the right strategies

Although WP students are often excluded from digital networks, which Connectivism highlights as essential for learning (Goldie, 2016), GenAI, if used thoughtfully, can help reconnect them by offering personalised support, reducing geographic barriers, and expanding access to educational resources.

To achieve this, we propose five key strategies:
- Invest in infrastructure and access: Universities must ensure that all students have the tools to participate in the AI-enabled classroom including access to devices, core software, and free versions of widely used GenAI platforms. While there is a growing variety of GenAI tools on the market, institutions facing financial pressures must prioritise tools that are both widely used and demonstrably effective. The goal is not to adopt everything, but to ensure that all students have equitable access to the essentials.
- Rethink training with inclusion in mind: GenAI literacy training must go beyond traditional models. It should reflect Equality, Diversity and Inclusion principles recognising the different starting points students bring and offering flexible, practical formats. Micro-credentials on platforms like LinkedIn Learning or university-branded short courses can provide just-in-time, accessible learning opportunities. These resources are available anytime and from anywhere, enabling students who were previously excluded such as those in rural or under-resourced areas to access learning on their own terms.
- Build digital communities and peer networks: Social connection is a key enabler of learning (Siemens, 2005). Institutions should foster GenAI learning communities where students can exchange ideas, offer peer support, and normalise experimentation. Mental readiness is just as important as technical skill and being part of a supportive network can reduce anxiety and stigma around GenAI use.
- Design inclusive GenAI policies and ensure ongoing evaluation: Institutions must establish clear, inclusive policies around GenAI use that balance innovation with ethics (Schofield & Zhang, 2024). These policies should be communicated transparently and reviewed regularly, informed by diverse student feedback and ongoing evaluation of impact.
- Adopt a human-centred approach to GenAI integration: Following UNESCO’s human-centred approach to AI in education (UNESCO, 2024; 2025), GenAI should be used to enhance, not replace the human elements of teaching and learning. While GenAI can support personalisation and reduce administrative burdens, the presence of academic and pastoral staff remains essential. By freeing staff from routine tasks, GenAI can enable them to focus more fully on this high-impact, relational work, such as mentoring, guidance, and personalised support that WP students often benefit from most.
Conclusion

Generative AI alone will not determine the future of equity in higher education, our actions will. Without intentional, inclusive strategies, GenAI risks amplifying existing digital inequalities, further disadvantaging WP students. However, by proactively addressing access barriers, delivering inclusive and flexible training, building supportive digital communities, embedding ethical policies, and preserving meaningful human interaction, GenAI can become a powerful tool for inclusion. The digital divide doesn’t close itself; institutions must embed equity into every stage of GenAI adoption. The time to act is not once systems are already in place, it is now.

Dr Lei Fang is a Senior Lecturer in Digital Transformation at Queen Mary University of London. Her research interests include AI literacy, digital technology adoption, the application of AI in higher education, and risk management. [email protected]

Professor Xue Zhou is a Professor in AI in Business Education at the University of Leicester. Her research interests fall in the areas of digital literacy, digital technology adoption, cross-cultural adjustment and online professionalism. [email protected]

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Categories: Access and Widening Participation, Student Experience | Tags: Connectivism, digital accessabillity, digital divide, Digital inclusion, digital technologies, GenAI, generative AI, inclusion, technology, Widening Participation | Permalink.

Author: SRHE News Blog

An international learned society, concerned with supporting research and researchers into Higher Education
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April 2, 2025
From Feedback to Feedforward: Using AI-Powered Assessment Flywheel to Drive Student Competency – Faculty Focus

A defining quality of student-centred teaching is effective assessments. In recent years, the discourse around effective assessment has steered towards incorporating the “assessment for learning” strategy into curriculum development. In basic form, assessment for learning entails assessment where pre-emptive, future-oriented comments (called feedforward) are given to learners to guide improvement in future performances. This contrasts with the typical assessment arrangement where feedback is considered a product offered to the student in exchange for what they submit. However, two broad factors have historically impeded the adoption of assessment for learning with feedforward corrective actions in educational settings:

Increased workload and scalability: This approach demands creating varied, low-stakes formative assessment tasks with actionable feedforward recommendations that guide future learning. Besides, providing personalized corrective actions for each student in each low-stake test is both time and resource-intensive. Particularly, in large courses with many students, the additional burden on educators’ heavy workloads cannot be underestimated.

Student engagement and motivation: Students are reluctant to engage with formative assessment tasks if they perceive them as low-stakes with no incentive that aids their grades.

Overcoming these impediments is crucial to accelerating progress with assessment for learning. In this vein, the central idea of the assessment flywheel is to harness the power of GenAI to overcome the above challenges. In effect, this approach adds to the growing realisation of the power of GenAI for the transformation of assessments.

The Assessment Flywheel Model

Why assessment flywheel? In the field of engineering, which is my academic background, traditional methods such as lectures, tutorials, and laboratory sessions are the dominant content delivery vehicles. However, oftentimes, these activities are lecturer-led and time-bound. This thus limits students’ active knowledge consumption and self-regulated learning. Compounding the problem further when it comes time to assess our students’ learning, lecturers tend to focus predominantly on summative assessments, which are often followed by feedback of one kind or another. Notably, this prevalent approach of provisioning meaningful feedback solely on summative assessments often creates a dilemma between the traditional assessment of learning (which is backwards-looking) and the assessment for learning (which is forward-looking).

The discourse around how to incorporate assessment for learning strategy into curriculum development has permeated the literature on teaching and learning for the past several years. However, progress has been slow in adoption. Among the reasons for this is that assessment for learning demands constructing and assessing a diversity of low-stake formative assessment tasks suitable for providing “feedforward” comments that trigger corrective actions in students. In other words, the new paradigm imposes a significant burden on resource-constraint educators, especially for a large cohort of students. And this is where the idea of “assessment flywheel” comes in.

Essentially, the assessment flywheel is a GenAI-mediated assessment framework. It is a dynamic, self-reinforcing system that continuously gathers, analyzes, and applies student performance data to enhance learning outcomes. As depicted in the figure below, the flywheel model operates in a continuous cycle, and each iteration of tasks, learning and proactive feedforward comment builds momentum for deeper skill development. This contrasts with the traditional assessments that are static and episodic. More specifically, within the flywheel model, the GenAI plays a central role in generating personalized tasks, personalized feedback, and adapting learning materials. The goal is to create an automated loop where student learning accelerates over time through persistent, data-driven refinement. The model allows instructors to incorporate personalized exercises that revolve around a subject’s learning outcomes in low-stake tests. And with proper tailoring of the assessment flywheel model for low-stake tests, students will be able to instantly identify their current state of progress and get instant comments for corrective action from the GenAI-powered system.

An illustration of the assessment flywheel towards achieving the idea of “assessment for learning” using LLM/GenAI (adapted from the author’s recent publication).

Rolling Out GenAI-Driven Assessment Flywheel to Improve Learning

To be effective, one will need to take a few steps before a GenAI-driven assessment flywheel can be introduced within the academic learning spaces. First, the “assessment flywheel” system will need to be fed with learning contents of specific subjects from which question-answer pairs can be automatically generated. With proper tailoring of such a tool for low-stake tests, students will be able to instantly identify their current state of progress and get instant comments for corrective “feedforward” action from the GenAI-powered system. Second, a mechanism to minimize hallucinations (a common problem with GenAI) must be in place. Third, a structured approach that guides users of the system will need to be instituted with the rollout. For this latter point, a framework similar to Professor Gilly Salmon’s five-stage model for online learning is recommended.

Conclusion

Overall, GenAI holds concrete pedagogical prospects as a way of augmenting traditional teaching and assessment approaches to enhance gains in students’ learning. The discussed GenAI-powered assessment flywheel embodies the spirit of the military strategy of “Observe, Orient, Decide and Act” or OODA as is well-known. In the context of a personalized learning strategy, this GenAI-powered assessment flywheel approach holds the potential to contribute to deepening students’ accomplishment in competencies associated with learning objectives. It will also allow educators to provide assessments that facilitate authentic learning with minimal addition to their workload.

Dr. Khameel Mustapha holds a PhD in mechanical engineering from Nanyang Technological University (Singapore). He also holds two teaching qualifications – Postgraduate Certificate in Higher Education (PGCHE, Nottingham UK) and Graduate Certificate in Teaching and Learning (GCLT, Swinburne Australia). He is currently an Associate Professor with the Department of Mechanical Engineering, University of Nottingham (Malaysia campus). He is a Fellow of the Higher Education Academy (UK).

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March 24, 2025
How can evolving student attitudes inform institutional Gen-AI initiatives?

This HEPI blog was authored by Isabelle Bristow, Managing Director UK and Europe at Studiosity.

In a HEPI blog published almost a year ago, Student Voices on AI: Navigating Expectations and Opportunities, I reported the findings of global research Studiosity commissioned with YouGov on students’ attitudes towards artificial intelligence (AI). The intervening year would be considered a relatively small time period in a more regular higher education setting. However, given the rapid pace of change within the Gen-AI sphere, this one year is practically aeons.

We have recently commissioned a further YouGov survey to explore the motivations, emotions, and needs of over 2,200 students from 151 universities in the UK.

Below, I will cover the top five takeaways from this new round of research, but first, which students are using AI?

64% of all students have used AI tools to help with assignments or study tasks.

International student use (87%) is a staggering 27% higher than their domestic student counterparts (60%).

There’s a 21% difference between students who identify as female who said they have never used AI tools for study tasks (42%) compared with those identifying as male (21%).

Only 17% of students studying business said they have never used it, compared with 46% studying Humanities and Social Sciences.

The highest reported use is by students studying in London at 78%, and conversely, the highest non-use was reported by students studying in Scotland at 44%.

The Top Five Takeaways:

There is an 11% increase from last year in students thinking that their university is adapting fast enough to provide AI study support tools.

Following a year of global Gen-AI development and another year for institutions to adapt, students who believe their university is adjusting quickly enough remain in the minority this year at 47%, up from 36% in 2024. The remaining 53% of student respondents believe their institution has more to do.

When asked if they expect their university to offer AI support tools to students, the result is the same as last year – with 39% of students answering yes to this question. This was significantly higher for male students at 51% (up by 3% from last year) and for international students 61% (up by 4% from last year). Once again, this year, business students have the highest expectations at 58% (just 1% higher than last year). Following this, medicine (53%), nursing (48%) and STEM (46%) were more likely to respond ‘Yes’ when asked if they expect their university to provide AI tools.

Some students have concerns over academic integrity.

When asked if they felt their university should provide AI tools, students who answered’ no’ were given a free text box to explain their reasoning. Most of these responses related to academic integrity.

‘I don’t think unis support its use because it helps students plagiarise and cheat.’

‘I think AI beats the whole idea of a degree, but it can be used for grammar correction and general fluidity.’

‘Because it would be unfair and result in the student not really learning or thinking for themselves.’

Only 7% of students said they would use an AI tool for help with plagiarism or referencing (‘Ask my lecturer’ was at 30% and ‘Use a 24/7 university online writing feedback tool’ was at 21%).

Students who use AI regularly are less likely to rank ‘fear of failing’ as one of their top three study stresses

We asked all students – regardless of their AI use – of their top three reasons for feeling stressed about studying the responses were as follows:

61% of all UK students included ‘fear of failing’ in their top 3 reasons for feeling stressed about studying;

52% of all students included ‘balancing other commitments’; and

41% of all students included ‘preparing for exams and assessments’.

These statistics change when we filter by students who use AI tools to help with assignments or study tasks. Fear of failing is still the highest-ranked study stress. The percentage of respondents who rank fear of failing in their top three study stresses by AI use are as follows:

69% for those who never use AI;

62% for those who have used AI once or twice;

58% for those who have used AI a few times and;

50% for those who use AI regularly.

Looking at the main reasons students want to use the university’s AI service for support or feedback, this year, ‘confidence’ (25%) overtook ‘speed’ (16%). Female respondents, in particular, are using AI for reasons relating to confidence at 29%, compared to 20% for male students. International students valued ‘skills’ the most at 20%, significantly higher than their domestic student counterparts at 11%.

Students who feel like they belong are more likely to use AI.

We examined the correlation between students’ sense of belonging in their university community, and the amount they use AI tools to help with assignments or study tasks.

For students who feel like they belong, 67% said they have used AI tools to help with assignments or study tasks; this compares with 47% for students who do not feel like they belong.

5. Cognitive offloading (using technology to circumvent the ‘learning element’ of a task) is a top concern of academics and institutional leadership in 2025. However, student responses suggest they feel they are both learning and improving their skills when using generative tools.

When asked if they were confident they are learning as well as improving their own skills when using generative tools, students responded as follows:

12% ‘were extremely confident that they were learning and developing skills;

31% were very confident;

29% were moderately confident;

26% were moderately confident; and

Only 5% were not at all confident that this was true.

Conclusion:

Reflecting on the three years since Gen-AI’s disruptive entrance into the mainstream, the sector has now come to terms with the power, potential, and risks of Gen-AI. There is also a significantly better understanding of the importance of ensuring these tools enhance student learning rather than undermining it by offloading cognitive effort.

Leaders can look to a holistic approach to university-approved, trusted Gen-AI support, to improve student outcomes, experience and wellbeing.

—

You can download the full Annual Global Student Wellbeing Survey – UK report here.

Studiosity is a HEPI Partner. Studiosity is AI-for-Learning, not corrections – to scale student success, empower educators, and improve retention with a proven 4.4x ROI, while ensuring integrity and reducing institutional risk. Studiosity delivers ethical and formative feedback at scale to over 250 institutions worldwide. With unique AI-for-Learning technology, all students can benefit from formative feedback in minutes. From their first draft to just before submission, students receive personalised feedback – including guidance on how they can demonstrably improve their own work and critical thinking skills. Actionable insight is accessible to faculty and leaders, revealing the scale of engagement with support, cohorts requiring intervention, and measurable learning progress.

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March 13, 2025

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