Some of that skepticism reflects real questions about relevance. If an English instructor has built a writing-intensive course around close reading and revision, does layering AI into that process help students or hollow out the very skills the course is designed to build? If a chemistry lab depends on students working through calculations until the procedural logic becomes second nature, does AI assistance accelerate learning or short-circuit it? These are legitimate pedagogical questions, and they deserve better answers than “AI is the future, get on board.”

So when Columbia Business School announced this fall that it had been named Poets & Quants 2025 MBA Program of the Year, in significant part for the way it has woven AI through its entire curriculum, the reaction across higher education was instructive. The dean’s quote that traveled fastest was not about competitive advantage or job placement. It was about duty.

“It would be a disservice to students to not equip them fully in the use of artificial intelligence so that when they enter or reenter the job market, they’re really well equipped to use it.”

That sentence reframes the entire debate. Note what it does not say. It does not argue that AI integration is desirable or innovative or strategically important. It frames the absence of AI integration as a failure of institutional duty. The faculty skeptic and the faculty enthusiast have been arguing about whether AI integration is good. Columbia’s leadership is arguing it is required.

What Columbia is actually doing

The specifics matter, because the institution is doing something more substantive than adding an elective and calling it AI integration. One Columbia faculty member described courses where AI appears in 22 out of 24 class sessions, not as a sidebar but as a working tool students use on real projects for real companies. Another spoke about building simple neural networks in Excel spreadsheets to demystify what AI actually does, showing students it is built from operations they already understand: addition, multiplication, and pattern recognition scaled up.

The pedagogical move worth noting is the cross-curricular one. Columbia did not concentrate AI in a single course or a single concentration. Faculty across the MBA, Executive MBA, MS, and PhD programs integrated AI into existing subject matter. The result is that students encounter AI in finance class, in marketing class, in operations class, in strategy class. They learn AI the way they learn any other professional tool, by using it across multiple contexts until it stops feeling like a special topic.

This is not a model that requires every institution to look like Columbia. It is a model that suggests what AI integration looks like when an institution treats it as foundational rather than supplementary.

Why the comparison still travels

The obvious objection writes itself. Columbia serves a student population heading into management consulting, investment banking, and corporate strategy roles where AI adoption is already widespread. A regional comprehensive university preparing students for K-12 teaching, social work, and nursing serves a different population. A community college preparing students for the trades, allied health, and transfer serves a different population still. The Columbia model cannot simply be transplanted.

It does not need to be. The fiduciary argument generalizes; the implementation does not.

Consider the populations downstream of every institutional tier. Medical offices are using AI for scheduling, coding, and documentation, which means the medical assistants and health information technicians community colleges train are entering AI-mediated workplaces from day one. Public school districts are deploying AI tutoring and assessment tools, which means the teachers regional universities prepare will be expected to evaluate and integrate them in their first classrooms. Law firms are using AI for document review that paralegals once handled manually. Research labs are using AI for literature synthesis, code generation, and experimental design, which means doctoral students who never touch the tools will be at a disadvantage in their first faculty position.

The gap between Columbia’s situation and every other institution’s is narrower than it looks, because every institution is preparing students for workplaces where the tools are already present. The question is not whether to acknowledge this. The question is what an institution’s particular obligation to its students looks like in light of it.

What translates across institutional types

Three principles from the Columbia approach travel without requiring a Columbia-size budget.

First, AI is more effective as a cross-curricular competency than as a standalone course. When students encounter AI only in a single elective, they learn about it in the abstract. When they use it across multiple courses, in writing, in research, in data analysis, in project planning, they begin to understand how it actually functions as a professional tool. This does not require every course to have an AI unit. It requires faculty who choose to integrate AI to do so within assignments they already teach, rather than building something entirely new.

Second, AI literacy does not require engineering depth. Columbia’s faculty describe finding the right depth for non-technical students, enough understanding to collaborate with technical teams and make informed decisions without requiring calculus or programming. The same posture works at any institution. Accessible tools like spreadsheets, visual interfaces, and structured prompt-writing activities can help students understand how models work, where they fail, and why human judgment remains essential. The goal is not to produce AI developers. It is to produce graduates who can work confidently alongside AI and the people who build it.

Third, every AI activity should include an ethics and limitations component. Columbia pairs hands-on AI use with explicit discussions about bias, data privacy, disclosure, and the boundaries of machine-generated output. This matters even more at institutions whose students represent communities most directly affected by AI’s risks. Teaching students to use AI without teaching them to question it would be irresponsible. Faculty who are already skeptical of AI hype are, frankly, well positioned to teach that critical perspective.

The asymmetric risk

Nobody is asking faculty to abandon methods that work. A writing instructor who believes students need to struggle through drafts without AI assistance may be exactly right for their course and their students. But even that instructor might find value in a single assignment where students compare their own draft against an AI-generated one and analyze the differences. The point is not to replace professional judgment with a technology mandate. It is to make sure students have encountered AI in a guided, critical setting before they encounter it unsupervised in a workplace.

Here is where the fiduciary frame matters. The risk of moving too fast on AI integration is pedagogical incoherence and the hollowing-out of skills that took generations to develop. The risk of moving too slow is the student who freezes the first time an employer asks them to review AI-generated content, the graduate who does not get the callback because a competing candidate listed AI tools on their resume, the medical assistant who cannot evaluate an AI scheduling recommendation, the new teacher who cannot tell when an AI-generated lesson plan is wrong.

Both risks are real. Neither can be wished away. What Columbia’s framing makes clear is that the second risk is just as much a failure of institutional duty as the first, and it has been treated as if it were not. The institutions taking AI integration seriously are not necessarily right about every implementation choice they have made. They are right that the question of whether to do nothing is no longer open.

We do not need every answer before we begin. We do not need unanimous enthusiasm. We need enough faculty willing to try something small, share what they learn, and let the results speak. That has always been how good teaching evolves.

Watch the full video:

Dr. Szymon Machajewski is the author of The Learn-It-All Educator: A Guidebook for Training Brains, Not Replacing Them with AI. The free OER edition, Chapters 1 through 4, is available on Zenodo under a CC BY 4.0 license. The Complete Edition is on Amazon. For institutional bulk pricing and faculty common-read inquiries, contact press@dataii.com.

Inquiry in Action: Using AI to Reimagine Learning and Teaching, edited by Associate Professor Rachel Fitzgerald and published in 2025 under a Creative Commons license, collects twelve AI-integration case studies from UQ faculty across health, science, dentistry, dietetics, economics, business, computer science, and creative inquiry. Each chapter has a DOI. The collection grew out of a 2024 Teaching Innovation Grant. The full text is freely available at uq.pressbooks.pub/inquiry-in-action.

For community college faculty who have been told for two years that AI integration is “complicated and emerging,” the collection is a quiet rebuke. It is not complicated. It is happening. The question is whether your institution is documenting what works, or repeating the same arguments at the next senate meeting.

The reframe at the center of the collection

Most institutional AI conversations assume the technology is an answer generator. Policy debates then become whether to permit, restrict, or ban its use in producing answers. This framing is what makes those debates feel unresolvable.

The most provocative chapter in Inquiry in Action, by Sean Mitchell at the UQ Business School, dispenses with the framing entirely. AI, Mitchell argues, can be a verifier, a coach, or a facilitator. None of these roles is “answer generator.” Each unlocks a different pedagogical use case.

His three implementations make the abstract concrete. BBOP is an AI tool that verifies assessment choices against course rules without generating any analysis itself. NotABot embeds discipline-specific language feedback into online modules at scale. The Lighthouse is a Socratic reflection platform that surfaces metacognitive insight rather than content.

This pattern recurs across the collection, even where the authors do not name it. Once the question shifts from “should AI produce student work” to “what role should AI play in the learning process,” the policy debate becomes manageable. The answer is no longer binary.

Three thematic clusters faculty can use

These twelve case studies sort cleanly into three groups, each addressing a different design problem community college faculty already face.

Moving students from anxiety to agency. Naghmeh Taptamat and colleagues describe co-designing an “AI Essential Guide” for science students at UQ, built specifically to address the anxiety students feel about academic misconduct risk. Their intervention is not a rules document. It is scenario-based learning, developed in partnership with students, grounded in self-regulated learning theory.

A similar pattern recurs in the first-year health literacy chapter, which structures AI literacy tasks around reflective use and transparent disclosure, and in the creative inquiry chapter, which positions AI as a co-participant rather than a replacement. The common move is to give students explicit permission to use AI in a defined way, then teach them to declare that use openly. Anxiety drops. Engagement rises. Misconduct, by the case studies’ account, does not increase.

Building progression models. Several chapters describe scaffolded AI integration that progresses from dependency through collaboration to genuine partnership. Reihaneh Bidar’s information systems chapter traces the arc most clearly: students begin with manual work in Google Colab notebooks, then move to AI-assisted versions, with explicit prompt engineering and validation protocols taught along the way. The economics chapter shows similar staging in research proposal development. The dietetics chapter introduces voice-based and text-based AI simulation in carefully sequenced phases to manage cognitive load.

For community college faculty teaching multi-section sequences (composition, math, intro health), this progression model is more useful than any single assignment. It tells you what week three should look like differently from week ten.

Embedding AI in discipline-specific simulation. The dentistry chapter uses AI to generate patient case scenarios and accessibility-focused content modalities. In dietetics, voice-based simulation prepares students for client-centered counselling. A web and mobile development chapter applies the TPACK model to integrate AI across project-based learning and secure code review. Each is an example of using AI to expand what students can practice before they reach a real client, patient, or production system.

This is the cluster with the highest immediate transfer to community college health and trades programs. Nursing simulation is expensive. Dental hygiene clinic time is limited. AI-generated case scenarios, scaffolded into existing simulation curricula, can multiply practice opportunities without adding manikin hours.

What the model itself demonstrates

Beyond the case studies, the Inquiry in Action project models something community colleges should pay attention to. UQ funded a teaching innovation grant. Faculty across disciplines opted in. A faculty editor coordinated the work. UQ’s library open textbook program published the result. Twelve case studies, peer-reviewed within the community of practice, each assigned a DOI, all available free under CC BY-NC 4.0.

From grant award to publication, the entire project took roughly eighteen months. It is not a research program. It is institutional documentation done deliberately.

Most community colleges have the inputs already. Faculty are running AI experiments in their classrooms whether the institution knows it or not. Library publishing infrastructure exists or can be reached through a consortium. CTLs have the convening capacity. What is usually missing is the grant funding to make the work visible and a faculty editor willing to coordinate it.

Lansing Community College, the University of Illinois Chicago, and similar institutions could run a version of Inquiry in Action in a single academic year. The product would be a CC-licensed collection of AI integration case studies from their own faculty, with their own students, in their own disciplines. Every subsequent senate AI discussion would have local evidence to reference instead of recycled debates about hypothetical risks.

What this changes for the AI conversation

The Fitzgerald collection makes one move that is worth borrowing directly. It treats AI integration as an empirical question rather than a normative one. The case studies do not argue whether AI should be allowed. They report what happened when faculty integrated it, what worked, what did not, and what the next iteration should change.

That framing is what community college AI policy is missing. Arguments have become circular because they rest on hypotheticals. The Fitzgerald model proposes a way out: stop arguing about what might happen, document what is happening, and let the next round of policy decisions rest on evidence from your own faculty and students.

The collection is free. The DOI structure makes individual chapters citable. The CC license permits adaptation. The institutional model is replicable. For community college leadership teams looking for the next concrete step on AI, this is one of the clearest playbooks published in 2025.

Inquiry in Action: Using AI to Reimagine Learning and Teaching: Case Studies from the Frontline of Higher Education Practice, edited by Rachel Fitzgerald, is available at uq.pressbooks.pub/inquiry-in-action. DOI: 10.14264/a18431b. CC BY-NC 4.0.

Generative AI, or GenAI, has become a daily tool for many students, including me. From brainstorming ideas to writing code, AI tools like ChatGPT and Gemini have changed how I study and complete projects. In this post, I’ll share how I use these tools, what I’ve learned, and what I hope my school will do to prepare students for an AI-driven future.

I first started using ChatGPT last semester when I was struggling with my HTML and CSS assignments. Instead of searching through dozens of web pages or going to a tutor at my school, I could ask specific questions and get clear examples that helped me fix my code. It felt like having a patient tutor available anytime. I also used AI for brainstorming topics and editing essays. However, I make sure not to copy answers, I treat AI as a guide, not a replacement for learning.

One of the biggest benefits of GenAI is efficiency. I can test ideas quickly, view different code examples, and better understand my mistakes. It also helps me write more clearly and check grammar before submitting assignments. For students who speak English as a second language, AI can be especially useful for improving writing and building confidence.

While AI is helpful, it can also make learning too easy. Some students may rely on it to do all the work, which limits real understanding. I’ve also noticed that AI sometimes gives incorrect or outdated information. For that reason, I always double-check the answers I get from AI tools. I believe students should be taught how to use AI ethically, not as a shortcut, but as a learning tool.

I hope my school continues to discuss and teach AI literacy in every course. Learning how to use AI responsibly will help us succeed in the future workplace, where these tools will be everywhere. GenAI is not perfect, but when used wisely, it can make education more accessible and creative. As students, our job is to use it to learn, not to replace learning.

— Guest Student Contributor

The University of Leeds has filled that gap. Learning With AI: A Student Edited Collection is a student-led open Pressbook, edited by a rotating team of Leeds students, published in September 2025 under a Creative Commons license. Twenty-eight contributions from undergraduates and postgraduates across business, law, accounting, philosophy, computer science, childhood studies, education, and leadership. Each entry is short, signed, and written in the contributor’s own register. The collection is available free at leeds.pressbooks.pub/learningwithai, with submissions open through June 2028.

For instructors who are still arguing about whether students should use AI, the collection is a useful corrective. They already are. The interesting question is what they are using it for.

Five patterns that emerge from the student voices

AI as concept clarifier. Oliver Fletcher, an undergraduate business student, describes using ChatGPT and Copilot to gain “a quicker understanding of economic concepts as well as concepts within law that I otherwise struggled to grasp.” The pattern recurs across the collection. Md Sahid Hossain, a postgraduate accounting researcher, makes the same point about financial ratios, investment strategies, and tax regulations. These students are not asking AI to write their assignments. They are asking it to explain concepts the textbook explained once, in one register, and move on.

For community college faculty, this is the most consequential pattern in the collection. Our students arrive with more variability in prior preparation than students at four-year universities, and the standard textbook explanation often misses the student who needed it most. AI’s capacity to re-explain a concept in five different registers, on demand, without judgment, is doing what office hours could do if office hours scaled.

AI as practice partner. An anonymous philosophy undergraduate from the United States describes test anxiety with unusual honesty: “When I walk into that room, all of a sudden, the knowledge I thought I had has evaporated and it feels as if I must figure out the problems from scratch.” The intervention is not memorization. It is generating practice questions on the same topic, prompted to come at the material from different angles. “The questions come at you differently every time,” the student writes, “and that really helps increase your understanding of the topic with nuance.”

This is the Cognitive Gym at work, even though the student does not use the term. The exercise builds neural pathways through productive struggle, not through outsourcing.

AI as language equalizer. Asa Ismia Bunga Aisyahrani, a postgraduate childhood studies student, makes a claim worth quoting in full: “I think this tool fosters equality in learning, enabling non-native speakers to write correctly without grammatical errors and demonstrating that language barriers do not hold us back.” Noviachri Imroatul Sadiyah, a postgraduate education student, calls AI “a safe and responsive space to check grammar, test ideas, and improve the flow of my writing without feeling judged.”

The language-equity argument is the one academic integrity policies most consistently fail to address. A native English speaker spends thirty minutes polishing a paragraph; a non-native speaker, doing the same intellectual work, takes three hours and still produces prose that gets marked down for fluency. AI compresses that gap. Policies that ban the compression are, whether intentionally or not, defending an artifact of the language barrier.

AI as Socratic interlocutor. Chrissi Nerantzi, returning to economics after years away, describes her postgraduate experience: “I wasn’t looking for answers, but to discuss my questions with somebody(?) who could help me to make sense of terminology.” She names her Copilot instance “Sofia” and uses it the way an earlier generation of returning students might have used a tutor, if a tutor had been available at 10 p.m. on a Sunday. Smriti Umesh, a computer science undergraduate, calls AI “a kind of ‘mind palace’” that helped her shift “from passive to active learning.”

Both descriptions sit closer to the Socratic ideal than to anything academic integrity discourse usually associates with AI. The student is the one asking the questions. The student is the one deciding which answers to accept. The AI is the one being interrogated, not the one producing the final work.

AI as accessibility tool. Mofei Bai, a postgraduate education student, describes a use case rarely surfaced in faculty discussions: “I use AI tools to listen to written texts. I usually do this while resting with my eyes closed or taking a walk outside.” Imogen Kelly, also at the postgraduate level, built a custom AI tool called SimPatient to simulate clinical interactions for healthcare students, describing it as a way of “demystifying academic research, enhancing reflection, and reducing cognitive load.”

The accessibility frame matters because it shifts the question from “should AI be allowed” to “for which students is denying AI a barrier to learning.”

What the collection asks of faculty

Misha Salehuddin, a law student, writes the sentence that should sit at the top of every academic integrity policy currently in revision: “I remain conscious of the limitations of these tools and make sure all final outputs are my own, guided by academic integrity and personal responsibility.” This is what mature AI use sounds like when a student is given room to develop it. It does not sound like cheating. It also does not sound like the answer that gets generated when faculty refuse to discuss AI in class.

Three takeaways follow.

First, the students who use AI well are not the ones who are gaming the system. They are the ones whose institutions have created space for them to talk about how they use it. The Leeds project succeeds because it asked students the question directly, on the record, under their own names.

Second, faculty who want to know how students actually use AI in their courses can simply ask. The Leeds model, a low-friction submission form, a small editorial team, a Creative Commons license, is replicable at any institution with a learning innovation office and a few sponsoring administrators. Community colleges could run a version of this in a single semester.

Third, the framing of student AI use as primarily a cheating risk is empirically out of step with what students themselves report. Cheating happens. So does practice. So does language equity. So does Socratic dialogue. A policy that addresses only the first is regulating a fraction of the actual behavior.

The Leeds collection is now in its second editorial cycle, with submissions open through 2028. For faculty who are tired of arguing about students in the abstract, it is the most honest source on what students are doing with AI right now. The contributors signed their names. They are not asking for permission. They are documenting what is already working, and what is not, and offering it to anyone who will read.

Learning With AI: A Student Edited Collection is freely available at leeds.pressbooks.pub/learningwithai under a CC BY-NC-SA 4.0 license. DOI: 10.63560/lwai99015. Edited by Olivia Davies and Amani Ahsan (2024/25), with Zachary Farouk Chai and Mark Grattan joining for 2025/26. Project lead: Dr. Chrissi Nerantzi, School of Education, University of Leeds.

As a guide for the instructor walking into Math 101 tomorrow with thirty students and a syllabus to defend, it has almost nothing.

That gap is the subject of this essay. The AAUP issue describes the disease with care and precision. It does not prescribe a treatment. Community college instructors, who teach more sections, advise more students, and have less institutional cover than their four-year counterparts, are the ones who most need both.

What the AAUP issue does well

Five contributions are worth singling out.

Daniel Greene’s “What Does AI Do?” applies Baumol’s cost disease to the AI adoption pressure on universities. Teaching is labor-intensive and resists automation, which is exactly why administrators feel constant economic pressure to substitute technology for instruction. Understanding this is the first step in pushing back on AI deployments that solve a budget problem rather than a pedagogical one.

Heather Hax’s “AI and Critical Thinking” puts a name on the cognitive cost. Drawing on the Kosmyna et al. (2025) MIT study, Hax describes how heavy LLM use during writing tasks produces measurably weaker neural connectivity and more homogeneous outputs. She calls the result cognitive debt, and the metaphor is exactly right. Students who outsource the thinking are borrowing against capacity they have not yet built.

Troy Swanson’s “Keeping Humans in the Loop” examines Illinois H.B. 1859, the August 2025 law that prohibits AI from being the sole source of instruction in Illinois community colleges. The legislation is short, but its implications are not. Whatever happens at the federal level, Illinois has just legally affirmed that credentialed human instructors are the foundation of higher education in the state.

Martha Lincoln and Martha Kenney examine the California State University system’s $16.9 million ChatGPT Edu rollout. The case study is a cautionary tale about top-down AI deployment without faculty consultation or evidence of pedagogical efficacy. It is also a preview of what is coming to many other state systems.

Debra Rosenthal’s “Teaching Climate Change in the Age of ChatGPT” raises the contradiction that climate-focused courses cannot ignore. An LLM query consumes meaningfully more electricity than a standard web search. Faculty teaching sustainability are now in the position of either modeling responsible AI use or quietly contradicting their own syllabus.

Read together, the eleven articles do something the field has needed for two years. They place AI adoption inside the political economy of higher education, where it belongs.

What the AAUP issue does not do

It does not give the instructor an assignment.

The articles describe what to watch for, what to resist, and what to negotiate at the senate level. None of them answer the question that arrives in every faculty member’s inbox by the third week of the semester: I just caught three submissions that look AI-generated. What do I do tomorrow?

The vocabulary for that question exists. It is just not in this issue of Academe.

The treatment side

The Learn-It-All Educator was written for exactly this gap. Where AAUP supplies the diagnosis, the guidebook supplies the operational frameworks. Three of them map directly onto the concerns raised in the Spring 2026 issue.

Cognitive Triage (FLUFF vs. SPARK) addresses the cognitive debt Hax describes. The framework gives faculty a sorting heuristic for assignment design. Work with capped cognitive payoff, such as formatting and routine drafting, is FLUFF and can be safely delegated to AI. Work with uncapped payoff, such as verifying claims against primary sources or constructing an argument, is SPARK and must remain with the student. The framework does not ban AI. It tells the student which parts of the assignment AI is allowed to touch, and why.

The Cognitive Gym answers Hax’s concern about frictionless learning more directly. Chapter 3 treats the classroom as a site of productive struggle, with the Verification Protocol as its anchor exercise. Students receive an AI-generated draft and audit it across five steps: source verification, claim provenance, logical consistency, counterevidence search, and revision. The assignment moves the cognitive load from generation, which AI can do, to verification, which it cannot.

The Four Layers of AI in Education gives the faculty senate language several AAUP authors implicitly call for. AI Literacy Education, AI for Education, AI in Education, and AI of the Profession are four distinct conversations, with four different sets of stakeholders. Most institutional AI policies fail because they collapse all four into one document. Separating them is the first step toward governance that survives contact with actual practice.

Why this matters at LCC, and at every community college like it

Lansing Community College operates in the resource-constrained environment the AAUP issue describes. Its institutional strength, the reason its graduates land 93 percent employment within a year and post near-perfect NCLEX-RN and dental hygiene licensure rates, is rigorous human instruction. That outcome is the product of expert mentorship, not technological shortcuts.

The risk of getting AI integration wrong at a community college is therefore concrete rather than abstract. A nursing student who has outsourced the clinical reasoning to AI is a patient safety risk the moment the alert system fails. A CIS student who cannot audit AI-generated code becomes a liability the first time it ships to production. The professions community colleges feed do not accept the excuse that the AI gave the wrong answer.

This is what makes the gap between the AAUP issue and the classroom so consequential. Diagnosis without treatment leaves instructors with the knowledge that something is wrong and no protocol for what to do about it. The result is either panic-banning AI, which the data on student use suggests is futile, or pretending the problem will resolve itself, which it will not.

There is a third option. Read the AAUP issue for what it is, a serious account of the institutional pressures around AI adoption. Then pick up the practical frameworks that turn those concerns into Monday-morning assignments. The two documents are designed to work together.

The AAUP issue makes the case for vigilance. The guidebook makes the case for action.

The Spring 2026 issue of Academe (Vol. 112, No. 2) is freely available at aaup.org. The Learn-It-All Educator: A Guidebook for Training Brains, Not Replacing Them with AI is available in paper format on Amazon, with Chapters 1 through 4 free under Creative Commons on Zenodo.