As I continue to think through the ideas for a possible book on the future of private colleges in the era of artificial intelligence, I have been looking for institutional responses to AI that go beyond the usual questions of cheating, plagiarism, detection software, and classroom policy.
The AI detection, plagiarism, and compliance issues are important. At the same time, faculty members and students need clarity. More college students are using AI than faculty members, and neither party is going to stop using it. Institutions need AI policies, but the more I read and reflect, the more I know that those are not the biggest issues.
A deeper question is this: What value can colleges provide when artificial intelligence can explain, summarize, draft, code, translate, tutor, simulate, and respond to students at any hour of the day?
A recent report from MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training is one of the most thoughtful institutional documents I have read regarding this topic. Importantly, the report’s focus isn’t limited to technology, academic integrity, or policy about AI usage. It is not a technology report. Its aims are broader, contemplating the meaning of education in an AI-shaped world.
MIT is obviously not a typical university. For as long as I can remember, it’s been ranked in the top 10 of America’s best universities. MIT has extraordinary students, faculty, resources, research infrastructure, and a long history of leadership in science, engineering, computing, and hands-on education. Its “Mind and Hand” culture is distinctive. Its Undergraduate Research Opportunities Program (UROP) is unmatched in scale and reputation. Its faculty were some of the pioneers in the development of Artificial Intelligence. While many colleges cannot duplicate MIT’s culture and resources, college leaders should read this report, especially its recommendations.
When MIT concludes that AI requires a reexamination of course goals, assessment, social learning, project-based work, undergraduate research, residential education, faculty support, and the broader purpose of higher education, other colleges should not assume that a few syllabus statements and a chatbot policy will be enough.
The report begins with a charge to the committee that sounds relatively contained: assess current AI use, identify innovations in teaching and assessment, and propose an AI use policy. But the committee quickly concluded that the institution had to confront “deeper questions about the structure, meaning, and value” of an MIT education in the AI era. Given the qualifications of MIT’s faculty, that is a cautionary signal to higher education in general.
AI Is Already Changing the Student Experience
One of the report’s important contributions is the committee’s refusal to pretend that generative AI is somewhere outside the gates. The report indicates that generative AI is everywhere in the student experience. Students use it frequently, but with feelings of curiosity, inspiration, gratitude, concern, resignation, and anxiety. Faculty surveyed expressed feelings ranging from enthusiastic adoption to skepticism and refusal, with many wanting to learn from one another about what works and what does not. Those reactions are not unique to MIT. They are happening at many higher education institutions.
Students and faculty continue to experiment with generative AI tools. Some faculty (and maybe a small percentage of students) are pretending (or claiming) not to experiment. Some students and faculty are anxious about the tools. Some are inspired by them. Some are using AI to improve learning, while others are using it to avoid learning. Some faculty are rewriting assignments. Others are returning to in-class exams. Some institutions are building AI policies. Others are waiting for the dust to settle. I wouldn’t bet on the dust settling anytime soon.
The MIT committee describes AI as producing both immediate changes and long-term disruptions. The report’s authors write that students are confused by inconsistent AI guidance across courses, and that every subject may need to be reexamined so that teaching, learning, and assessment become “AI-aware.” The committee notes that AI changes what information and methods students need to know, while raising larger questions about the purpose of higher education itself. AI is not a tool that can fit neatly inside the old model. Student use of AI is changing the old model, whether institutions are ready or not.
The committee observed that colleges treating AI mainly as a cheating problem are missing the larger issue. Faculty can’t prevent the use of AI to complete an assignment any more effectively than they could limit the use of Google or Wikipedia 20 years ago. The issue at hand is whether the assignment still measures what we say it measures, whether the course goals still make sense, and whether students are learning how to think, judge, create, collaborate, and persevere when a machine can give them something that looks like an answer.
The Illusion of Learning
The MIT report is especially strong on the danger of confusing efficiency with education. MIT students may be unusually busy, but the pressure of completing college assignments is normal everywhere. Students take heavy course loads, work jobs, participate in activities, worry about grades, and try to keep up with their friends. When a tool offers a faster route to the answer, the temptation to use it is obvious.
MIT’s report warns that AI makes it easier for students to offload the cognitive work of learning. Its authors write that when students automate the work, they may lose the “cognitive friction” and “productive struggle” required for durable understanding.
Notice how the term “productive struggle” articulates something more valuable than pointless difficulty. It is not a defense of bad teaching, nor is it nostalgia for the days when students had to suffer through inefficient learning simply because no better tools existed. Productive struggle describes the kind of effort that changes the learner. It is the struggle of working through a problem set, revising an argument, debugging code, testing a hypothesis, interpreting a text, listening to criticism, and discovering why a first answer was inadequate. Properly designed, productive struggle is arguably the entire thrust of real learning.
AI can help students learn. It can provide explanations, examples, practice, feedback, and support. For some students, AI may reduce intimidation and help them get unstuck. The report includes examples of AI coaches helping students practice skills, including public speaking and mediation. However, when AI becomes the place students turn to at the first hint of difficulty, it can short-circuit the very process by which competence develops.
The report’s distinction between augmentation and automation is important. The committee argues that AI should be used to augment curiosity, creativity, and learning, not automate them. The report warns that chatbots can create an “illusion of learning” and that overreliance can weaken memory, confidence, mastery, and critical thinking. That warning applies far beyond MIT.
College leaders must ask a difficult question: Are our students using AI to become stronger learners, or are they using AI to avoid becoming learners at all? The answer varies by course, instructor, student, discipline, and assignment. But institutions need to take the question seriously and react to it.
Assessment Cannot Remain the Same
AI has exposed long-simmering weaknesses in traditional assessment. For decades, colleges relied on problem sets, papers, take-home exams, coding assignments, projects, and other out-of-class work to help students practice and demonstrate what they know. MIT’s report states that generative AI has “created and revealed” a mismatch between established learning objectives and familiar assessment forms.
When a student can use AI to generate a credible essay, solve a problem, write code, summarize a reading, produce a report, or prepare a take-home exam response, the college must reassess what exactly is being measured. Is the student demonstrating understanding of the subject matter, or merely deft tool use? Is the student developing mastery, or cosplaying it? Is the final product sufficient evidence of learning, or does the instructor need to see the student’s process, judgment, revision, and defense?
Some faculty have responded by increasing the weight of in-class exams or requiring students to write or code under time constraints. That response is understandable. However, MIT’s report notes a limitation: if assessment moves too heavily toward quick, high-stakes in-class evaluation, students may have less incentive to invest in the time-intensive projects, problem sets, and deliberative work that build mastery.
The report recommends assessments that are less vulnerable to AI and more valuable for learning, such as oral exams, semester portfolios, and out-of-class assignments paired with in-class conversations. That recommendation should resonate with any institution thinking seriously about the future of the transcript and its impact on the employability of its graduates.
Transcripts record courses, credits, grades, majors, and degrees. In the AI era, the transcript is too thin to demonstrate the entire educational experience. Colleges and employers need richer evidence. Students need to show how they think, how they revise, how they use tools, how they work with others, how they defend conclusions, and how they apply knowledge in real contexts. Not every course requires an oral exam, nor does every assignment require a portfolio. At the same time, colleges should stop assuming that the familiar artifacts of learning are automatically reliable.
The primary question should not be, “How do we make every assignment AI-proof?” That goal is unrealistic and may lead to a policing culture instead of a learning culture. The better question is, “How do we make student learning more apparent?”
From Coverage to Capability
MIT’s report urges instructors to revisit course goals before constructing AI-aware assessments. It asks a simple question: “What should students know or be able to do by the end of the course?” Every college should be asking the same question.
For decades, courses have been organized around coverage. Week one covers the foundations. Week two covers the historical background. Week three covers the major theories. Week four covers the methods. The course may be rigorous, coherent, and valuable. But the organizing question has often been, “What content will we get through?” AI changes that question.
Content is still important. Students need disciplinary knowledge. A student cannot think historically without understanding history, scientifically without understanding science, mathematically without understanding mathematics, or ethically without engaging seriously in moral reasoning. I am not suggesting that content is dispensable. But content is no longer scarce in the same way it was fifty years ago. Explanations are no longer scarce. Tutoring is no longer limited to teaching assistants or instructors.
The better organizing questions for the college leadership teams are:
- What can students do with what they know?
- Can they analyze evidence?
- Can they ask better questions?
- Can they distinguish strong claims from weak ones?
- Can they use AI without surrendering judgment?
- Can they communicate to a real audience?
- Can they work with others?
- Can they revise their thesis in response to criticism?
- Can they apply knowledge to a problem that does not come neatly packaged at the end of a chapter?
These questions reflect the shift from coverage to capability.
MIT’s report recognizes that AI may allow instructors to create learning goals that were previously difficult or impossible, such as having students work with complex texts, engineering artifacts, or large software systems in new ways. That is one of AI’s most exciting educational possibilities. AI can lower some barriers, allowing students to attempt more ambitious work. However, that work is only beneficial if it still requires human judgment, social reasoning, and conceptual understanding.
Project-Based Learning Becomes More Important, Not Less
One of the most important sections of the MIT report recommends increasing the role of experiential and project-based learning. This may seem counterintuitive at first. If AI can help produce code, designs, analyses, and written products, one might think projects become less meaningful. The report’s authors argue the opposite.
The report notes that AI may allow students to complete more ambitious projects than were previously possible in a single semester. In software engineering, for example, students can now use AI coding tools to build near-production-quality artifacts. In architecture, AI can help students visualize and test ideas more rapidly. The committee is careful to note that this only underscores the importance of fundamental skills, judgment, conceptual understanding, and social reasoning, so that students remain in charge of their ideas and their vision. That is the balance colleges should seek.
AI can make projects bigger, faster, and more ambitious. But the educational value of the project lies in what the student learns while doing it: framing the problem, testing assumptions, using evidence, receiving feedback, collaborating, making tradeoffs, revising, and explaining why the work matters. MIT’s report also notes that properly structured collaborative projects can build communication, problem-solving, emotional intelligence, and collaboration. All of these are durable human skills employers will increasingly demand.
This is important restructuring advice for all colleges, but especially for private colleges that claim to provide a more personal and formative experience. If colleges want to prove their value, they should create more opportunities for students to work on meaningful projects with faculty, peers, alumni, employers, and community partners. Projects should not be reserved for honors students, seniors, or students who already know how to find opportunities. They should be built into the curriculum.
A student who graduates with a transcript has a record of completion. A student who graduates with a portfolio of projects has evidence of capability. That difference will become more important as AI changes entry-level work.
AI and the Social Fabric of Learning
Perhaps the most significant part of the MIT report is its discussion of social learning. The authors write that AI is already affecting office hours, study groups, online discussions, and other patterns of campus life. They warn that reliance on chatbots can displace peer-to-peer learning and reduce interactions between students and instructors. That concern should not be dismissed as nostalgia. Social learning is not incidental to college, but it is one of the indispensable reasons college works.
Students learn from faculty, but they also learn from each other. Students learn in study groups, labs, residence halls, clubs, teams, rehearsals, research groups, dining halls, advising meetings, informal conversations, and through late-night struggles with difficult problems. They learn how to explain ideas, ask for help, disagree, persist, fail, recover, and participate in an intellectual community.
AI can provide answers. It cannot replace the experience of working through a proof with peers, testing an experiment repeatedly, arguing over a design, presenting to a group, or hearing a professor ask the question the student did not know to ask.
MIT’s report is explicit that residential education is powerful in part because it happens everywhere. The impact of residential education is that learning occurs not only in classes, but in living groups, sports teams, arts groups, clubs, labs, and other communities. That observation is central to my thesis about the future of colleges in the era of AI.
When learning is reduced to the delivery of content, AI looks like a replacement. If learning is understood as a cultural practice through which students make meaning, develop judgment, form identities, and join communities, then AI’s role changes. It becomes a tool that must be governed by a larger educational purpose.
The authors of the report recommend structured in-person social learning in subjects. They provide examples such as group projects with staff check-ins, guided problem-solving sessions, rubric-based feedback discussions, and facilitated in-class discussions. They also recommend that instructors explain why such interactions matter for both learning and community.
That last point is important because students need to understand why doing the work themselves is in their interest. Policies alone will not persuade them. Detection software will not persuade them. Top-down prohibitions will not create a learning culture. Colleges need to rebuild the shared conviction that human learning requires human engagement.
The Community Premium
MIT’s report intersects directly with the idea I have been calling the community premium.
The community premium is the added value created when a student joins a serious, intentional, high-expectation community of students, faculty, staff, alumni, and outside partners. For many of us, college was the last time we experienced a true sense of community, but nostalgia is an oversimplification. Living and learning on a beautiful campus is obviously appealing, but not immediately differentiating. Friendships can form through proximity, whether or not AI is used. The community premium, as I define it, is how effectively an institution designs the relationships, expectations, experiences, and evidence unique to the student’s time spent there.
The MIT report says AI is not arriving in a socially neutral environment. Students across higher education report anxiety, depression, loneliness, and disconnection. The report warns that AI could accelerate erosion of the habits that residential education depends on, such as showing up, building trust, joining shared efforts, and living with mutual obligation. AI could also become an impetus for deliberate rebuilding.
AI could make college less human. Or it could force colleges to become more intentionally human. That choice will not be made by technology. It will be made by the humans running institutions. Colleges can use AI to automate interactions, reduce human contact, weaken office hours, diminish study groups, and make students feel that learning is an individual transaction between a person and a machine, or colleges can use AI to free up time for more human work, create better projects, support students who are stuck, provide new forms of feedback, and help students prepare for richer in-person learning.
The committee writes that AI should become an opportunity to renew residential education around human presence, shared work, and meaningful mentorship. That is on target for private colleges. In fact, this is the heart of the strategic challenge. Most private colleges charge more than public alternatives. They justify that price by promising community, faculty attention, mentorship, and personal formation. AI will make vague versions of those promises harder to defend. If a student can get explanations, tutoring, writing assistance, and coding help from a machine, the college must show what the human community adds.
The answer cannot be “we have small classes” unless those classes are designed for interaction, feedback, and growth. The answer cannot be “our faculty care” unless students experience that care consistently. The answer cannot be “we prepare students for careers” unless students graduate with evidence, networks, judgment, and job offers. The answer cannot be “we are a community” unless students are known, challenged, mentored, and missed when absent.
Undergraduate Research and the Risk of Replacing Novices
The MIT report’s discussion of the institution’s Undergraduate Research Opportunities Program (UROP) is also instructive. MIT notes that UROP directly engages 93 percent of undergraduates and 58 percent of faculty. The report describes undergraduate research as providing credit and paid work, but says the broader benefits of personal development, mentor and peer connections, career exploration, and entry into intellectual communities are even more important.
Undergraduate research is valuable not only because students help produce it. It is valuable because students become participants in a community of inquiry. They learn how questions are formed, how mistakes are interpreted, how credit is shared, how disagreement works, and how knowledge is produced collectively.
The report raises a concern that should worry every educator. If AI systems become cheaper or more efficient replacements for undergraduate researchers or research assistants, students may lose access to the relationships and practices through which belonging, confidence, judgment, and professional identity are formed.
In many organizations, novice work is inefficient. Beginners are slower than experts. They require supervision, make mistakes, and ask basic questions. In a narrow productivity analysis, AI may appear to be a better choice for certain tasks. However, reducing education to a narrow productivity exercise is shortsighted at both the institutional and societal levels. Novice work is how novices become capable. If AI replaces too many novice opportunities, students may lose the entry-level experiences through which tacit knowledge, judgment, and confidence are formed.
This concern applies beyond undergraduate research. It applies to internships, campus jobs, lab work, writing centers, peer mentoring, teaching assistantships, project teams, and entry-level employment after graduation. If colleges want students to thrive in an AI-saturated world, they need to preserve and expand structured opportunities for students to practice real work under human guidance.
Clear Policies With Educational Rationales
MIT’s report also gets the AI policy issue right. The committee recommends that every course have a clear generative AI policy, posted prominently, but argues against a one-size-fits-all rule. More importantly, the report says AI policies should include a rationale tied to the course’s learning goals.
Rationale tied to the learning goals of the course is the right approach. Students do not need arbitrary rules. They need rules that make educational sense. If AI is prohibited, students should understand why. Perhaps the task is designed to build foundational skills. Perhaps AI use would bypass necessary practice. Perhaps the student must demonstrate independent problem-solving for a later exam, course, or professional standard.
If AI is allowed, students should understand how and why. Perhaps it is being used as a tutor, editor, simulator, coding assistant, or research aid. Students should know what must be disclosed, what remains their responsibility, and how to evaluate the tool’s output. If AI is required, the assignment should teach students how to use it critically and ethically.
This rationale is very different from saying, “AI is cheating.” Sometimes it is. Sometimes it is not. The better question is: “What is the purpose of the learning task, and does AI support or undermine that purpose?” MIT’s report emphasizes that students remain responsible for any work submitted with AI assistance, including inaccurate, biased, offensive, or unethical content. That is the standard all colleges should teach. AI can assist. It cannot assume responsibility.
Faculty Need Support, Not Just Expectations
The MIT report is also honest about the burden placed on instructors. It recommends an AI Pilot Fund to support course revision, experimentation, AI-enabled projects, deliberately AI-free experiences, and additional resources such as AI credits, teaching assistants, UROPs, and summer support. It also recommends ongoing training, lunch-and-learn seminars, communities of practice, and institutional support. These are important issues.
Too often, higher education responds to change by asking faculty and staff to do more without asking what should stop or what support is required. Faculty are expected to redesign assignments, learn new tools, preserve academic integrity, support students, experiment with AI, assess differently, and maintain rigor, all while continuing their regular teaching, research, advising, and service responsibilities. Asking more without changing pay or other workload assignments is not a sustainable strategy.
If colleges believe AI-aware education is essential, they must invest in the people who will design it. Faculty development cannot be a compliance exercise. It must become strategic infrastructure. The same is true for staff. Advisers, librarians, instructional designers, career professionals, student affairs staff, IT teams, and teaching and learning centers all have roles to play. More than any AI tool, it’s people who will end up transforming education.
What Should College Leaders Do?
The MIT report is a call to action. It says thoughtful AI integration into the classroom, research, and residential education is “not an optional exercise.” It argues that MIT must rethink what students need to know, revise assessments, teach students to use AI appropriately within disciplines, and help them develop the skills, judgment, and attitudes needed to succeed in an AI-saturated world.
I would translate those recommendations into several questions for college leaders.
- Have we defined what we want students to learn in an AI-aware world?
- Do our assessments still measure what we claim to value?
- Are we using AI to deepen learning or merely to make old work more efficient?
- Are students learning to use AI responsibly within their disciplines?
- Are we protecting the productive struggle that builds mastery?
- Are we strengthening or weakening human interaction?
- Are we expanding project-based, experiential, and research opportunities?
- Are faculty and staff being supported with time, training, tools, and recognition?
- Are students receiving clear AI policies with educational rationales?
- Are we measuring how AI is affecting learning, confidence, belonging, and community?
Those questions are relevant to a broad group of stakeholders. They belong to presidents, provosts, deans, trustees, faculty governance bodies, student affairs leaders, enrollment leaders, career services, advancement offices, and alumni leaders.
AI is not an IT issue, a faculty issue, or a cheating issue. AI is an institutional value issue.
The Future of College Is More Human, Not Less
The most important lesson from MIT’s report is that AI should make college more human.
It is easy to imagine a less-human future in which students interact less with faculty, rely less on peers, skip office hours, avoid study groups, outsource drafts, automate problem-solving, and treat education as a private exchange with a machine. It is also easy to imagine institutions using AI to reduce costs in ways that weaken relationships. But that need not be our future, and that choice is ultimately up to the humans in charge.
AI can also help colleges create more ambitious projects, provide better feedback, support students who are stuck, improve accessibility, help faculty create new learning experiences, and prepare students for a world in which human judgment and machine capability will be intertwined. The difference will depend on institutional design.
Whether AI will replace education is the wrong concern. Colleges should ask whether they will use AI to strengthen or weaken the parts of education that matter most. For private colleges, this is especially urgent. The future of private colleges will not be secured by defending the old model of knowledge scarcity. It will be secured by proving a new model of community value.
In a new model of community value, students must be challenged and mentored. They must learn to use AI responsibly. They must work on real problems and build evidence of capability. They must graduate with human networks, not just digital fluency. MIT’s report may be written for MIT, but its core message should echo across higher education. Institutions that want to preserve the value of their education will need to change.
Colleges that want to preserve the human value of education cannot simply preserve old practices unchanged. They must redesign learning so that human presence, judgment, community, mentorship, and productive struggle are not accidental byproducts of college, but central features of it.
AI is forcing colleges to answer an old question with new urgency: What is the value of bringing together students, faculty, staff, and mentors in a community of learning? The answer cannot be content alone. The answer must be formation. Formation, done well, remains one of the most human forms of work we have.
