待翻译：MIT's Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training

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Report​ MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training August 13, 2026 1. Introduction This report is a call to action. Through five intense months of meetings, research, and outreach across the MIT community, the Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training sought to understand the role of generative AI in the life and educational mission of the Institute and recommend how to navigate its challenges and opportunities. In January 2026, Chancellor Melissa Nobles, Provost Anantha Chandrakasan, and Faculty Chair Roger Levy charged us1 specifically to: Assess current AI use at MIT. Identify innovations in teaching and student assessment. Propose an AI use policy. However, what we learned as a group quickly convinced us that the Institute community, particularly the faculty, must tackle a set of deeper questions about the structure, meaning, and value of an MIT education in an era in which AI is one of several factors complicating the Institute’s mission. Our committee consisted of undergraduate and graduate students, faculty from every school, and staff from relevant units, including the MIT Libraries and the Teaching and Learning Lab. Though we brought to the assignment a broad range of experience and no fixed thesis, our brief but intense explorations led us to a strong shared view. In this report, we: Highlight key aspects of the current educational landscape at MIT. Share eight principles we relied on and that we hope will guide the Institute in the work ahead. Recommend immediate and long-term actions for both instructors and the administration. As an institution deeply identified with the birth of AI and known for its distinctively rigorous, hands-on education, designed to produce graduates unafraid of the world’s hardest problems, MIT has a unique role to play in this moment. We believe it also has a responsibility to lead. We hope our report can help the Institute lean into the spirit of Mind, Hand, and Heart as it continues to define and foster the highest-quality residential education – of humans, by humans, in support of human flourishing, and for the betterment of humankind. Eric Klopfer, co-chair Sam Madden, co-chair On behalf of the Committee on AI Use in Teaching, Learning, and Research Training 1.1 The Landscape This report’s recommendations reflect the following insights about the educational landscape at MIT. Generative AI is everywhere already, spurring an assortment of views: MIT students use AI frequently and pervasively – with strongly mixed feelings, from curiosity, creative inspiration, and gratitude to resignation, concern, and anxiety. Instructors’ attitudes range from enthusiastic exploration and growing reliance on AI to skepticism, suspicion, and “AI refusal”– and there’s a widespread desire to share experiences, ideas and techniques. While AI is allowing instructors to develop exciting new learning experiences and enabling students to learn and experiment in innovative ways, there have been a number of concerning effects on the life of the campus, including signs that AI is: Upending foundational elements of the MIT educational experience, especially for under-graduates, from the p-set, the take-home exam and UROPs to office hours and the study group. Increasing isolation. Undermining student mastery and confidence. Eroding the “social contract” between instructors and students. Making it much harder to assess student progress. Challenging decades of distinctive MIT community norms and values about rigor, the creative friction required for learning, collaborative problem solving, and personal integrity. AI is generating both immediate rapid changes and long-term tectonic disruptions – and MIT needs to respond: Students are confused and concerned about a lack of clarity, consistency and justification about the use of AI, within a given subject and across the curriculum. Every subject taught at MIT will likely need to be reexamined and potentially revamped to make sure that how students are being taught, what they’re learning, and how they’re assessed are “AI-aware.” AI is changing what students need to know and know how to do. Beyond its implications for specific subjects and disciplines, the advent of AI demands a broader, holistic reassessment of the nature, scope and purpose of higher education today. Finally, AI presents substantial practical concerns for our community, from data privacy and confidentiality to questions around disparate access, bias, fairness, and accountability. AI’s explosive growth and ascendance also raise important questions for society, from the environmental impact of AI data centers, to the use of intellectual property and training data, to the overall human impact of the technology and the industry – and MIT needs to engage with those questions too. Other educational institutions are grappling with similar questions around AI and generating interesting ideas – but no one seems to have it all figured out. Three notes on the words we use: N.B. In this report, “Instructors” includes faculty and everyone else engaged in teaching at MIT. Most references to “students” apply to both undergraduate and graduate students, except in a few obvious places, as when we refer to the General Institute Requirements (GIRs) or to participants in the Undergraduate Research Opportunities Program (UROP). “We,” “us,” and “our” sometimes refer to the members of the committee, and sometimes the whole of MIT. The difference should be clear from the context. 2. Guiding Principles We start by defining eight principles we relied on and that we hope will guide the Institute in the work ahead. 2.1. Be humble Some technological innovations emerge gradually: As society and technology evolve in concert, mutual adaptation softens the impact. The computer – AI’s precursor and key enabler – fits this pattern. Other innovations land more abruptly, becoming socially consequential before individuals and institutions have time to adapt. Society tends to peg the “birth” of a new technology as the point when it becomes readily usable. By that measure, generative artificial intelligence was “born” with the release of ChatGPT in late 2022. Public engagement with generative AI is therefore less than four years old. In that time, it has amassed more than a billion users, and the companies selling AI technology have come to dominate the headlines, the stock market, and public consciousness. In other words, AI is progressing across almost every domain and on a timescale too compressed for society to properly observe and analyze its impacts and then gradually adapt. This suggests our first guiding principle: We offer our proposals in a spirit of humility. Course corrections – perhaps even major ones – will be inevitable as the technology continues its relentless evolution and the Institute experiments and learns. 2.2. Be bold Yet uncertainty can’t be an excuse for inaction. This is not a moment for patches and duct tape. The challenges AI presents in teaching and learning call for a bold strategic response – everywhere, and especially at MIT. With our Social and Ethical Responsibilities of Computing program2 completing its seventh year, we are uniquely positioned to find ways to employ this new technology for the benefit of society, and for our students in particular. AI also presents extraordinary opportunities, from unprecedented possibilities for individualized tutoring and coaching to a dramatic acceleration and revamping of research in many disciplines. Seizing these opportunities deserves and demands boldness too. Bold thinking is especially important because our students will go on to help shape the intellectual, ethical, and technical direction of our society – and soon. We owe them a deep engagement in rich and constructive uses of AI, and a sophisticated understanding of its potential and its drawbacks. Their MIT experience should prepare them with the wisdom to help determine how and where AI is used for the betterment of society and the world at large. 2.3. Put humanity front and center Facing a technology that already has such immense capabilities – built on, modeled after, and in many ways now exceeding human powers – the Institute’s fundamental challenge and most important goal must be to nurture and protect our shared humanity, and to value the MIT community, its members, and their flourishing above all. In a listening session with instructors, we learned that some were considering using AI agents as research assistants instead of hiring undergraduates as UROPs. One can see the case for speed and efficient use of resources (especially now, when research resources are so constrained). But if those criteria come to dominate our decisions, we all have to ask, “What is it that we are here together to do?” As a community, we need to keep in mind that although research is central to MIT’s mission, it’s more than an end in itself; on our campus, research is also an apprenticeship, a means of training the next generation of researchers to continue our work and drive our fields of inquiry forward. Using research as an opportunity for learning-by-doing may produce seeming “inefficiencies,” but that’s a feature, not a bug. A focus on our humanity should also alert us to the fact that common current remedies for the problems AI creates can risk damaging the relationships between and among teachers and students. For instance, many instructors recounted that having to “police” unauthorized AI use was harming their connection to students (a dynamic made worse by the fact that, as we learned, AI detection software is quite unreliable). For their part, students fear being wrongly accused of AI cheating and are increasingly frustrated by instructors’ use of AI in areas that demand a human touch, such as grading, assignment creating, and feedback. Such an underground river of mutual suspicion is no foundation for a healthy classroom. 2.4. Lean into learning Many problems and assignments used in MIT classes to reinforce learning and assess student progress can already be accomplished by AI. Unrestricted AI use by students can make some traditional assessments less reliable as indicators of individual learning, potentially weakening confidence in grades and credentials. For educators, this radical shift in norms and expectations can feel profoundly disorienting. But as a community, what should worry us most is that many uses of AI deprive students of the opportunity to learn. The threat AI poses to familiar ways of teaching and testing may be a blessing in disguise – because the changes are too sudden and severe to ignore. As the faculty on our committee can attest, for at least two decades, educators have lamented the strain on teaching and learning from fragmented attention, ubiquitous devices, a narrow preoccupation with grades rather than learning, and a weakening sense that students and teachers are joined in a common intellectual project. As an institution that prides itself on the power of its distinctive educational recipe, it is up to MIT to seize the opportunity of this moment: to make sure that the undeniable changes imposed by AI become a tipping point, forcing us to deal decisively with the forces eroding our shared educational mission. Leaning into learning means creating a new “social contract” between teachers and students. All of us who teach at MIT will need to be prepared to help students understand both that the process of education is necessarily a productive struggle, and that the most important product of their education is not a GPA or a diploma but themselves: their personal growth and intellectual maturity and the development of their own imagination, insight, and judgment. Instilling these attitudes needs to become a central task for every educator, so that our students know not only what th [truncated for AI cost control]