MIT’s Report on AI Use in Teaching, Learning, and Research Training
How can universities protect real learning as AI reshapes coursework? Explore MIT’s principles and recommendations, with English and Chinese PDFs.
When AI can complete more and more assignments, how can a university tell whether students have actually learned? MIT’s August 13, 2026 report from its Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training puts this question at the center of its discussion.
The report considers how AI can expand learning and research while also addressing student judgment, trust between teachers and students, and relationships on campus. This reading guide was prepared by ClawGuard. The complete PDFs are linked below.
Read the report
Read / download the English PDF（40 pages） · 中文版 PDF（23 页） · Original MIT source
About the source: The report was written by MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training, co-chaired by Eric Klopfer and Sam Madden. The Chinese PDF is a reference translation and has not been verified as an official MIT translation; consult the English original for precise wording. The report contains committee recommendations, which should not all be treated as policies already in effect.
Define the learning goals before deciding how to use AI
The report asks instructors to start with what students should know, what they should be able to do independently, and what judgment they should develop. Assignments, assessments, and AI rules should follow from those goals. Different courses and stages of learning call for different arrangements; foundational practice and advanced research need not have identical restrictions.
AI can help students attempt larger projects and receive practice feedback. It can also allow them to skip the thinking needed to build understanding. The report places human learning and development ahead of tool efficiency.
Eight guiding principles
- Stay humble: Recognize that technology and evidence are changing, and leave room to revise policy.
- Experiment boldly: Explore new possibilities for courses, teaching, and learning experiences.
- Keep people central: Protect relationships and trust among students, instructors, and peers.
- Value the learning process: Help students understand the purpose of practice, exploration, and working through difficulty.
- Teach with clear goals: Make choices about AI serve the purpose of the course.
- Respond to context: Choose rules that fit the discipline, task, and stage of learning.
- Expand human capability: Use AI to extend what students can think about, create, and solve.
- Look beyond campus: Consider personal development, shared life, and social responsibility.
Where instructors can begin
The committee suggests assessment formats such as oral exams, semester portfolios, and take-home work combined with classroom discussion. Simply placing more weight on timed tests has costs: it can reduce room for sustained thinking, revision, and complex projects.
Courses also need clear rules. Appendix B offers four syllabus policy examples: unrestricted generative AI use, support-only use, required use, and prohibited use. These are templates to select and adapt to the context. The report also recommends that instructors explain their own use of AI in work such as preparation, feedback, and grading.
Universities need to provide ongoing support
The recommendations cover three areas: adapting educational processes; strengthening people, community, and the residential experience; and creating processes, teams, and tools for continued reflection and improvement. Course changes require instructor time, teaching assistants, suitable spaces, and ways to exchange practical experience.
Tool access also raises questions of fairness and privacy. The committee recommends attention to access to capable models, protection of sensitive data, continued model choice, and clear policies for logging and auditing. Campus administrators need to explain what data is retained, who can see it, and when users are informed.
A suggested reading route
Instructors can start with Section 3.1 and Appendix B. Readers concerned with student relationships and learning experiences can turn to Section 3.2. Campus technology, data, and governance teams can begin with Section 3.3. Appendix C presents survey findings; read the population, timing, and sample information alongside any figures you cite.
The question to carry into discussion is how courses can develop understanding, judgment, and collaboration when AI makes finished work easier to produce.