Teaching
Selected articles on assessment, student learning, graduate education, and responsible AI use in teaching.
Frank Calegari proposes changes to mathematical practice in the AI era
Frank Calegari argues for broader graduate education while retaining technical skills and strengthening students’ ability to explain mathematics.
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Early-career researchers share their perspectives on AI in mathematics
Graduate students and postdocs describe their experiences and concerns about AI in this survey preprint, offering perspectives for educators and mentors.
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U-M releases guiding principles for the use of artificial intelligence
U-M’s guiding principles address critical thinking, AI literacy, human judgment, integrity, and privacy in teaching and learning.
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Álvaro Lozano-Robledo offers students advice on mathematics in the AI era
Álvaro Lozano-Robledo offers students advice on studying mathematics in the AI era, emphasizing understanding, research, and communication.
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Report of Summit on PhD Math Education in the Age of AI
This summit report discusses how doctoral mathematics education can adapt to AI and how expectations for graduate training may change.
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David Glickenstein proposes graduate training centered on mathematical judgment
David Glickenstein proposes graduate training focused on evaluating mathematical ideas and arguments as generative AI changes learning and assessment.
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Ploum reflects on university exams with optional chatbot use
Ploum describes an exam with optional chatbot use, prompt disclosure, and student responsibility for errors. This is a personal teaching account, not a controlled study.
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