Discernment and Disclosure for AI Agents in Education
Marc Watkins argues faculty should question when to use AI agents and disclose their use to students.
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Public readings arranged by original publication date. People and institutions use explicit mappings; article sources remain in their details.
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Beyond chatbots: AI agents as software, not characters
Furze argues chatbot framing hides corporate responsibility and misdirects education's assessment debates.
The Overhang
Mollick argues that expertise, breadth, taste and agency help people use AI capabilities. His experiments invite a question: how do learners acquire the knowledge and judgment needed to direct and evaluate increasingly capable tools?
Varying Retrieval Practice to Promote Transfer of Learning
Sumeracki explains Butler et al.’s (2017) experiments contrasting repeated and varied application during retrieval practice. Her account invites asking whether an AI activity makes students retrieve, explain and apply concepts across contexts.
Reading Across Fields and Working with AI: A Course Retrospective — Shishufeng EP29
The second retrospective describes AI-built demonstrations and source navigation, while exposing the limits of keyword retrieval.
Motivation, Themes and Practice: A Course Retrospective — Shishufeng EP28
Chongqing and participant Mizai revisit how a music course found its themes and used live demonstrations.
What if transferable skills don’t exist?
A deliberately strong argument locates reasoning and problem-solving in domain knowledge.
Finding Sources in the AI Era — Shishufeng EP27
Hanyang and Liushou discuss moving from an unfamiliar topic to a syllabus, search terms and original sources.
What Is Understanding? — Shishufeng EP23
Chongqing uses Hinton’s account of understanding to question what learners lose when AI explanations become too smooth.
Choosing to Stay Human
Mollick considers how convenient AI defaults can bypass the effort through which people develop valued abilities.
China’s AI+Education Action Plan: curriculum, teacher development and governance
The five-department action plan connects AI literacy, university curricula, teacher development, applications and safeguards, with goals for 2030. These are policy directions, not completed implementation or demonstrated learning effects.
Teaching Friends to Use AI — Shishufeng EP9
The hosts find that tacit knowledge about files, versions and structure often explains beginners’ difficulties.
Is Relying on AI Cognitive “Offloading” or “Outsourcing”?
Outlines illustrate a distinction between external memory support and delegating the thinking itself.
Playing Pretend: Expert Personas Don’t Improve Factual Accuracy
This report tests whether assigning expert identities reliably improves answers to difficult factual questions.
Pitfalls in Evaluating Generative AI — Hung-yi Lee, Lecture 4
This public lesson extends AI literacy to evaluation: metrics, human preferences, model judges and benchmark contamination.
Education isn’t working
The essay distinguishes productive work from the practice needed to become an expert.
How ChatGPT Might Inadvertently Improve Student Thinking
Willingham describes conferencing as a way to retain and inspect writing-related thinking.
Against “Brain Damage”
Mollick challenges sensational interpretations of AI studies and examines learning, idea generation and writing practices.
Prompt Engineering is Complicated and Contingent
Repeated tests illustrate why a successful answer does not establish a reliable prompting technique.
Historical reference: AI-Generated Summaries Are a Problem—for Authors and Society as a Whole
An author’s inspection of generated book companions reveals losses beyond factual accuracy.
Andy Matuschak — The reason most learning tools fail
Matuschak distinguishes forgetting from never understanding, and discusses questioning, memory and fading scaffolds. The interview offers a lens for AI tools: do they help learners notice confusion and act, or mainly ease consumption?