Essays, position papers, and presentations on AI, pedagogy, and related topics. These are not formal academic papers; they represent ongoing thinking intended to be useful to practitioners and others engaged with these questions.
This session draws on my teaching experience from 2019 to the present — through the pandemic, a period of AI-skeptical rebuilding, and successive phases of AI integration — to argue that relationships, trust, and community are not extras in learning environments: they are preconditions. The central question is whether AI use supports or substitutes for the work of learning, and how educators can evaluate that distinction using a relationships-first lens.
The session uses the Eight Dimensions of AI Literacies (Gunder et al., 2024) as an organizing framework, mapped against a support-versus-substitution distinction. The accompanying handout is designed to be useful independently of the presentation as a practical reference for evaluating AI use in educational contexts.
A historical reading guide tracing one recurring pattern across roughly 250 years of intellectual history: a confident claim about achieving genuine machine autonomy, followed by real and lasting tools that claim actually produced, followed by a quiet retreat from the original claim. Covers cybernetics, GOFAI, behavior-based robotics, BDI multi-agent systems, and LLM agents. Designed to be used independently of any specific presentation or course session.
Seven years of teaching experiments with various ed-tech tools including AI - from chatbots as "ideal classmates" to students building their own study tools - led to a surprising conclusion: the relationship between teacher and learner is not a precondition for AI integration. It is the point. Without trust, community, and genuine care, AI tools become sophisticated avoidance mechanisms on both sides.
This session traces one instructor's evolution from novice to practitioner to cautious philosopher, sharing what worked, what failed, and what the failures revealed. The central framework do not automate the work that brings intrinsic reward offers faculty a practical filter for deciding what to delegate to AI and what to protect.
Participants will leave with a transferable philosophical grounding, concrete examples from seven years of classroom experiments, and questions of their own. Heavy discussion is by design, not accident.