Tom Reeves's five-day HAA project occupies an instructive space between Level 1 and Level 2. He is more engaged than the minimum-effort profile and occasionally produces work that shows genuine pedagogical thinking — particularly in Phase 3 and Phase 5 — but his dominant pattern is over-delegation to AI, acceptance of AI output without sufficient critical scrutiny, and a tool-level understanding of what he is doing.
Day 1 opened with a telling admission embedded in the first activity description: 'I ask the AI to write lesson plans for me... It creates full lesson plans with objectives, activities, and assessment ideas. Really helpful for planning.' Tom classified this as Type A (cognitive extension), but the description reveals a Type B dynamic — the AI is not extending his cognition, it is producing the lesson plan while he reviews it. This misclassification, repeated in 'Research summaries' (also classified A when the description shows AI producing content Tom consumes passively), is a signature Level-1 error: the A/B/C taxonomy is applied to tool names rather than to the cognitive function being extended or delegated. Tom's Boundary Map was slightly stronger than his other Phase 1 work — he named 'AI can't analyze primary sources properly' and 'AI can present biased historical narratives,' which show awareness of discipline-specific limits — but each entry was stated as a general principle rather than grounded in a specific lesson or student population. The Teacher Irreplaceability Declaration was the strongest Phase 1 piece, particularly the entry about 'choosing which historical perspectives to emphasize' with the example of including enslaved peoples' perspectives when teaching Rome. This shows genuine subject-specific pedagogical reasoning, but it stood alone among four generic entries.
Day 2 was where Tom's over-delegation pattern was most visible. His Prompt Engineering Log was not really about engineering prompts — it was about generating complete lesson plans, tests, rubrics, and handouts from AI. The iterations show format refinement ('need to provide my own sources,' 'the AI's idea of differentiation is too simplistic') but the dominant posture is acceptance: 'Looks professional. The content seems accurate. Probably usable as-is.' Critically, when the AI generated a fabricated primary source quote attributed to Pliny the Elder, Tom caught it — 'doesn't look like a real source. Might be made up' — but this catch happened during the project when he was specifically looking for AI errors, and his own later admission (Phase 5) that he 'initially accepted' a fake source before checking suggests this vigilance is not yet habitual. The Output Evaluation Reports were Tom's best Day 2 work: he identified a genuine fabrication, a Eurocentric narrative bias, and overconfident dating — all specific to historical methodology. The corrected versions showed real subject knowledge. But these evaluations existed in isolation from his actual practice of using AI to generate lesson plans wholesale.
Day 3 was Tom's most interesting phase because it produced his most authentic teaching moment. When students copy-pasted chatbot responses, Tom made a real-time judgment call: close the chatbots, write from memory, then reopen to check. This intervention showed genuine pedagogical instinct. The discussion about authentic versus AI-sounding presentations was an unplanned learning moment that Tom recognized and leveraged. His Decision Log captured eight entries including two overrides and an exclusion — the override at 12:00 (stopping copy-paste) and 18:00 (the Caesar 'greatest leader' claim) both showed in-the-moment subject-specific judgment. However, the lesson structure itself was conventional: AI slides for content delivery, chatbot for research, presentations without AI. The AI did not change Tom's lesson architecture; it substituted for library books and textbook assignments. Student work evaluations showed Tom can identify when AI flattens historical complexity (the Caesar evaluation is strong), but his feedback focused on information accuracy rather than on developing students' historical thinking processes.
Day 4 showed the uncritical acceptance pattern at its clearest. Tom accepted all five AI insights about his practice. While some acceptances were reasonable, the overall pattern of zero rejections, zero modifications, and no echo-chamber identification placed this squarely at Level 1 for D4.1. The irony is that insight #1 ('You rely heavily on AI for lesson planning, which could reduce your own pedagogical creativity over time') is an accurate diagnosis of Tom's central challenge — and he accepted it — but acceptance without a concrete response plan is recognition without transformation. Tom's stakeholder communications were differentiated in tone (casual for students, formal for admin) and the student communication was genuinely effective ('AI is like that friend who sounds super confident but sometimes gets things completely wrong'). His PD plan included one strong self-identified goal (building a verified primary source library), directly addressing the fabricated Pliny incident, and a modified-from-AI goal about reducing lesson planning dependency.
Day 5 was Tom's strongest phase. His ethical audit moved beyond generic risk categories to identify mechanisms with some specificity: 'The chatbot gives instant answers that feel complete, reducing motivation to dig into primary sources' names a real dynamic in history classrooms; 'AI defaults to mainstream narratives and simple cause-effect chains, discouraging nuanced historical analysis' identifies a discipline-specific epistemological risk; 'AI's training data reflects Western-centric historical perspectives' names a structural bias with concrete implications for his teaching of Ancient Rome. The Purpose Drift Audit was honest and specific — his acknowledgment that 'I was outsourcing my own content knowledge preparation to AI. A history teacher should be reading scholarship, not AI summaries' is a genuine insight that connects to the HAA philosophy's distinction between knowing and producing. His unit plan revisions included one tool-level change (building a verified source library to replace AI-generated primary sources) and several significant behavioral changes (AI becomes a verification tool rather than a first source; core learning arc planned by teacher, AI only for supplementary materials).
Tom's project arc shows a teacher who began by over-delegating to AI, discovered through the project's structure that this was a problem, and arrived at genuine insights about what needs to change — but whose understanding remains partially formed. His Phase 5 work is more sophisticated than his Phase 1 work, suggesting the project itself catalyzed some development. The gap that remains is between recognizing the problem ('I need to plan lessons myself') and understanding at a conceptual level why the cognitive work of lesson planning is irreducibly part of knowing the lesson — the core HAA proposition. Tom knows he should do things differently; he does not yet fully understand why, in terms of what AI does to the teacher-learner-content relationship.
100 log entries across 6 types
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