Lisa Park's five-day HAA project illustrates the Level-1 profile with honesty rather than pretension: a teacher who is competent in her subject, uses AI for practical efficiency, but has not yet developed a conceptual framework for what AI does in the teaching-learning relationship. Her work is consistently minimal, generic, and tool-focused, with almost no subject-specific pedagogical reasoning.
Day 1 was the most revealing. Lisa's Extension System Analysis reduced five AI uses to their simplest descriptions: 'making worksheets,' 'making PowerPoint slides,' 'marking assistance,' 'online quiz,' and 'chatbot for questions.' None of these names a pedagogical function — they name production tasks. The A/B/C classifications are assigned but never justified in cognitive terms. The 'If AI were removed?' column is filled with variants of 'I would do it myself' and 'same thing really,' suggesting Lisa sees AI exclusively as a time-saver rather than as something that changes the nature of the activity. Her Boundary Map is the project's thinnest artefact: all five entries are one-sentence observations ('AI can give wrong information,' 'AI can't do practicals,' 'AI doesn't know my students') with generic lesson activities ('All lessons,' 'Lab work,' 'Teaching') and single-sentence justifications. No entry names a specific biology concept, a specific lab activity, a specific student error pattern, or a specific CAPS content standard. The Teacher Irreplaceability Declaration follows the same pattern — all five reasons (reading body language, maintaining discipline, caring about students, adapting on the fly, assessing understanding) are generic teacher capabilities with no connection to biology as a discipline. Lisa's Phase 1 work would read identically if she taught mathematics, geography, or any other subject.
Day 2's Prompt Engineering Log was functional but pedagogically thin. Eight iterations moved from 'make a worksheet on mitosis' to a usable product, but the revision rationale at each stage noted format issues ('too easy,' 'need harder questions,' 'fix diagram issue,' 'add CAPS alignment') rather than anything about what the worksheet needed to achieve in terms of student understanding of cell division. The sixth iteration is notable: Lisa prompted for CAPS alignment but then admitted 'I need to check the CAPS alignment myself' and 'can't fully trust the curriculum alignment' — an honest acknowledgment that the AI doesn't reliably know the South African curriculum, but she did not explore what that means for her practice. The Output Evaluation Reports found a genuine sequencing error (prophase events out of order), an overconfident absolute claim ('always occurs in the S phase'), and a plant/animal cell omission — these are all real biology errors, but the 'why it matters' explanations stopped at 'students could learn the wrong order' and 'students should understand that biology has exceptions' rather than naming the specific conceptual model being disrupted or the downstream learning impact. Her Customised Artefact (Mitosis Worksheet Generator) was a format template with no embedded pedagogy.
Day 3's micro-lesson was structured as present → practice → review, with AI used at each stage in its most conventional role: slides for content delivery, worksheet for practice, memo for marking. The lesson structure was unchanged by AI presence — the technology accelerated production but did not change anything about the learning experience. Lisa's Decision Log entries are terse and observational ('used AI slides for note-giving — standard, nothing special'; 'students were copying notes'). The most significant entry is the exclusion at 25:00: 'Did Q&A without AI — just me and the students talking about what confused them,' followed by the retrospective 'This was the most productive part of the lesson actually.' Lisa noticed this but did not explore what it means — why was the AI-free segment more productive? What does that imply about the AI-mediated segments? The student work evaluations showed one genuine strength: Lisa caught that a rigid AI answer key penalized a student whose answer was actually more nuanced ('Your answer is actually more accurate than the memo!'). But this insight remained isolated rather than informing a broader reflection on AI's limitations in assessing biological understanding.
Day 4 was the project's weakest phase. Lisa accepted all five AI insights about her practice without modification or rejection, and none of her acceptance reasoning went beyond agreement ('True,' 'Yes,' 'Fair'). The AI insights themselves were generic praise ('strong focus on factual accuracy,' 'your approach shows growth in AI literacy,' 'your ethical awareness is developing'), and Lisa did not notice the echo-chamber pattern. Her stakeholder communications used nearly identical content and framing across all three audiences. Her PD goals were entirely tool-focused: 'learn to use AI for creating better visual materials,' 'explore AI tools for lab safety demonstrations.' No goal addressed a specific pedagogical limitation identified in Phases 1-3.
Day 5's ethical audit listed practical concerns (curriculum misalignment, content errors, over-reliance, internet dependency) rather than ethical risks. 'AI tools require internet access which isn't always reliable' and 'Free AI tools might start charging' are infrastructure risks, not ethical analyses. The structural ethics layer produced no analysis of how the AI tools' design assumptions might shape the teaching of biology or affect particular student populations. The Purpose Drift Audit identified efficiency-driven decisions honestly but the revisions were minimal: 'cross-reference with CAPS,' 'at least 50% of materials must be teacher-created,' 'generate and print materials in advance.' No revision changed a tool or platform; none referenced a specific biological concept, student group, or pedagogical principle.
Lisa's project is not the work of a bad teacher — her subject knowledge shows in the specific errors she catches, and her honesty about what works ('the Q&A without AI was the most productive part') suggests genuine reflective capacity. What is missing is a framework for thinking about what AI does in the relationship between her biology expertise and her students' learning. She uses AI as a photocopier with better formatting, and the project did not shift her beyond that.
100 log entries across 6 types
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