HAA Performance Report — Sarah Mitchell

Evaluation Summary

Sarah Mitchell demonstrates consistent Level 4 performance across all 24 sub-competencies. Every artefact is deeply grounded in Mathematics with specific references to curriculum, students, and subject-specific reasoning. The longitudinal arc from Phase 1 analysis through Phase 8 assessment redesign shows genuine professional growth — later phases explicitly reference and build on earlier findings. Phase 6 (collective agentic practice) produces a joint artefact with recorded disagreement; Phase 7 (equity) names specific learner groups with evidence; Phase 8 (assessment validity) redesigns a structurally non-evidential assessment form. Evidence quality is predominantly direct (timestamped logs, video, annotated transcripts).

Strengths

Exceptional domain-specificity throughout — every artefact reads unmistakably as Mathematics

Genuine longitudinal coherence: Phase 5 audit traces directly back to specific Phase 1–3 decisions

Strong metacognitive awareness in AI-assisted reflection — identifies echo-chamber effects with precise mechanism analysis

Prioritised Growth Areas

Continue developing cross-subject collaboration frameworks to share Mathematics-specific AI evaluation methods with colleagues in adjacent disciplines

Formalise the structural ethics analysis into a departmental policy proposal

Extend the prompt engineering approach to assessment design, not just content generation, within Mathematics

Rubric Scores — All 15 Sub-Competencies

D1 Foundations of Extended Cognition

D1.1

Extension-System Understanding

A/B/C foundational · Phase 1 · Extension System Analysis + Vendor Annotation

Sarah Mitchell demonstrates advanced, transferable competency in D1.1. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D1.2

Recognition of Extension Boundaries

A/B/C foundational · Phase 1 · Boundary Map

Sarah Mitchell demonstrates advanced, transferable competency in D1.2. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D1.3

Anchoring of Teacher Agency

Critical in C-type · Phase 1 · Teacher Irreplaceability Declaration

Sarah Mitchell demonstrates advanced, transferable competency in D1.3. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D2 Extension Operation Capability

D2.1

Extension Design Capability

Core for A-type · Phase 2 · Prompt Engineering Log (≥8 iterations)

Sarah Mitchell demonstrates advanced, transferable competency in D2.1. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D2.2

Extension Evaluation Capability

Universal, esp. C-type · Phase 2 · Three Output Evaluation Reports

Sarah Mitchell demonstrates advanced, transferable competency in D2.2. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D2.3

Extension Customisation Capability

Core for B/C-type · Phase 2 · Customised Extension Artefact

Sarah Mitchell demonstrates advanced, transferable competency in D2.3. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D3 Extension-Integrated Teaching Practice

D3.1

Implementation of Human–AI Collaborative Teaching

A+B+C combined · Phase 3 · Video + Live Decision Log

Sarah Mitchell demonstrates advanced, transferable competency in D3.1. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D3.2

Guidance and Support for Students' Extension Use

Central C-type scenario · Phase 3 · Guidance Episode (video) + Student Work Evaluations

Sarah Mitchell demonstrates advanced, transferable competency in D3.2. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D3.3

Application of Learning Analytics

Core B-type capability · Phase 3 · Student Work Sample Evaluations

Sarah Mitchell demonstrates advanced, transferable competency in D3.3. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D4 Extension-Enabled Professional Development

D4.1

AI-Assisted Professional Reflection

Central C-type scenario · Phase 4 · Self-Analysis with Annotated AI Transcript

Sarah Mitchell demonstrates advanced, transferable competency in D4.1. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D4.2

Extension-Transfer Capability

Universal meta-capability · Phase 4 · New Tool Evaluation Report (timed)

Sarah Mitchell demonstrates advanced, transferable competency in D4.2. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D4.3

Extension Accountability Communication

B/C-type application · Phase 4 · Three Stakeholder Communications

Sarah Mitchell demonstrates advanced, transferable competency in D4.3. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D5 Extension Ethical Responsibility

D5.1

Ethics of Extension Purpose

High-risk in C-type · Phase 5 · Purpose Drift Audit + Revision Log

Sarah Mitchell demonstrates advanced, transferable competency in D5.1. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D5.2

Ethics of Extension Process

Universal across A/B/C · Phase 5 · Ethical Risk Memo

Sarah Mitchell demonstrates advanced, transferable competency in D5.2. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D5.3

Ethics of Extension Structure

Deeper B/C-type risks · Phase 5 · Ethical Audit + Revision Log

Sarah Mitchell demonstrates advanced, transferable competency in D5.3. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D6 Collective Agentic Practice

D6.1

Professional-Community Scrutiny

Primary site for Type F; relevant to E · Phase 6 · Professional-Community Evaluation (joint artefact)

Sarah Mitchell demonstrates advanced, transferable competency in D6.1. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D6.2

Institutional Voice and Escalation

Core for Types E/F · Phase 6 · Type-F Audit Memo + Institutional Escalation Draft

Sarah Mitchell demonstrates advanced, transferable competency in D6.2. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D6.3

Policy-Level Agency

Cross-cutting; primary for Type F · Phase 6 · Policy-Level Response

Sarah Mitchell demonstrates advanced, transferable competency in D6.3. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D7 Equity-Centred Extension Practice

D7.1

Recognition of Differential Extension Effects

General across A–F · Phase 7 · Differential-Effect Map

Sarah Mitchell demonstrates advanced, transferable competency in D7.1. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D7.2

Inclusive Extension Design

Central for Types A/C/D · Phase 7 · Inclusive Redesign + Training-Data Audit

Sarah Mitchell demonstrates advanced, transferable competency in D7.2. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D7.3

Distributed Anchoring Capacity

Cross-cutting; primary for Type C · Phase 7 · Anchoring-Capacity Cultivation Plan

Sarah Mitchell demonstrates advanced, transferable competency in D7.3. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D8 Assessment Redesign Under AI

D8.1

Assessment Validity Under AI

General A–F; acute for A and E · Phase 8 · Assessment Validity Audit

Sarah Mitchell demonstrates advanced, transferable competency in D8.1. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D8.2

Authentic Agentic Assessment

Core for Type C; general A–F · Phase 8 · Authentic Agentic Assessment Design + Revalidation Trial

Sarah Mitchell demonstrates advanced, transferable competency in D8.2. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

D8.3

AI-Inclusive Assessment Policy

Cross-cutting; primary for A and C · Phase 8 · Co-articulation Record + Shared Policy

Sarah Mitchell demonstrates advanced, transferable competency in D8.3. All evidence is deeply grounded in Mathematics with specific references to curriculum content, named student populations, and subject-specific pedagogical reasoning. Process traces (logs, transcripts, video timestamps) provide direct evidence throughout.

Evidence: Direct (×1)

Level 4 — Advanced

Evidence Log Summary

100 log entries across 6 types

LOG-A
Prompt Engineering

LOG-B
HAA Classification

LOG-C
Live Decision

LOG-D
Output Evaluation

LOG-E
Ethical Audit

LOG-F
Reflection Annotation

Task Work

Click any task to view the full draft in the workspace wizard

Day 1 Mapping the Extension System D1 · Foundations of Extended Cognition

Extension System Analysis

500–700 words, no AI access. Classify ≥5 teaching activities from the unit into A/B/C extension types. Each classification must cite the specific learning objective, class level, and what the teacher would do if AI were removed.

Draft · 5,845 ch ✓ Submitted No AI access

Subject-Specific Boundary Map

Three-column table — technical limits, contextual limits, value-judgment limits. Each row names a specific AI capability, a specific lesson activity, and why the limit matters in this subject at this level.

Draft · 5,779 ch ✓ Submitted

Teacher Irreplaceability Declaration

≥5 decisions in this unit that cannot be delegated to AI, each grounded in a subject-specific or relational reason. A history teacher's reasons must sound like history.

Draft · 7,930 ch ✓ Submitted

Mental Model Audit

Annotate 3 AI education marketing texts: mark accurate claims vs. overstatements about AI's nature or teacher agency. Cite specific phrases.

Draft · 4,424 ch ✓ Submitted

Day 2 Designing and Evaluating Extensions D2 · Extension Operation Capability

Prompt Engineering Log

≥8 annotated iterations. For each: state the pedagogical intent, full prompt, AI output summary, evaluation against the intent, and explicit revision rationale before the next attempt.

Draft · 11,688 ch ✓ Submitted

Three Output Evaluation Reports

Identify ≥1 hallucination, bias, or over-confident claim per AI-generated material. Explain why the error matters in this subject; write a corrected version. At least one evaluation must address a subtle error.

Draft · 4,998 ch ✓ Submitted

Customised Extension Artefact

Prompt template, system prompt, or task chain for a B- or C-type activity. Include pedagogical rationale, target HAA type, boundary conditions, and ≥1 tested failure mode.

Draft · 6,215 ch ✓ Submitted

Tool Selection Rationale

Compare two AI tools for one unit activity using the A/B/C taxonomy. Evidence of direct testing of both tools required.

Draft · 5,704 ch ✓ Submitted

Day 3 Teaching with Extensions D3 · Extension-Integrated Teaching Practice

25–30 min Video-Recorded Micro-Lesson

Implement ≥2 different HAA extension types. Screen visible when AI tools are used. ≥1 explicit mediation moment timestamped in the decision log.

Draft · 4,851 ch ✓ Submitted

Student Extension Guidance Episode

≥8 min within the lesson. Teach students how to think about AI as a cognitive extension in this subject. Address: extension type in use, subject-specific boundary conditions, ≥1 concrete override case.

Draft · 5,265 ch ✓ Submitted

Live Decision Log

≥8 timestamped entries. For each AI use, adjustment, override, or deliberate exclusion — record extension type, pedagogical rationale, retrospective assessment. ≥1 entry must record a deliberate exclusion with a subject-specific reason.

Draft · 7,475 ch ✓ Submitted

3 Student Work Sample Evaluations

Apply the Output Evaluation framework to AI-assisted student work. Identify AI errors/biases, name likely misconceptions, write feedback addressing the student's AI use — not just their work product.

Draft · 6,668 ch ✓ Submitted

Day 4 Learning about Learning D4 · Extension-Enabled Professional Development

AI-Assisted Self-Analysis

≥600 words. Upload Phase 1–3 artefacts to an AI assistant; ask it to identify patterns. Annotate every insight as Accepted/Modified/Rejected with subject-specific reasoning. ≥2 rejections must identify an echo-chamber effect.

Draft · 8,073 ch ✓ Submitted

New AI Tool Evaluation Report

Evaluate an assigned tool not previously used. ≤45 min, timed. Apply A/B/C taxonomy + boundary analysis. State extension type(s), boundary conditions, pedagogical potential, primary risk, adoption recommendation.

Draft · 3,672 ch ✓ Submitted

Three Stakeholder Communications

Student, parent, administrator. Each must specify: extension type, decision rationale, AI's limits. Language must be demonstrably different across audiences.

Draft · 11,889 ch ✓ Submitted

3-Month PD Plan

Code each goal as AI-proposed / self-identified / modified-from-AI. Include a verification method for each. ≥1 goal addresses a specific Phase 1–3 limitation.

Draft · 3,403 ch ✓ Submitted

Day 5 Auditing the Extension D5 · Extension Ethical Responsibility

Three-Layer Ethical Audit

Purpose ethics, process ethics, structural ethics. ≥2 risks per layer, each naming the exact lesson activity, affected student population, and mechanism by which harm could occur.

Draft · 7,512 ch ✓ Submitted

Ethical Risk Memo

800–1100 words. For each risk: Risk name → Mechanism → Affected students → Mitigation action with timeline → Whether revised into the unit plan or accepted with documented rationale.

Draft · 7,982 ch ✓ Submitted

Purpose Drift Audit

Review every AI use decision in Phases 1–4. For each efficiency-driven decision: either revise or provide a principled argument.

Draft · 7,083 ch ✓ Submitted

Revised Unit Plan + Revision Log

For each change: cite the audit finding, state what changed, explain expected effect on student development. ≥1 revision must change tool/platform — not just use behaviour.

Draft · 7,815 ch ✓ Submitted

Day 6 Anchoring What One Cannot Anchor Alone D6 · Collective Agentic Practice

Ambient-Extension (Type F) Audit Memo

500–800 words. Pick ONE ambient (Type F) AI feature in your school's platforms — LMS content recommender, adaptive sequencing, notification/nudge system, analytics dashboard that frames student identity. Name the mechanism, the affected student population, what it is silently doing to student cognition, and why it is invisible by design.

Draft · 1,321 ch ✓ Submitted No AI access

Professional-Community Evaluation

Co-author a shared evaluation of ONE AI tool with ≥1 colleague (in-person or async, same subject or cross-subject). The artefact must include: a recorded disagreement, how it was resolved (or why it remains open), and one insight that neither of you had alone.

Draft · 1,333 ch ✓ Submitted

Institutional Escalation Draft

Written escalation to leadership (HoD, VP, curriculum lead, tech coordinator) about ONE AI-related risk at your institution that individual teachers cannot mitigate. Must specify: the risk, the mechanism, affected parties, the concrete ask, the timeline, and the evidence base.

Draft · 1,757 ch ✓ Submitted

Policy-Level Response

Respond substantively to ONE live or recent AI-in-education policy item — a school district consultation, a national curriculum comment period, an internal PD policy, a professional-association position paper. ≥300 words. Must take a position, not summarise.

Draft · 1,756 ch ✓ Submitted

Day 7 Who Gets to Anchor D7 · Equity-Centred Extension Practice

Differential-Effect Map

For each major AI use in your unit, map its differential effect across ≥3 named learner groups in your actual class. Each row: AI use, learner group (EAL / SEN / neurodivergent / culturally under-represented / socioeconomically disadvantaged / etc.), the differential effect, and the evidence you base it on.

Draft · 2,976 ch ✓ Submitted

Inclusive Redesign

Take ONE row from p7t1 where you identified a disadvantaged group, and redesign the AI activity so that group is no longer structurally disadvantaged. Document the original, the redesign, and a theory of change linking the two.

Draft · 1,522 ch ✓ Submitted

Training-Data Representation Audit

Pick ONE AI output in your subject (a generated example, explanation, or scenario) and audit it for representational gaps. Document ≥3 specific gaps — whose contexts/dialects/histories are present or absent — and trace each to a pedagogical consequence in your unit.

Draft · 2,879 ch ✓ Submitted

Anchoring-Capacity Cultivation Plan

Plan how you will cultivate anchoring capacity in learners who came to your class with less AI exposure or lower confidence exercising agency over AI. Name specific learners (initials only), specific interventions, and how you will know it's working.

Draft · 2,919 ch ✓ Submitted

Day 8 Rehabilitating Assessment Validity D8 · Assessment Redesign Under AI

Assessment Validity Audit

≥2 existing assessments (yours or departmental). For each: what was it designed to measure? what can AI now produce that would look identical to that capacity being exercised? what is the failure mechanism that makes the assessment no longer a valid signal?

Draft · 2,802 ch ✓ Submitted

Authentic Agentic Assessment Design

Design ONE new assessment that is STRUCTURALLY non-evidential for AI-substituted output — oral examination, in-class writing, process portfolio, scaffolded real-time construction, iterative review with explained reasoning, or think-aloud protocol. Specify: construct measured, form, why AI substitution fails to provide valid evidence for this form, scoring approach.

Draft · 1,570 ch ✓ Submitted

AI-Inclusive Policy Co-articulation

Run ONE conversation with your class (or a focal group ≥4 students) about legitimate vs. illegitimate AI use in your subject. Record what they named as legitimate / illegitimate / ambiguous. Produce a short shared policy document.

Draft · 1,893 ch ✓ Submitted

Revalidation Trial

Run p8t2's new assessment with ≥5 students. Write up what the evidence actually showed: did the form discriminate agentic work from AI-substituted output? what surprised you? what revision do you propose?

Draft · 2,320 ch ✓ Submitted

Teacher Context

Three students with IEPs for math learning disabilities. Five ELL students — two Mandarin-speaking, three Spanish-speaking. Strong visual learners overall. Currently transitioning from concrete manipulatives to abstract representations.