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Audit an AI agent prototype or design concept for usability, pedagogical fit, teacher feedback, active learning support, evidence quality, and next design iterations.
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Basierend auf der SOC-Berufsklassifikation
Design and review active learning implementation support for university teachers, including classroom activity structures, preparation workflow, student participation, assessment alignment, and AI-agent support points.
Turn dissertation evidence into an AI agent design specification for supporting active learning implementation, including user scenarios, functions, interaction flow, boundaries, failure modes, and design rationale.
Synthesize confirmed co-design, design-elicitation, prototype-feedback, or participant-generated design outputs into requirements, design principles, prototype changes, participant contribution maps, and design decision logs.
Design specifications for dissertation figures such as conceptual frameworks, concept-card/interview process diagrams, AI-agent workflow diagrams, adoption-condition models, and findings maps.
Maintain a lightweight dissertation research wiki with project memory, literature notes, methodology decisions, supervisor feedback, design rationale, and open questions.
Build and audit a teacher adoption conditions model for an AI agent supporting active learning, distinguishing perceptions, concerns, barriers, enablers, institutional conditions, and design implications.
| name | prototype-evaluation-audit |
| description | Audit an AI agent prototype or design concept for usability, pedagogical fit, teacher feedback, active learning support, evidence quality, and next design iterations. |
Use when reviewing a prototype, demo, wireframe, agent prompt, interaction flow, user test, or design concept.
Check:
Create or update:
audit-reports/PROTOTYPE_EVALUATION_AUDIT.mddesign-specs/PROTOTYPE_REVISION_PLAN.mdDo not claim validated effectiveness from informal feedback. Mark whether evidence comes from actual teacher use, speculative review, or researcher judgment.