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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.
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Based on SOC occupation classification
| name | teacher-adoption-modeling |
| description | 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. |
Use when the user asks to analyze teacher perceptions, concerns, adoption conditions, barriers, enablers, readiness, or implementation conditions.
Keep these categories separate:
Create or update:
models/TEACHER_ADOPTION_MODEL.mdmodels/ADOPTION_CONDITIONS_MATRIX.mdfigures/adoption-conditions-model.mmdDo not flatten teacher concerns into generic "resistance". Do not imply causal relationships unless the study design supports them.
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.
Audit an AI agent prototype or design concept for usability, pedagogical fit, teacher feedback, active learning support, evidence quality, and next design iterations.