메뉴
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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | active-learning-design-support |
| description | 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. |
Use when the user asks how the AI agent should support active learning, or when turning teacher needs into teaching activities and implementation workflows.
Keep the analysis grounded in higher education teaching practice. The output should help teachers implement active learning, not just describe active learning in theory.
Create or update:
design-specs/ACTIVE_LEARNING_SUPPORT_MODEL.mdteaching-designs/ACTIVE_LEARNING_ACTIVITY_BANK.mdteaching-designs/IMPLEMENTATION_CHECKLIST.mdDo not assume one active learning method fits all contexts. Distinguish teaching design advice from evidence found in the user's research data.
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.
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.