183건 · 관련도순
Diagnose motivation problems in a task using self-determination theory and redesign for autonomy, competence, and relatedness. Use when students are disengaged, resistant, or going through the motions.
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Surface beliefs, assumptions, stories, and values shaping a system. Use when deeper mental models need examining with care and evidence.
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Design a mastery experience sequence that systematically builds student confidence in a skill they avoid. Use when students say 'I can't do this', avoid tasks, or show learned helplessness.
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Design structured perspective-taking activities with anti-projection guardrails. Develops genuine understanding of complexity across history, social sciences, and literature — not performed empathy.
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After scaffolded practice, run an unassisted check with no AI help. Use to distinguish supported performance from what the learner can currently do independently.
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Generate structured reflection prompts for a specific teaching experience or professional learning challenge. Use when debriefing lessons, journaling, or preparing for coaching conversations.
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Present pathway options and orchestrate place-based curriculum design from a local place, curriculum requirement, or community issue. Use when place should become a primary text for learning.
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Map control, influence, and concern after systems analysis. Use when students need wise agency without being made responsible for everything.
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After the learner demonstrates understanding of a concept, present near-transfer and far-transfer challenges. Use to test whether learning is portable or task-specific — this is what separates understanding from familiarity.
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Generate scaffolds supporting student self-regulation across planning, monitoring, and evaluation phases. Use when students struggle to manage their own learning during extended or independent tasks.
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Design a diagnostic hinge question that reveals whether students understand enough to move on. Use when planning key checkpoints mid-lesson during explicit or direct instruction.
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Design a complete lesson study cycle from research question through collaborative planning to research lesson. Use when planning jugyou kenkyuu or collaborative teacher inquiry into practice.
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Generate follow-up teaching moves that extend student thinking after a specific classroom response. Use when a student says something worth exploring and the teacher wants to deepen the dialogue.
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Design a focused lesson observation protocol with specific look-fors and evidence collection methods. Use when planning peer observations, coaching visits, or developmental classroom visits.
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Design the deeper patterns, structures, artefacts, and mental models needed to grow a desired event. Use when a class imagines a better future.
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Review the week using accumulated session evidence — retrieval rates, hint depths, calibration accuracy, transfer and unassisted results. The learner identifies patterns and sets a strategy goal. Use weekly or after a multi-session period.
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Before any explanation or answer, require the learner to produce a free-recall attempt and confidence rating. Use when a student wants help understanding or reviewing a topic — this skill ensures the AI works from what the learner already knows.
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Design a practitioner inquiry cycle from research question through data collection to evidence-informed action. Use when starting action research, teacher-led investigation, or professional inquiry.
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Select evidence-based study strategies matched to material type, learning goal, and student habits. Use when advising students on revision techniques, homework, or independent study approaches.
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Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.
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Develop pedagogical content knowledge for a topic by surfacing common misconceptions and effective representations. Use when preparing to teach unfamiliar content or improving teaching of hard topics.
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After any AI-generated explanation, require the learner to identify one place it could be wrong, one thing to check, and one source to consult. Builds epistemic vigilance — treats AI output as a claim to evaluate, not truth to absorb.
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Require the learner to explain a concept in their own words before the AI evaluates or extends it. Ensures the AI works from the learner's understanding rather than providing an explanation from scratch.
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Coordinates UDL and differentiation tools through a universal-first hierarchy: barrier removal before targeted differentiation before individualised accommodation. Use when planning accessible learning.
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