Co-create micro-intervention repertoires with generative AI through a seed-request-refine prompt sequence, with the instructor curating output¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article proposes a three-step AI collaboration protocol: seed the context (subject, audience, emotion, situation), request interventions that "a) validate the feeling, b) normalize the struggle as part of the learning process, and c) suggest a very small next step," then refine for discipline-specific metaphors. The instructor's role is to "curate, adapt, and personalize the AI's output" so it matches their authentic voice and knowledge of the cohort.

Design Implications¶

Context¶

Requirements¶

• The instructor must curate, adapt, and personalize the AI's output to align with their authentic voice and precise knowledge of the student cohort

Constraints¶

• The article frames the process as collaborative brainstorming, not outsourcing

Target Learners¶

• Higher education instructors developing their intervention repertoire

Target Learning Goals¶

• Scalable development of affective pedagogical content knowledge

Related Strategies¶

• Teachers iteratively refine LLM-simulated student ideas with classroom context before instructional use
• Use the DOT Framework's Empathize and Test/Feedback stages as practical checklists, and build professional learning communities around shared prompting and critique practices
• Run a pre-class Scout ritual: identify 2-3 cognitive-emotional bottlenecks, prepare 1-2 interventions each, and anticipate logistical failures
• Educators gamify learning materials or create learning games using generative AI with iterative, phased prompting

Examples¶

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Key Sources¶

• François PARLANT. (2025). Can You Feel It? A Practical Guide to Emotional Micro-Interventions for Higher Education Teachers (and Others). https://osf.io/tuw2a/overview