exercise-designer

Design evidence-based exercises by defining learning objectives first, then use this Skill to automatically generate varied exercise types—fill-in-blank, debug-this, build-from-scratch, extend-code—with built-in spaced repetition and retrieval practice principles for Python concepts.

Generate multiple exercise formats including fill-in-blank activities, debugging tasks, build-from-scratch projects, code-extension problems, and AI-collaborative exercises, each tied to explicit learning objectives with difficulty progression.

Yes. Beyond generation, this Skill evaluates existing exercises against cognitive science principles—spaced repetition, retrieval practice, pedagogical soundness—and provides rubric generation to assess exercise quality against learning objectives.

No. The Skill embeds cognitive science principles—spaced repetition, retrieval practice, evidence-based design—automatically, so educators focus on learning objectives while the system handles pedagogically sound exercise creation.

Create exercises that teach students to work with AI as co-learning partners, generating exercise types and success criteria that explicitly integrate AI interaction into the learning workflow for Python education.

Python environment setup and knowledge of your learning objectives. Use templates provided by the Skill to map objectives to exercises, then generate varied practice activities with built-in evaluation criteria and rubrics.