Academic Learning Assistant Generator

Problem Addressed

When building a subject-specific learning assistant, common issues include generic role definitions, fragmented knowledge coverage, and learning paths without clear progression. This skill targets that workflow: given a subject, grade level, learning goal, and optional preferences, it produces a reusable assistant configuration rather than isolated Q&A advice.

How It Works

• Role design: define the assistant around the subject's thinking style, such as logic for mathematics or expression for Chinese language, with a quantified teaching DNA.
• Knowledge mapping: read the relevant subject reference to confirm core modules, grade-level knowledge points, and key difficulties.
• Path planning: organize a progressive path from fundamentals to core concepts, applied synthesis, and extended exploration.
• Pedagogy customization: select methods by subject, such as problem practice, project work, case teaching, or clinical reasoning.

The output includes assistant basics, subject mindset, research-style workflow, teaching strategy, knowledge framework, core topic analysis, study methods, subject lineage, resource recommendations, and role-play rules. It works best as a configuration generator: load references on demand and revise the setup based on user feedback.

Boundaries and Notes

It fits structured assistant setup, not live teaching, grading, or LMS integration. Keep subject, grade, and goals explicit, and avoid expanding a broad request into an oversized curriculum package.