Pathways for the development of educational intelligences
ReCall-based implementation of educational scenarios:
- Toolset Configuration::
- Subject Knowledge Base API (math formulas/historical events, etc.)
- Error book database
- Solution Step Validator
- Data preparation::
- Using prepare_musique_recall.py to generate subject quiz data
- Injecting the course outline as a constraint
- Labeling typical error patterns as negative samples
- System deployment::
- Open HTTP interfaces with the -port parameter
- Front-end integration with Gradio Interactive Interface
- Use -context-length to ensure that long conversations are memorized
Typical application scenario: When students ask geometric proof questions, the system automatically calls theorem libraries + graphing tools + step checkers to guide the solution in stages.
This answer comes from the articleReCall: training large models for tool-call inference via reinforcement learningThe
May not be reproduced without permission:AI productivity tools " How can the ReCall framework be applied to develop an intelligent tutoring system in the education domain?
