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AI Research
arXiv CS.AI·Jun 10·5 min read
Detecting Knowledge Gaps from Conversational AI Interactions Using Curriculum Prerequisite Graphs
TL;DR
Students often ask numerous questions to conversational AI teaching assistants, revealing potential knowledge gaps. A few-shot text classifier was developed to map these questions to curriculum topics using a prerequisite knowledge graph.
✦ Why It Matters
Engineers can leverage this approach to identify and address knowledge gaps in educational settings effectively.
Key Takeaways
- Implement a few-shot text classifier for mapping student questions.
- Utilize interaction logs to identify knowledge gaps in course material.
- Correlate question volume with student-reported difficulties.
- Focus instructional efforts on topics with high student confusion.
How It Works
The pipeline employs a few-shot text classifier to categorize student questions based on a prerequisite knowledge graph. This graph, extracted from GPT-4, outlines the relationships between course concepts, allowing the classifier to effectively map questions to relevant curriculum topics.