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The paper presents a pipeline that maps student questions from a conversational AI teaching assistant to curriculum topics using a few-shot text classifier and a GPT-4-extracted prerequisite knowledge graph, achieving 80% accuracy on 1,340 question events and correlating with self-reported difficulty.
This paper proposes a three-stage diagnostic framework to identify why offline model selectors fail to beat the best single model, applying it to dropout prediction on edX clickstream data. The study finds that the bottleneck is local representational ambiguity rather than learner choice or distribution shift, recommending state redesign or new data collection over further algorithm tuning.