Representing expertise accelerates learning from pedagogical interaction data
Work in cognitive science and artificial intelligence has suggested that exposing learning agents to traces of interaction between multiple individuals can improve performance in a variety of settings, yet it remains unknown which features of interactions contribute to this improvement. We examined the factors that support the effectiveness of interaction data, using a controlled paradigm that allowed us to precisely operationalize key distinctions between interaction and an expert acting alone. We generated synthetic datasets of simple interactions between an expert and a novice in a spatial navigation task, and then trained transformer models on those datasets, evaluating performance af...
| Authors | Dhara Yu, Karthikeya Kaushik, Bill D. Thompson |
|---|---|
| Primary category | cs.CL |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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