A new adaptive LLM tutoring system uses subject-aware prompting to enhance student engagement. It outperforms static models in simulation and shows real-world effectiveness, reducing interactions by 3 turns and increasing exercise conversion rates to 28.1% with a stochastic strategy.
arxiv
arXiv cs.AI
·
6d ago
·
research
Adaptive LLM Tutoring Improves Engagement and Efficiency
from English
Benchmarks
| Benchmark | Model | Score |
|---|---|---|
| AIME 2025 | adaptive prompt selection mechanism | 28.1% |
| AIME 2024 | adaptive prompt selection mechanism | 19.6% |
| AIME 2024 | greedy router | 19.1% |
| SWE-bench Verified | router | 0.69null |
| SWE-bench Verified | baseline | 0.65null |