aiqbench analysis
This research introduces DICE, a framework that separates the decision to intervene from generating responses in LLM tutoring systems, addressing a common inefficiency where models over-respond to student inputs. By defining an Intervention Value metric and optimizing for selective engagement, the approach significantly reduces unnecessary turns while maintaining or improving pedagogical effectiveness on math problems. This advancement highlights critical progress in aligning large language model behavior with genuine educational utility rather than mere conversational fluency.
DICE: Decoupling Capability from Intervention Necessity in LLM Tutoring
arXiv:2610.04825v1 Announce Type: new Abstract: Fluent guidance is not the same as useful intervention. LLM tutors are typically trained to generate the next teacher utterance, implicitly assuming that every student turn warrants a response. However, our experiments indicate that this conflates tutoring capability (what to say) with intervention necessity (whether to say it). We introduce DICE, a framework that decouples intervention decisions from response generation by first selecting an explicit pedagogical action. To calibrate this action selection policy, we define Intervention Value (IV), a rollout-grounded counterfactual metric that compares each action against non-intervention. IV shows that many prescribed interventions provide little or no marginal benefit. We further introduce DICE-Bench, a multi-variant math tutoring benchmark with skill-preserving problem variants for session-level evaluation. Using IV-weighted and KL-regularized policy optimization, DICE learns to intervene selectively while preserving tutoring effectiveness. In simulated tutoring sessions, DICE reduces the over-intervention rate to near zero while guiding students to correct solutions in approximately 3-4 fewer turns on average than existing Socratic tutoring baselines.
Abstract:Fluent guidance is not the same as useful intervention. LLM tutors are typically trained to generate the next teacher utterance, implicitly assuming that every student turn warrants a response. However, our experiments indicate that this conflates tutoring capability (what to say) with intervention necessity (whether to say it). We introduce DICE, a framework that decouples intervention decisions from response generation by first selecting an explicit pedagogical action. To calibrate this action selection policy, we define Intervention Value (IV), a rollout-grounded counterfactual metric that compares each action against non-intervention. IV shows that many prescribed interventions provide little or no marginal benefit. We further introduce DICE-Bench, a multi-variant math tutoring benchmark with skill-preserving problem variants for session-level evaluation. Using IV-weighted and KL-regularized policy optimization, DICE learns to intervene selectively while preserving tutoring effectiveness. In simulated tutoring sessions, DICE reduces the over-intervention rate to near zero while guiding students to correct solutions in approximately 3-4 fewer turns on average than existing Socratic tutoring baselines.
Artificial Intelligence (cs.AI)
arXiv:2610.04825 [cs.AI]
(or arXiv:2610.04825v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.04825 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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