PIVOT: Preference-based Intervention Vectors for Pedagogical Tutor Steering
LLMs are increasingly used for conversational tutoring, but effective tutoring requires more than correct answers. Tutors must choose when to scaffold reasoning, hint, give feedback, explain, or invite reflection. Existing prompting and training methods improve pedagogical alignment, but lack reliable inference-time control over pedagogical strategies. We introduce PIVOT, an activation-steering framework that learns preference-based intervention vectors online for frozen LLM tutors. PIVOT uses a seven-category tutor-move taxonomy and a generate-label-optimise loop, where a human-validated LLM judge identifies target and confusable non-target moves to construct preference pairs for multi-l...
| Authors | Fares Fawzi, Jiaxu Zhao, Tanya Nazaretsky et al. |
|---|---|
| Primary category | cs.HC |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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