{"ID":2847975,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.26322","arxiv_id":"2510.26322","title":"SCRIBE: Structured Chain Reasoning for Interactive Behaviour Explanations using Tool Calling","abstract":"Language models can be used to provide interactive, personalized student feedback in educational settings. However, real-world deployment faces three key challenges: privacy concerns, limited computational resources, and the need for pedagogically valid responses. These constraints require small, open-source models that can run locally and reliably ground their outputs in correct information. We introduce SCRIBE, a framework for multi-hop, tool-augmented reasoning designed to generate valid responses to student questions about feedback reports. SCRIBE combines domain-specific tools with a self-reflective inference pipeline that supports iterative reasoning, tool use, and error recovery. We distil these capabilities into 3B and 8B models via two-stage LoRA fine-tuning on synthetic GPT-4o-generated data. Evaluation with a human-aligned GPT-Judge and a user study with 108 students shows that 8B-SCRIBE models achieve comparable or superior quality to much larger models in key dimensions such as relevance and actionability, while being perceived on par with GPT-4o and Llama-3.3 70B by students. These findings demonstrate the viability of SCRIBE for low-resource, privacy-sensitive educational applications.","short_abstract":"Language models can be used to provide interactive, personalized student feedback in educational settings. However, real-world deployment faces three key challenges: privacy concerns, limited computational resources, and the need for pedagogically valid responses. These constraints require small, open-source models tha...","url_abs":"https://arxiv.org/abs/2510.26322","url_pdf":"https://arxiv.org/pdf/2510.26322v1","authors":"[\"Fares Fawzi\",\"Vinitra Swamy\",\"Dominik Glandorf\",\"Tanya Nazaretsky\",\"Tanja Käser\"]","published":"2025-10-30T10:17:05Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Language Model\",\"LoRA\"]","has_code":false}