FATE (FLC AI Tutor Evaluator), a specialized 8B-parameter language model designed to evaluate AI tutors, is introduced, which assesses pedagogical ability across Mistake Identification, Mistake Location, Guidance, and Actionability.
Abstract
The rapid integration of Large Language Models (LLMs) into K-12 and higher education has outpaced the development of reliable methods for evaluating their pedagogical quality. As the research community starts to explore the space of automating evaluation of AI tutors, we introduce FATE (FLC AI Tutor Evaluator), a specialized 8B-parameter language model designed to evaluate AI tutors. Aligned with the four core evaluation tracks from the BEA 2025 Shared Task, our model assesses pedagogical ability across Mistake Identification, Mistake Location, Guidance, and Actionability. Because pedagogical evaluation is a specialized task with limited labeled data, we leverage knowledge distillation from a frontier LLM to generate additional supervision, yielding absolute performance gains up to 22.63 percentage points. Finally, we demonstrate FATE's utility as an automated evaluator by benchmarking instructional responses generated by popular commercial models, including ChatGPT, Claude, Gemini, and DeepSeek. On average, we have found that Gemini 2.5 Flash perfomed best (82.88%), then ChatGPT 5.5 Instant (80.75%), followed by DeepSeek V4 Flash (80.13%) and Claude Sonnet 4.6 (74.00%).
CSTutorBench is introduced, a benchmark for evaluating language models as CS tutors in VEX VR, a block-based robotics environment, and preliminary findings reveal that models perform well on surface-level criteria such as vocabulary and tone but struggle with deeper pedagogical behaviors.
A simulation-based textual analysis of prompt design evaluates a frontier large language model as a tutor across 60 scripted sessions on a single topic and point to dynamic, dialogue-aware prompting alongside explicit SRL scaffolding.
Kendall Hartley, Fabiola Sáez-Delgado, Javier Mella-Norambuena· Future Internet· 0 citations
EduMind is introduced, a unified tutoring and assessment platform designed around a dual-track evaluation model that demonstrates how assessment and tutoring can be unified into a seamless workflow, and remained operationally stable throughout all testing phases.
Dhyan Gowda, M. Aruna, P. Prasad et al.· International Journal of Sci...· 0 citations
A novel approach to Intelligent Tutoring Systems (ITS) is presented by integrating Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to enable dynamic personalization in educational contexts by implementing a three-layered architecture combining semantic retrieval mechanisms with generative AI capabilities.
Kuyoro Afolashade, N. Uchenna, Akinwunmi Damilare· British journal of computer,...· 0 citations
Agentic tutoring systems increasingly rely on large language models (LLMs) to generate real-time feedback, yet risky outputs, such as answer leakage, contradiction, and hallucination, can directly affect learning. Answer leakage is a central concern in this setting. It exposes problem solutions to students and can be verified through human annotation. This article presents a pedagogical style-conditioned audit protocol for automated evaluators that detect answer leakage in LLM-generated tutoring feedback before deployment. Using 1100 mathematics problems from the MATH dataset and 4400 feedback items generated under Baseline, Direct, Socratic, and Encouragement styles, we compare rule-based, embedding-based, natural language inference-based, and two LLM-as-a-Judge evaluators from different model families (Qwen2.5-32B and Gemma3-27B) and validate them against a 314-item human-annotated gold standard. The results show that pedagogical style changes how answer-leakage signals appear in feedback and how reliably automated evaluators detect them. Rule-based and LLM-Judge evaluators that perform well under Baseline, Direct, and Encouragement show consistent degradation under Socratic feedback. The primary LLM-Judge (Qwen2.5-32B) achieves overall F1 = 0.726 but drops to 0.427 on Socratic items, and a second LLM-Judge (Gemma3-27B) reproduces this degradation with Socratic F1 = 0.342. The four factorial prompt-sensitivity variants reach overall F1 up to 0.844, but their Socratic F1 remains at or below 0.390. These findings show that automated tutoring-feedback auditing must be style-specific, prompt-calibrated, and human-grounded before deployment.
Wenzhuo Song, Xuechen Wang, Shuang Liang et al.· IEEE Transactions on Learnin...· 0 citations
The results suggest that learner- and curriculum-aware alignment may matter more for effective tutoring than model category alone, and that such alignment is both measurable and improvable.
Benjamin Barlog, Hudson Craig, Ze-Dong Peng· IEEE International Conferenc...· 0 citations
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