BD at BEA 2025 Shared Task: MPNet Ensembles for Pedagogical Mistake Identification and Localization in AI Tutor Responses

This paper presents an MPNet ensemble system for the BEA 2025 Shared Task that automatically classifies AI tutor responses in educational dialogues across two tracks: whether tutors correctly identify student mistakes (Track 1) and whether they locate the mistakes (Track 2). The system uses fine-tuned Transformer models with grouped cross-validation and hard-voting ensemble to achieve macro-F1 scores of 0.7110 and 0.5543 on the respective tracks.

We present Team BD's submission to the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors, under Track 1 (Mistake Identification) and Track 2 (Mistake Location). Both tracks involve three-class classification of tutor responses in educational dialogues - determining if a tutor correctly recognizes a student's mistake (Track 1) and whether the tutor pinpoints the mistake's location (Track 2). Our system is built on MPNet, a Transformer-based language model that combines B

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