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

This paper presents an MPNet-based ensemble system for automatically evaluating AI tutor responses in educational dialogues, specifically assessing whether tutors correctly identify and locate student mistakes across two classification tasks at the BEA 2025 Shared Task.

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

Source

Framework Categories

Tool Types

Tags