Averroes at BEA 2025 Shared Task: Verifying Mistake Identification in Tutor, Student Dialogue

This paper presents the approach and findings of Averroes Team in the BEA 2025 Shared Task Track 1: Mistake Identification.Our system uses the multilingual understanding capabilities of general text embedding models.Our approach involves full-model fine-tuning, where both the pre-trained language model and the classification head are optimized to detect tutor recognition of student mistakes in educational dialogues.This end-to-end training enables the model to better capture subtle pedagogical cues, leading to improved contextual understanding.Evaluated on the official test set, our system achieved an exact macro-F 1 score of 0.7155 and an accuracy of 0.8675, securing third place among the participating teams.These results underline the effectiveness of task-specific optimization in enhancing model sensitivity to error recognition within interactive learning contexts.