Year: 2025
Type: conference-paper
Abstract: This paper presents our system submission to the Building Educational Applications (BEA) 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors.The task evaluates multiple dimensions of AI tutor responses within student-teacher educational dialogues, including mistake identification, mistake location, providing guidance, and actionability.Our approach leverages transformer-based models (Vaswani et al., 2017), primarily DeBERTa and RoBERTa, and incorporates ordinal regression, threshold tuning, oversampling, and multi-task learning.Our best-performing systems are capable of assessing tutor response quality across all tracks.This highlights the effectiveness of tailored transformer architectures and pedagogically motivated training strategies for AI tutor evaluation.
Authors: Trishita Saha, Shrenik Ganguli, Maunendra Sankar Desarkar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4412889221