The rapid adoption of AI in educational technology is changing learning settings, making the thorough evaluation of AI tutor pedagogical performance is quite important for promoting student success. This paper describes our solution for the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered tutors, which assesses tutor replies over several pedagogical dimensions. We developed transformer-based approaches for five diverse tracks: mistake identification, mistake location, providing guidance, actionability, and tutor identity prediction using the MRBench dataset of mathematical dialogues. We evaluated several pre-trained models including DeBERTa-V3, RoBERTa-Large, SciBERT, and EduBERT. Our approach addressed class imbalance problems by incorporating strategic fine-tuning with weighted loss functions. The findings show that, for all tracks, DeBERTa architectures have higher performances than the others, and our models have obtained in the competitive positions, including 9th of Tutor Identity (Exact F1 of 0.8621), 16th of Actionability (Exact F1 of 0.6284), 19th of Providing Guidance (Exact F1 of 0.4933), 20th of Mistake Identification (Exact F1 of 0.6617) and 22nd of Mistake Location (Exact F1 of 0.4935). The difference in performance over tracks highlights the difficulty of automatic pedagogical evaluation, especially for tasks whose solutions require a deep understanding of educational contexts. This work contributes to ongoing efforts to develop robust automated tools for assessing.

Despite being a popular language in the world, the Bengali language lacks in having a good wordnet. This restricts us to do NLP related research work in Bengali. Most of the today’s wordnets are developed by following expand approach. One of the key challenges of this approach is the cross-lingual word-sense disambiguation. In our research work, we make semantic relation between Bengali wordnet and Princeton WordNet based on well-established research work in other languages. The algorithm will derive relations between concepts as well. One of our key objectives is to provide a panel for lexicographers so that they can validate and contribute to the wordnet.