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17 papers
AITutor-EvalKit: Exploring the Capabilities of AI Tutors
Numaan Naeem, Kaushal Kumar Maurya, Kseniia Petukhova +1
We present AITutor-EvalKit, an application that uses language technology to evaluate the pedagogical quality of AI tutors, provides software for demonstration and evaluation, as we…
Pedagogy-driven Evaluation of Generative AI-powered Intelligent Tutoring Systems
Kaushal Kumar Maurya, Ekaterina Kochmar
The interdisciplinary research domain of Artificial Intelligence in Education (AIED) has a long history of developing Intelligent Tutoring Systems (ITSs) by integrating insights fr…
LLMs cannot spot math errors, even when allowed to peek into the solution
KV Aditya Srivatsa, Kaushal Kumar Maurya, Ekaterina Kochmar
Large language models (LLMs) demonstrate remarkable performance on math word problems, yet they have been shown to struggle with meta-reasoning tasks such as identifying errors in…
UNVEILING: What Makes Linguistics Olympiad Puzzles Tricky for LLMs?
Mukund Choudhary, KV Aditya Srivatsa, Gaurja Aeron +6
Large language models (LLMs) have demonstrated potential in reasoning tasks, but their performance on linguistics puzzles remains consistently poor. These puzzles, often derived fr…
Findings of the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors
Ekaterina Kochmar, Kaushal Kumar Maurya, Kseniia Petukhova +3
This shared task has aimed to assess pedagogical abilities of AI tutors powered by large language models (LLMs), focusing on evaluating the quality of tutor responses aimed at stud…
Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension?
KV Aditya Srivatsa, Kaushal Kumar Maurya, Ekaterina Kochmar
Large Language Models (LLMs) are increasingly used as proxy students in the development of Intelligent Tutoring Systems (ITSs) and in piloting test questions. However, to what exte…