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20242026
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cs.CL2026

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…

cs.CL2026

Opportunities and Challenges of LLMs in Education: An NLP Perspective

Sowmya Vajjala, Bashar Alhafni, Stefano Bannò +3

Interest in the role of large language models (LLMs) in education is increasing, considering the new opportunities they offer for teaching, learning, and assessment. In this paper,…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models

Kaushal Kumar Maurya, KV Aditya Srivatsa, Ekaterina Kochmar

Large language models (LLMs) have been widely adopted due to their remarkable performance across various applications, driving the accelerated development of a large number of dive…