Papers with intelligent tutoring systems
Pre-Training BERT on Domain Resources for Short Answer Grading (D19-1)
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| Challenge: | Pre-trained contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data. |
| Approach: | They propose to augment domain-specific data by using labeled short answering grading data for further enhancement of the pre-trained language model. |
| Outcome: | The proposed model can be enhanced by augmenting data from domain-specific resources like textbooks and labeled short answering grading data. |
CogNet-KG: Empowering Tutoring Dialogues with a Cognitively-Structured Knowledge Graph for STEM Learning (2026.findings-acl)
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| Challenge: | Educational knowledge graphs are a critical component of intelligent tutoring systems that are structured around cognitive principles and provide support for interactive teaching. |
| Approach: | They propose a cognitively-structured large-scale knowledge graph for STEM learning that models nearly 500 core concepts across five subjects with various cognitively grounded relations corresponding to specific learning objectives. |
| Outcome: | The proposed model generates a high-quality tutoring dialogue dataset CogDialogue-QA and a specialized tutorial LLM that internalizes this structured pedagogical reasoning. |
Revita: a Language-learning Platform at the Intersection of ITS and CALL (L18-1)
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| Challenge: | Existing language-learning tools do not address the fundamental requirements of language learners and teachers. |
| Approach: | They propose a free-to-use platform for language learning beyond the beginner level . they outline the established desiderata of CALL and ITS . |
| Outcome: | The proposed platform supports language learning beyond the beginner level. |
Linguistic Alignment Predicts Learning in Small Group Tutoring Sessions (2025.findings-emnlp)
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| Challenge: | Cognitive science offers rich theories of learning and communication, yet these are often difficult to operationalize at scale. |
| Approach: | They investigate linguistic alignment in a longitudinal dataset of real-world tutoring interactions and associated student test scores. |
| Outcome: | The proposed method can be applied to real-world tutoring interactions and student test scores. |
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are being developed to provide personalized tutoring systems that can understand and adapt to individual student needs. |
| Approach: | They propose to train large language models on student-tutor dialogue datasets to understand student behavior and evaluate their performance across multiple benchmarks. |
| Outcome: | The proposed model performance declines across multiple benchmarks, indicating a broad impact on their capabilities when trained to model student behavior. |