Automated Knowledge Component Generation and Interpretable Knowledge Tracing in Coding Problems

Challenge:	Existing solutions to automate KC generation and tagging for open-ended programming problems are highly labor-intensive and prone to bias and errors.
Approach:	They propose an automated pipeline for KC generation and tagging for open-ended programming problems using large language models.
Outcome:	The proposed method outperforms existing ones and outperfies human-written KCs on future student response prediction.

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Overview

Challenge

Approach

Outcome

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Naiming Liu, Zichao Wang, Richard Baraniuk, Andrew Lan

Multimodality

Challenge:	Knowledge tracing (KT) is a method used to estimate student mastery of concepts/skills/knowledge components from their responses to questions and to predict future performance.
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Reasoning

Challenge:	Knowledge Tracing (KT) models a learner's evolving knowledge state over time, but lacks the rich information embedded in students' problem-solving processes.
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CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models (2025.emnlp-main)

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Explainability & Interpretability in NLP

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Aysa Fan, Haoran Zhang, Luc Paquette, Rui Zhang

Language Models

Challenge:	Using large language models, we generate code-tracing questions based on code snippets and descriptions.
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Ziwei Wang, Jie Zhou, Qin Chen, Bo Jiang, Qingchun Bai, Liang Dou, Liang He

Language Models

Challenge:	Existing methods to learn behavioral sequences fail to capture complex behavioral patterns due to a lack of deep reasoning capabilities and world knowledge.
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Knowledge Tracing in Programming Education Integrating Students’ Questions (2025.acl-long)

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Multimodality

Challenge:	Existing knowledge tracing models that ignore student questions are suboptimal for programming education because of the complexity of coding tasks and the diverse methods students use to solve problems.
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Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators (2023.emnlp-main)

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Semantic Text Processing

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Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction (2024.lrec-main)

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Unggi Lee, Sungjun Yoon, Joon Seo Yun, Kyoungsoo Park, YoungHoon Jung, Damji Stratton, Hyeoncheol Kim

Semantic Text Processing

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DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)

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Wenting Zhao, Ye Liu, Tong Niu, Yao Wan, Philip Yu, Shafiq Joty, Yingbo Zhou, Semih Yavuz

Information Retrieval

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Systematic Task Exploration with LLMs: A Study in Citation Text Generation (2024.acl-long)

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Furkan Şahinuç, Ilia Kuznetsov, Yufang Hou, Iryna Gurevych

Text Generation

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