Papers by Naiming Liu
CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles (2023.findings-emnlp)
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| Challenge: | CLASS empowers ITS with two key capabilities: first, it equips it with essential problem-solving strategies, and second, it facilitates natural language interactions, fostering engaging student-tutor conversations. |
| Approach: | They propose a design framework called Conversational Learning with Analytical Step-by-Step Strategies (CLASS) that empowers ITS with two key capabilities: first, a carefully curated dataset and second, facilitating natural language interactions. |
| Outcome: | The proposed framework empowers ITS with two key capabilities: first, it equips it with essential problem-solving strategies, and second, it facilitates natural language interactions, fostering engaging student-tutor conversations. |
Open-ended Knowledge Tracing for Computer Science Education (2022.emnlp-main)
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| 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. |
| Approach: | They propose a student knowledge-guided code generation approach that combines program synthesis methods with student knowledge tracing methods to solve the OKT problem. |
| Outcome: | The proposed method is based on a student knowledge-guided code generation approach and validates on coding questions. |
CLEAR-3K: Assessing Causal Explanatory Capabilities in Language Models (2026.findings-eacl)
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| Challenge: | Existing natural language understanding benchmarks inadequately address the ability to evaluate causal relationships. |
| Approach: | They propose to use CLEAR-3K to evaluate whether language models can determine if one statement causally explains another. |
| Outcome: | The proposed questions show that language models often confuse semantic similarity with causality, relying on lexical and semantic overlap instead of inferring actual causal explanatory relationships. |
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. |
MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education (2024.findings-emnlp)
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| Challenge: | Using a novel dataset, we evaluate the counterfactual reasoning capabilities of Large Language Models (LLMs) . |
| Approach: | They propose a dataset to evaluate the counterfactual reasoning capabilities of Large Language Models (LLMs) using a pedagogical approach. |
| Outcome: | The proposed method mimics how educators anticipate and model potential student misconceptions by creating plausible but incorrect answer options by envisioning hypothetical scenarios and logically coherent reasoning paths. |
MalruleLib: Large-Scale Executable Misconception Reasoning with Step Traces for Modeling Student Thinking in Mathematics (2026.acl-long)
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| Challenge: | MalruleLib is a learning-science-grounded framework that translates documented misconceptions into executable procedures and generates step-by-step traces of malrule-consistent student reasoning. |
| Approach: | They propose a learning-science-grounded framework that translates documented misconceptions into executable procedures and generates step-by-step traces of malrule-consistent student reasoning. |
| Outcome: | The framework translates misconceptions into executable procedures and generates step-by-step traces of malrule-consistent student reasoning. |