Papers by Junling Wang
Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation (2026.acl-long)
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| Challenge: | Existing systems that detect logical fallacies in public discourse do not help people recognize them independently. |
| Approach: | They propose an intelligent tutoring system which uses large language models to help humans learn about logical fallacies. |
| Outcome: | The proposed system outperforms baseline LLMs lacking such pedagogical strategies. |
Generating Pedagogically Meaningful Visuals for Math Word Problems: A New Benchmark and Analysis of Text-to-Image Models (2025.findings-acl)
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| Challenge: | Math word problems (MWPs) describe mathematical scenarios through text, requiring learners to interpret both linguistic and numerical information to derive mathematical expressions. |
| Approach: | They propose a framework for generating pedagogically meaningful visuals from MWP text descriptions using a pre-defined visual language and a design space grounded in interviews with math teachers. |
| Outcome: | The proposed framework illustrates the core mathematical relationships in math word problems. |
Book2Dial: Generating Teacher Student Interactions from Textbooks for Cost-Effective Development of Educational Chatbots (2024.findings-acl)
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| Challenge: | Educational chatbots are a promising tool for assisting student learning, but high-quality data is difficult to obtain due to privacy concerns. |
| Approach: | They propose a framework for generating synthetic teacher-student interactions grounded in a set of textbooks and propose to open-source their results. |
| Outcome: | The proposed framework captures a key aspect of learning interactions where curious students with partial knowledge ask teachers questions about the material in the textbook. |
Can Vision-Language Models Solve Visual Math Equations? (2025.emnlp-main)
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| Challenge: | Vision-Language Models (VLMs) perform well on textual equations, but fail on visually grounded counterparts. |
| Approach: | They propose to decompose visual equation solving into symbolic equation solving and visual recognition into two core components to understand this gap. |
| Outcome: | The proposed models perform well on textual equations, but fail on visual grounded ones. |