Learning Through Dialogue: Engagement and Efficacy Matter More Than Explanations (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used as conversational partners for learning, yet the interactional dynamics supporting users’ learning and engagement are understudied. |
| Approach: | They analyze linguistic and interactional features from LLM and participant chats to identify the mechanisms and conditions under which LLM explanations shape changes in political knowledge and confidence. |
| Outcome: | The results show that LLM explanations shape political knowledge and confidence . they also show that their effects are highly conditional and vary by political efficacy . |
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| Challenge: | Existing studies have found that large language models can generate persuasive content without engaging in human experimentation. |
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| Challenge: | Using large language models, interlocutors can reach mutual understanding even when they do not possess perfect knowledge. |
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Jillian Fisher, Shangbin Feng, Robert Aron, Thomas Richardson, Yejin Choi, Daniel W Fisher, Jennifer Pan, Yulia Tsvetkov, Katharina Reinecke
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| Challenge: | Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored. |
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance on many unseen tasks in a zero-shot setting. |
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| Challenge: | Existing language models such as Transformer-based models fail to predict the conversation outcome. |
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| Challenge: | Recent studies have shown that ChatGPT has limitations such as failing to ask clarifying questions to ambiguous queries or refusing problematic user requests. |
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