多模态和对话式AI对学习成果和体验的影响
Impact of Multimodal and Conversational AI on Learning Outcomes and Experience
AI总结：
本研究比较了三种生物学学习方法，发现多模态对话式AI在学习成果和体验上表现最佳，但文本仅对话式AI虽易用但效果最差，揭示了认知负荷理论下的学习效果与感知之间的差异。
摘要：
多模态大语言模型（MLLMs）为通过对话系统支持多媒体学习提供了机会，但对话式AI在视觉丰富STEM领域中的学习影响尚不明确。本研究通过随机对照在线实验（N=124）比较了三种学习生物学的方法：（1）基于文档的对话式AI，提供交替的文本和图像响应（MuDoC）；（2）基于文档的对话式AI，仅提供文本响应（TexDoC）；（3）带有语义搜索和高亮功能的教科书界面（DocSearch）。使用MuDoC的学员在后测中得分最高，并报告了最积极的学习体验。值得注意的是，尽管TexDoC被认为比DocSearch更吸引人且更容易使用，但其后测得分最低，揭示了学生感知与学习成果之间的脱节。通过认知负荷理论的视角，这些发现表明对话性减少了额外负荷，而多模态带来的视觉-语言整合增加了相关负荷，从而改善了学习成果。当对话性未被多模态补充时，减少的认知努力可能会夸大感知理解，而不会改善学习成果。
英文摘要：
Multimodal Large Language Models (MLLMs) offer an opportunity to support multimedia learning through conversational systems grounded in educational content. However, while conversational AI is known to boost engagement, its impact on learning in visually-rich STEM domains remains under-explored. Moreover, there is limited understanding of how multimodality and conversationality jointly influence learning in generative AI systems. This work reports findings from a randomized controlled online study (N = 124) comparing three approaches to learning biology from textbook content: (1) a document-grounded conversational AI with interleaved text-and-image responses (MuDoC), (2) a document-grounded conversational AI with text-only responses (TexDoC), and (3) a textbook interface with semantic search and highlighting (DocSearch). Learners using MuDoC achieved the highest post-test scores and reported the most positive learning experience. Notably, while TexDoC was rated as significantly more engaging and easier to use than DocSearch, it led to the lowest post-test scores, revealing a disconnect between student perceptions and learning outcomes. Interpreted through the lens of the Cognitive Load Theory, these findings suggest that conversationality reduces extraneous load, while visual-verbal integration induced by multimodality increases germane load, leading to better learning outcomes. When conversationality is not complemented by multimodality, reduced cognitive effort may instead inflate perceived understanding without improving learning outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
