从书面回答到与AI对话：活动形式、交互模态和语言如何影响学生的学习与参与度
From Written Response to Dialogue with AI: How Activity Format, Interaction Modality, and Language Impact Student Learning and Engagement
浏览论文内容
中文总结 AI 辅助
本研究通过实地实验比较书面回答与文本/语音对话式AI活动，发现对话式活动提升参与度和自我效能，双语语音对话尤其有益，但知识增益无显著差异，建议给予学习者模态控制并利用母语作为跨语言支持。
中文摘要 AI 辅助
大型语言模型的广泛普及正在挑战书面学习活动，因为学生越来越能够在不真正参与学习内容的情况下生成回答。对话式AI为将这些活动重新设计为对话提供了机会，而多语言能力可能使此类对话对通过非母语学习的学生更加可及。我们在印度卡纳塔克邦英语授课院校对305名以卡纳达语为母语的本科生进行了一项实地研究。我们比较了书面回答活动与基于文本和基于语音的AI对话活动，每种活动均在纯英语或卡纳达语-英语双语环境中进行。完成对话活动的学生比完成书面回答的学生在活动上花费了更多时间，贡献了更多内容，并报告了更高的兴趣和自我效能感，尽管总体完成对话活动的学生人数较少。所有条件下的知识均有所增加，不同活动形式或语言之间的增益没有可靠差异。语言以不同方式影响各模态的参与：双语互动在语音对话中特别有益，它减少了发音困难，增加了展示理解的轮次，并减少了对话放弃。然而，学生也重视英语，因为它与他们的学术和职业抱负相关。这些发现表明，设计基于AI的对话式学习不仅仅需要在写作与对话、语音与文本、英语与学生母语之间做出选择。我们强调了为学习者提供对模态、信息和节奏更大控制权的机会，并将母语用作跨语言辅助支持而非替代英语。
英文摘要
The widespread availability of LLMs is challenging written learning activities, as students can increasingly generate responses without necessarily engaging with the learning content. Conversational AI creates an opportunity to redesign these activities as dialogue, while multilingual capabilities may make such dialogue more accessible to students learning through a non-native language. We conducted a field study with 305 native Kannada-speaking undergraduate students at English-medium institutions in Karnataka, India. We compared written response activities with text- and voice-based dialogic activities with AI, each conducted in English-only or bilingual Kannada-English settings. Students who completed dialogic activities spent more time on the activities, contributed more, and reported greater interest and self-efficacy than those completing written responses, although fewer students completed the dialogic activities overall. Knowledge increased across all conditions, with no reliable differences in gains between activity formats or languages. Language shaped participation differently across modalities: bilingual interaction was particularly beneficial in voice dialogue, where it reduced articulation difficulties, increased turns demonstrating understanding, and reduced conversation abandonment. However, students also valued English because of its connection to their academic and professional aspirations. These findings show that designing dialogic learning with AI requires more than choosing between writing and dialogue, voice and text, or English and students' native languages. We highlight opportunities to give learners greater control over modality, information, and pace, and to use native languages as translanguaging support rather than as a replacement for English.
发表机构
- Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。
