面向工程教育的需求与注意力感知型AI学习工具设计：来自心理结果的见解
Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes
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中文总结 AI 辅助
本研究针对工程教育，通过调查206名工程专业学生，探究AI聊天机器人对学生心理需求与动机状态的影响，提出了工程专用AI学习工具的设计原则。
中文摘要 AI 辅助
人工智能（AI）正在变革高等教育，但其益处会因支持学习的场景、方式和频率而有所不同。现有研究多关注认知与学业结果，本研究则探究AI聊天机器人对工程专业学生心理需求与动机状态的支持作用。对206名高校工程专业学生的调查，分析了他们感知到的AI聊天机器人在自主感、关联感以及缓解能力挫败感方面的影响；采用带潜在交互效应的结构方程模型，探究基线自主感、能力挫败感、关联感和个人能动性对感知AI结果的作用。结果显示，学生认为AI在缓解能力挫败感方面益处最大，对自主感的益处较小，对关联感的益处最弱；基线动机状态比人口统计学因素更重要，注意力不集中会调节基线能力挫败感和自主感与感知到的AI相关益处的关联。这些结果为制定工程专用AI工具的设计原则提供了见解。
英文摘要
Artificial Intelligence (AI) is transforming higher education, but its benefits can vary depending on where, how, and how often it supports learning. While prior research emphasizes cognitive and academic outcomes, this study examines how AI chatbots support the psychological needs and motivational states of engineering students. A survey of college engineering students (n = 206) examined perceived effects of AI chatbots on autonomy, relatedness, and relief from competence frustration. Structural equation modeling with latent interaction effects examined how baseline autonomy, competence frustration, relatedness, and personal agency contributed to perceived AI outcomes. Results indicate that students perceived that AI provided the greatest benefits as relief from competence frustration, smaller benefits for autonomy, and the weakest benefits for relatedness. Baseline motivational states mattered more than demographic factors, and inattention moderated how baseline competence frustration and autonomy related to perceived AI-related benefits. These results offer insights into formulating design principles for engineering-specific AI-based tools.
