Wang Anlan, Wu Xingting
Academy of Fine Arts, Aba Teachers College, Wenchuan, China.
School of Architecture and Design, Swinburne University of Technology, Melbourne, VIC, Australia.
Front Psychol. 2025 Jul 2;16:1624182. doi: 10.3389/fpsyg.2025.1624182. eCollection 2025.
In design education, it is often more difficult to keep students engaged in theory courses than in hands-on studio classes. Theory courses focus on abstract concepts like design history and principles, which can feel disconnected from practical experience. This study explores how AI-powered teaching assistants can support student engagement in design theory through a mixed-methods approach. Based on Self-Determination Theory (SDT) and Task-Technology Fit (TTF) Theory, we developed a triadic engagement model and tested it with data from 363 undergraduate design students who used a domain-specific AI assistant. Results from Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) show that communication quality, perceived competence, task-technology fit, and school support are key predictors of engagement. In contrast, individual technology fit and lecturer support have limited effects. Fuzzy-set Qualitative Comparative Analysis (fsQCA) identifies five learner profiles leading to high engagement, showing that different combinations of motivation, support, and technology fit can be effective. Interviews with 10 students identify three themes, further revealing that while the AI assistant is helpful and accessible, it lacks depth in critical thinking, and it demonstrates that students learn to verify AI assistants' responses and reflect on their learning. This study contributes to education and AI research by showing that chatbots must support both psychological needs and task alignment to foster meaningful engagement. It positions AI not just as an information tool, but as a partner in reflective and autonomous learning.
在设计教育中，相较于实践工作室课程，让学生参与理论课程往往更具挑战性。理论课程聚焦于诸如设计历史和原则等抽象概念，这可能会让人感觉与实践经验脱节。本研究通过混合方法探讨了人工智能驱动的教学助手如何支持学生参与设计理论学习。基于自我决定理论（SDT）和任务-技术匹配（TTF）理论，我们开发了一个三元参与模型，并使用来自363名使用特定领域人工智能助手的本科设计专业学生的数据对其进行了测试。偏最小二乘结构方程模型（PLS-SEM）和人工神经网络（ANN）的结果表明，沟通质量、感知能力、任务-技术匹配度和学校支持是参与度的关键预测因素。相比之下，个人技术匹配度和讲师支持的影响有限。模糊集定性比较分析（fsQCA）确定了导致高参与度的五种学习者类型，表明动机、支持和技术匹配度的不同组合可能是有效的。对10名学生的访谈确定了三个主题，进一步揭示出虽然人工智能助手很有帮助且易于使用，但它在批判性思维方面缺乏深度，并且表明学生学会了验证人工智能助手的回答并反思自己的学习。本研究通过表明聊天机器人必须同时支持心理需求和任务匹配以促进有意义的参与，为教育和人工智能研究做出了贡献。它将人工智能定位为不仅是一种信息工具，更是反思性和自主学习的伙伴。
