ORIGINAL RESEARCH article
Front. Psychol.
Sec. Educational Psychology
Volume 16 - 2025 | doi: 10.3389/fpsyg.2025.1624182
This article is part of the Research TopicAI Innovations in Education: Adaptive Learning and BeyondView all 10 articles
Building a Triadic Model of Technology, Motivation, and Engagement: A Mixed-Methods Study of AI Teaching Assistants in Design Theory Education
Provisionally accepted
- 1Aba Teachers University, Wenchuan, China
- 2Swinburne University of Technology, Hawthorn, Australia
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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 ten 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.
Keywords: design education, AI chatbot, mixed methods, Task technology fit, Self Determination Theory, Student engagemen
Received: 07 May 2025; Accepted: 09 Jun 2025.
Copyright: © 2025 Wang and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Anlan Wang, Aba Teachers University, Wenchuan, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.