Digital learning innovation
Engineering students’ learning motivation for AI scaffolding
Bibliographic Data
| ID | 21618592 |
|---|---|
| Authors | Jung Sun Sung (0000-0002-9236-368X, University of Illinois Urbana-Champaign, corresponding author), Wenhao David Huang, Wen‐Hao David Huang (University of Illinois Urbana-Champaign) |
| Year | 2026 |
| Volume | 74 |
| Issue | 2 |
| Pages | 493-516 |
| Publication date | 2026-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational Technology Research and Development (JOURNAL) |
| Journal identifiers | ISSN: 1042-1629 • E-ISSN: 1556-6501 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11423-025-10576-w |
| OpenAlex | W4416925796 |
| Language | EN |
| References cited | 18 |
This study compares engineering college students’ learning motivation between AI-featured and traditional scaffolding methods using a mixed-methods approach. Two different types of class activities, one involving solving a computational problem and the other assembling an essay, were designed to be carried out using both AI-featured and conventional methods. All activities were designed using the Attention, Relevance, Confidence, and Satisfaction (ARCS) model and results were analyzed using both quantitative and qualitative means. Findings show that participants’ motivation was significantly higher in all Attention, Relevance, Confidence, and Satisfaction categories when they used AI-featured methods for solving computational problems while their motivation was significantly higher when they used AI-featured methods only in Attention and Confidence for the essay activity. Qualitative findings describe how AI features motivated learners based on the different types of scaffoldings. Additionally, how the AI features are related to each ARCS model categories are summarized. The results suggest strategies for optimizing the design of AI-featured scaffolds to motivate learners in STEM fields grounded in the ARCS model. This involves linking learners’ goals and interests to real scientific applications of AI by expanding ‘Relevance’ and adjusting learning requirements to strengthen ‘Confidence’ and ‘Satisfaction’
Digital Learning · Educational technology · Electronic learning · Engineering education · Instructional design · Qualitative research · Scaffold · Teaching method · Educational Games and Gamification · Online Learning and Analytics · Teaching and Learning Programming
Motivational Design for Learning and Performance
Examining Science Education in ChatGPT
Development and use of the ARCS model of instructional design
Role of the Big Five personality traits in predicting college students' academic motivation and achievement
Content analysis and thematic analysis
Towards Intelligent-TPACK
Evaluating the effectiveness of blended learning using the ARCS model
Scaffolding Human Champions
Using thematic analysis in psychology
| Citation velocity | historical |
|---|---|
| Highly cited | No |