Bibliographic Details
| Title: |
Predicting Online Instructional Design Students’ Intention to Use AI Tools: Value, Utility, and Self-Efficacy. |
| Authors: |
Cho, Moon-Heum1, Chen, Ying1 |
| Source: |
Online Learning. Jun2026, Vol. 30 Issue 2, p130-152. 23p. |
| Subject Terms: |
*Online education, *Intelligent tutoring systems, *Technology education, *Academic motivation, Self-efficacy, Perceived benefit, Cluster analysis (Statistics) |
| Abstract: |
This study examined online instructional design (ID) students’ intention to use AI tools in their practice. Seventy-four online ID master’s students in the United States participated. Regression analysis showed demographic variables (gender, age, full-time status) were not related to their intention to use AI tools in practice. However, students’ value of AI tools for learning, utility for their own academic tasks, and self-efficacy were significantly related to their intention. Cluster analysis revealed two distinct groups: one scored above average on value, utility, self-efficacy, and intention to use AI tools while the other scored below average on these measures. Content analysis revealed diverse perspectives between groups on AI tool use, perceptions of AI in education, and necessary training for AI tools in ID. Findings inform practical guidance for training ID students. [ABSTRACT FROM AUTHOR] |
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| Database: |
Education Research Complete |
