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Predicting Online Instructional Design Students’ Intention to Use AI Tools: Value, Utility, and Self-Efficacy.

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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]
Copyright of Online Learning is the property of Online Learning Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22Online+Learning%22">Online Learning</searchLink>. Jun2026, Vol. 30 Issue 2, p130-152. 23p.
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  Data: *<searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br />*<searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Technology+education%22">Technology education</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+motivation%22">Academic motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Self-efficacy%22">Self-efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Perceived+benefit%22">Perceived benefit</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink>
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  Data: 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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  Data: <i>Copyright of Online Learning is the property of Online Learning Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=194761189
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        Value: 10.24059/olj.v30i2.5566
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      – SubjectFull: Technology education
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              Text: Jun2026
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