Modeling AI Tool Adoption in Higher Education: The Role of Authentic Learning and Prompting Competence.
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| Title: | Modeling AI Tool Adoption in Higher Education: The Role of Authentic Learning and Prompting Competence. |
|---|---|
| Authors: | Alshammari, Sultan Hammad1 sh.alshammari@uoh.edu.sa, Alshammari, Mohammed Habib1 m.habib@uoh.edu.sa |
| Source: | Electronic Journal of e-Learning. 2026, Vol. 24 Issue 4, p44-59. 16p. |
| Subject Terms: | *Authentic learning, *Artificial intelligence, *Higher education, Prompt engineering, Technology Acceptance Model, Perceived benefit, Innovation adoption |
| Abstract: | As AI tools are increasingly used in higher education, understanding the factors affecting students' adoption intentions has become theoretically and practically important. Previous studies have mainly relied on traditional technology acceptance constructs, while comparatively little attention has been given to pedagogical and competence-based conditions shaping students' cognitive evaluations of AI systems. To address this gap, the current study builds on the technology acceptance model (TAM) by proposing authentic learning (AL) and prompt engineering competence (PEC) as precursors of perceived usefulness (PU), perceived ease of use (PEU), and behavioral intention (BI) to use AI tools. The study was based on data collected from 309 undergraduate students at the University of Ha'il. A two-step structural equation modeling (SEM) approach was employed using AMOS software. Confirmatory factor analysis confirmed construct reliability, convergent validity, and discriminant validity. SEM was then conducted to test the hypotheses. The findings show that AL significantly predicts both PU and PEU, whereas PEC significantly predicts PEU but not PU. Both PU and PEU were found to be important predictors of BI. Bootstrapping results reveal that AL affects BI through PU and PEU, while PEC affects BI entirely through PEU. The results also confirm considerable explanatory power, with an R² of .71 for BI. These findings extend TAM by reconceptualizing AL as a foundational pedagogical precursor influencing AI adoption and by clarifying the unique role of PEC in improving PEU. Integrating pedagogical and competence-based determinants into AI-enabled higher education advances technology acceptance theory and explains the AI adoption mechanism more precisely. The findings provide practical guidance for educators and instructional designers by emphasizing the importance of integrating authentic learning tasks and developing students' prompt engineering skills to enhance meaningful AI-supported learning. [ABSTRACT FROM AUTHOR] |
| Copyright of Electronic Journal of e-Learning is the property of Academic Conferences & Publishing International Ltd. 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 196822497 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modeling AI Tool Adoption in Higher Education: The Role of Authentic Learning and Prompting Competence. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alshammari%2C+Sultan+Hammad%22">Alshammari, Sultan Hammad</searchLink><relatesTo>1</relatesTo><i> sh.alshammari@uoh.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Alshammari%2C+Mohammed+Habib%22">Alshammari, Mohammed Habib</searchLink><relatesTo>1</relatesTo><i> m.habib@uoh.edu.sa</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Electronic+Journal+of+e-Learning%22">Electronic Journal of e-Learning</searchLink>. 2026, Vol. 24 Issue 4, p44-59. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Authentic+learning%22">Authentic learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br /><searchLink fieldCode="DE" term="%22Prompt+engineering%22">Prompt engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Acceptance+Model%22">Technology Acceptance Model</searchLink><br /><searchLink fieldCode="DE" term="%22Perceived+benefit%22">Perceived benefit</searchLink><br /><searchLink fieldCode="DE" term="%22Innovation+adoption%22">Innovation adoption</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As AI tools are increasingly used in higher education, understanding the factors affecting students' adoption intentions has become theoretically and practically important. Previous studies have mainly relied on traditional technology acceptance constructs, while comparatively little attention has been given to pedagogical and competence-based conditions shaping students' cognitive evaluations of AI systems. To address this gap, the current study builds on the technology acceptance model (TAM) by proposing authentic learning (AL) and prompt engineering competence (PEC) as precursors of perceived usefulness (PU), perceived ease of use (PEU), and behavioral intention (BI) to use AI tools. The study was based on data collected from 309 undergraduate students at the University of Ha'il. A two-step structural equation modeling (SEM) approach was employed using AMOS software. Confirmatory factor analysis confirmed construct reliability, convergent validity, and discriminant validity. SEM was then conducted to test the hypotheses. The findings show that AL significantly predicts both PU and PEU, whereas PEC significantly predicts PEU but not PU. Both PU and PEU were found to be important predictors of BI. Bootstrapping results reveal that AL affects BI through PU and PEU, while PEC affects BI entirely through PEU. The results also confirm considerable explanatory power, with an R² of .71 for BI. These findings extend TAM by reconceptualizing AL as a foundational pedagogical precursor influencing AI adoption and by clarifying the unique role of PEC in improving PEU. Integrating pedagogical and competence-based determinants into AI-enabled higher education advances technology acceptance theory and explains the AI adoption mechanism more precisely. The findings provide practical guidance for educators and instructional designers by emphasizing the importance of integrating authentic learning tasks and developing students' prompt engineering skills to enhance meaningful AI-supported learning. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Electronic Journal of e-Learning is the property of Academic Conferences & Publishing International Ltd. 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.34190/ejel.24.4.4784 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 44 Subjects: – SubjectFull: Authentic learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Higher education Type: general – SubjectFull: Prompt engineering Type: general – SubjectFull: Technology Acceptance Model Type: general – SubjectFull: Perceived benefit Type: general – SubjectFull: Innovation adoption Type: general Titles: – TitleFull: Modeling AI Tool Adoption in Higher Education: The Role of Authentic Learning and Prompting Competence. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alshammari, Sultan Hammad – PersonEntity: Name: NameFull: Alshammari, Mohammed Habib IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14794403 Numbering: – Type: volume Value: 24 – Type: issue Value: 4 Titles: – TitleFull: Electronic Journal of e-Learning Type: main |
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