Unraveling Factors Affecting Engineering Students' Acceptance of Artificial Intelligence in the Context of a Blended Learning Environment.
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| Title: | Unraveling Factors Affecting Engineering Students' Acceptance of Artificial Intelligence in the Context of a Blended Learning Environment. |
|---|---|
| Authors: | Hamkah, Muh.1, Retnawati, Heri1, Muthmainah, Muthmainah1 muhammadhakiki@unesa.ac.id, Hakiki, Muhammad2, Hamid, Mustofa Abi1, Hasruddin, Hasruddin1, Dahlan, M.3, Ramadhan, M. Agphin1, Nurtanto, Muhammad1, Mutiara, Indra1 |
| Source: | Online Learning. Dec2025, Vol. 29 Issue 4, p560-594. 35p. |
| Subject Terms: | *Artificial intelligence, *Engineering education, *Blended learning, Risk perception, Perceived benefit, Structural equation modeling, Attitudes toward technology, Self-efficacy |
| Geographic Terms: | Indonesia |
| Abstract: | The rapid advancement of artificial intelligence (AI) has significantly transformed various educational domains, including engineering education. Despite AI's growing prevalence, limited research has explored the determinants influencing engineering students' acceptance of AI. This study investigates the factors shaping AI acceptance among engineering students in Indonesia. Using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach, data were collected from 158 engineering students across multiple universities. The research model incorporates six constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Social Influence (SI), Facilitating Conditions (FC), Self-Efficacy (SE), and Perceived Risks (PR), each operationalized through seven measurement indicators. The results indicate that PU, PEOU, SI, and SE have significant positive effects on AI acceptance, while PR exerts a significant negative influence. Conversely, FC does not demonstrate a significant impact. These findings offer theoretical and practical implications for fostering AI adoption in The rapid advancement of artificial intelligence (AI) has significantly transformed various educational domains, including engineering education. Despite AI's growing prevalence, limited research has explored the determinants influencing engineering students' acceptance of AI. This study investigates the factors shaping AI acceptance among engineering students in Indonesia. Using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach, data were collected from 158 engineering students across multiple universities. The research model incorporates six constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Social Influence (SI), Facilitating Conditions (FC), Self-Efficacy (SE), and Perceived Risks (PR), each operationalized through seven measurement indicators. The results indicate that PU, PEOU, SI, and SE have significant positive effects on AI acceptance, while PR exerts a significant negative influence. Conversely, FC does not demonstrate a significant impact. These findings offer theoretical and practical implications for fostering AI adoption in engineering education, including strategies for educators, policymakers, and developers of AI-based tools to enhance user acceptance. This study extends the literature on technology acceptance in educational settings, providing actionable insights for improving the integration of AI in higher education. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 190369492 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unraveling Factors Affecting Engineering Students' Acceptance of Artificial Intelligence in the Context of a Blended Learning Environment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hamkah%2C+Muh%2E%22">Hamkah, Muh.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Retnawati%2C+Heri%22">Retnawati, Heri</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Muthmainah%2C+Muthmainah%22">Muthmainah, Muthmainah</searchLink><relatesTo>1</relatesTo><i> muhammadhakiki@unesa.ac.id</i><br /><searchLink fieldCode="AR" term="%22Hakiki%2C+Muhammad%22">Hakiki, Muhammad</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hamid%2C+Mustofa+Abi%22">Hamid, Mustofa Abi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Hasruddin%2C+Hasruddin%22">Hasruddin, Hasruddin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Dahlan%2C+M%2E%22">Dahlan, M.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Ramadhan%2C+M%2E+Agphin%22">Ramadhan, M. Agphin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Nurtanto%2C+Muhammad%22">Nurtanto, Muhammad</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mutiara%2C+Indra%22">Mutiara, Indra</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Online+Learning%22">Online Learning</searchLink>. Dec2025, Vol. 29 Issue 4, p560-594. 35p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Engineering+education%22">Engineering education</searchLink><br />*<searchLink fieldCode="DE" term="%22Blended+learning%22">Blended learning</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+perception%22">Risk perception</searchLink><br /><searchLink fieldCode="DE" term="%22Perceived+benefit%22">Perceived benefit</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Attitudes+toward+technology%22">Attitudes toward technology</searchLink><br /><searchLink fieldCode="DE" term="%22Self-efficacy%22">Self-efficacy</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Indonesia%22">Indonesia</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The rapid advancement of artificial intelligence (AI) has significantly transformed various educational domains, including engineering education. Despite AI's growing prevalence, limited research has explored the determinants influencing engineering students' acceptance of AI. This study investigates the factors shaping AI acceptance among engineering students in Indonesia. Using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach, data were collected from 158 engineering students across multiple universities. The research model incorporates six constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Social Influence (SI), Facilitating Conditions (FC), Self-Efficacy (SE), and Perceived Risks (PR), each operationalized through seven measurement indicators. The results indicate that PU, PEOU, SI, and SE have significant positive effects on AI acceptance, while PR exerts a significant negative influence. Conversely, FC does not demonstrate a significant impact. These findings offer theoretical and practical implications for fostering AI adoption in The rapid advancement of artificial intelligence (AI) has significantly transformed various educational domains, including engineering education. Despite AI's growing prevalence, limited research has explored the determinants influencing engineering students' acceptance of AI. This study investigates the factors shaping AI acceptance among engineering students in Indonesia. Using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach, data were collected from 158 engineering students across multiple universities. The research model incorporates six constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Social Influence (SI), Facilitating Conditions (FC), Self-Efficacy (SE), and Perceived Risks (PR), each operationalized through seven measurement indicators. The results indicate that PU, PEOU, SI, and SE have significant positive effects on AI acceptance, while PR exerts a significant negative influence. Conversely, FC does not demonstrate a significant impact. These findings offer theoretical and practical implications for fostering AI adoption in engineering education, including strategies for educators, policymakers, and developers of AI-based tools to enhance user acceptance. This study extends the literature on technology acceptance in educational settings, providing actionable insights for improving the integration of AI in higher education. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.24059/olj.v29i4.4890 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 35 StartPage: 560 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Engineering education Type: general – SubjectFull: Blended learning Type: general – SubjectFull: Risk perception Type: general – SubjectFull: Perceived benefit Type: general – SubjectFull: Structural equation modeling Type: general – SubjectFull: Attitudes toward technology Type: general – SubjectFull: Self-efficacy Type: general – SubjectFull: Indonesia Type: general Titles: – TitleFull: Unraveling Factors Affecting Engineering Students' Acceptance of Artificial Intelligence in the Context of a Blended Learning Environment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hamkah, Muh. – PersonEntity: Name: NameFull: Retnawati, Heri – PersonEntity: Name: NameFull: Muthmainah, Muthmainah – PersonEntity: Name: NameFull: Hakiki, Muhammad – PersonEntity: Name: NameFull: Hamid, Mustofa Abi – PersonEntity: Name: NameFull: Hasruddin, Hasruddin – PersonEntity: Name: NameFull: Dahlan, M. – PersonEntity: Name: NameFull: Ramadhan, M. Agphin – PersonEntity: Name: NameFull: Nurtanto, Muhammad – PersonEntity: Name: NameFull: Mutiara, Indra IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 24725749 Numbering: – Type: volume Value: 29 – Type: issue Value: 4 Titles: – TitleFull: Online Learning Type: main |
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