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 |
|---|---|
| Language: | English |
| Authors: | Muh. Hamkah, Heri Retnawati, Muthmainah Muthmainah, Muhammad Hakiki, Mustofa Abi Hamid, Hasruddin Hasruddin, M. Dahlan, M. Agphin Ramadhan, Muhammad Nurtanto, Indra Mutiara |
| Source: | Online Learning. 2025 29(4):560-594. |
| Availability: | Online Learning Consortium, Inc. P.O. Box 1238, Newburyport, MA 01950. Tel: 888-898-6209; Fax: 888-898-6209; e-mail: olj@onlinelearning-c.org; Web site: https://olj.onlinelearningconsortium.org/index.php/olj/index |
| Peer Reviewed: | Y |
| Page Count: | 35 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Online Courses, Artificial Intelligence, Engineering Education, College Students, Student Attitudes, Blended Learning, Value Judgment, Usability, Social Influences, Affordances, Self Efficacy, Barriers, Risk, Foreign Countries, Student Characteristics |
| Geographic Terms: | Indonesia |
| ISSN: | 2472-5749 2472-5730 |
| 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 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. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1490587 |
| Database: | ERIC |
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