Bibliographic Details
| 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] |
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| Database: |
Education Research Complete |
