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All Journal Journal of Education, Religious, and Instructions (JoERI)
Dharma Yuga Putra Sanjaya
Buddhist Education Department, Jinarakkhita Buddhist College, Lampung, Indonesia
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The Influence of Artificial Intelligence, Learning Motivation, and Digital Literacy on the Learning Independence of Buddhist Students Jiny Dharma Ditha; Sukma Ayu; Dharma Yuga Putra Sanjaya; Yusmati Liau
Journal of Education, Religious, and Instructions (JoERI) Vol. 4 No. 1 (2026): JOERI June 2026
Publisher : LPPM STIAB JINARAKKHITA LAMPUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60046/joeri.v4i1.328

Abstract

The development of Artificial Intelligence (AI) in education has transformed the way students acquire information and engage in the learning process. However, the use of AI that is not balanced with adequate learning motivation and digital literacy has the potential to affect students’ learning autonomy. This study aims to analyze the influence of Artificial Intelligence, learning motivation, and digital literacy on the learning autonomy of Buddhist students in Bandar Lampung City. The study employs a quantitative approach using a survey method. The sample consists of 30 Buddhist students selected using purposive sampling. Data were collected via a questionnaire and analyzed using multiple linear regression with the aid of SPSS. The results indicate that Artificial Intelligence has a positive and significant effect on learning autonomy, contributing 87.6%, while digital literacy has a positive and significant effect, contributing 83.0%. Learning motivation has a positive relationship with learning autonomy and contributes 24.0%, but does not show a significant effect in the multiple regression model. Simultaneously, Artificial Intelligence, learning motivation, and digital literacy have a significant effect on learning autonomy, contributing 91.0%. The variable with the most dominant influence is Artificial Intelligence, followed by digital literacy. These findings indicate that enhancing students’ learning autonomy in the digital age requires support through the judicious use of AI, the strengthening of digital literacy, and the development of learning strategies capable of fostering learning motivation. This study contributes to the development of technology-based education and self-directed learning in the digital age. Keywords: Artificial Intelligence; Learning Motivation; Digital Literacy; Independent Learning
Explaining Generative Artificial Intelligence Acceptance Among Indonesian University Students: an Extended Technology Acceptance Model Arya Sura Pratama; Dharma Yuga Putra Sanjaya; Komang Mudita Tribi Putra
Journal of Education, Religious, and Instructions (JoERI) Vol. 4 No. 1 (2026): JOERI June 2026
Publisher : LPPM STIAB JINARAKKHITA LAMPUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60046/joeri.v4i1.329

Abstract

The rapid integration of Generative Artificial Intelligence (GenAI) into higher education has transformed learning practices, creating a need to better understand the factors that influence students’ acceptance of this emerging technology. While the Technology Acceptance Model (TAM) has been widely used to explain technology adoption, limited studies have incorporated contextual factors relevant to educational environments. This study extends TAM by integrating Value Compatibility (VC) and Lecturer Support (LS) to examine students’ acceptance of GenAI in Indonesian higher education. A quantitative cross-sectional survey was conducted with 100 university students selected through purposive sampling. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 and a bootstrapping procedure of 5,000 subsamples. The findings reveal that Perceived Ease of Use significantly influences Perceived Usefulness, while Perceived Usefulness positively affects both Attitude Toward Using and Behavioral Intention. Attitude Toward Using significantly predicts Behavioral Intention, which subsequently influences Actual Use Behavior. Furthermore, the extended variables demonstrate significant effects, with Value Compatibility positively affecting Behavioral Intention and Lecturer Support enhancing Attitude Toward Using. The model exhibits moderate to high explanatory power and satisfactory predictive relevance. This study contributes to the technology acceptance literature by demonstrating that students’ adoption of GenAI is shaped not only by technological perceptions but also by contextual and institutional factors. The findings provide practical insights for higher education institutions seeking to promote effective and responsible integration of GenAI into teaching and learning practices.
Co-Authors Arya Sura Pratama Jiny Dharma Ditha Komang Mudita Tribi Putra Sukma Ayu Yusmati Liau