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Information Technology Education Journal
Published by Universitas Negeri Makassar
ISSN : 28097971     EISSN : 2809798X     DOI : -
Core Subject : Science, Education,
Computer Science & IT Education
INTEC Journal is published by the Informatics and Computer Engineering Education Study Program at Makassar State University. INTEC Journal is published periodically three times a year, containing articles on research results and / or critical studies in the field of Informatics and Computer Engineering Education from students, lecturers, and practitioners from universities or research institutions. The INTEC journal already has a print version ISSN with the number 2809-798X in 2022 and an online version ISSN with the number 2809-7971. INTEC Journal contains articles on informatics and computer engineering education in particular: learning multimedia e-learning/blended learning, information system, artificial intelligence and robotics, embedded expert system, big data and machine learning, software and network engineering
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 1 Documents
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Search results for , issue "Vol. 5, No. 1, February (2026)" : 1 Documents clear
Predicting Generative AI–Based Learning Among Students: The Roles of Adaptive Learning Motivation, Technology Openness, and Digital Collaboration Readiness Daud Mahande, Ridwan
Information Technology Education Journal Vol. 5, No. 1, February (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i1.2601

Abstract

The integration of Generative Artificial Intelligence (AI) in higher education presents opportunities and challenges related to student readiness as the main users of technology. This study aimed to analyze the role of adaptive learning motivation, technology openness, and digital collaboration readiness in predicting student perceptions of Generative AI-based learning. A quantitative approach with an explanatory design was used through a survey of 370 students from the Faculty of Engineering, State University of Makassar, Indonesia. The data were analyzed using Partial Least Squares structural equation modeling (PLS-SEM). The results showed that the three predictor variables had a positive and significant effect on students' perception of Generative AI-based learning, with adaptive learning motivation being the most dominant factor. In addition, a pattern of tiered relationships was found, in which adaptive learning motivation affects openness to technology, which further strengthens the readiness for digital collaboration. The research model explained 60.8% of students' perceptions of AI-based learning. These findings confirm that the success of Generative AI integration is not only determined by technological readiness but also by students' psychological and digital readiness. This study contributes to expanding the model of learner readiness in the AI-based education ecosystem.

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