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All Journal Information Technology Education Journal Journal of Vocational, Informatics and Computer Education
Muhammad Nur Faiz
Universitas Negeri Makassar
Computer Science & IT Education

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Explaining Continuance Intention in Artificial Intelligence-Supported Learning through Artificial Intelligence Literacy and Technology Acceptance Model Muhammad Nur Faiz; Muhammad Yahya; Abdul Muis Mappalotteng; Sanatang; Fathahillah; Fitria Arifyanti; Selen Subasi
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.372

Abstract

Purpose – Despite the growing use of artificial intelligence in higher education, prior studies have focused more on initial adoption than on continuance intention, while the role of AI literacy in sustaining AI use remains underexplored. This study examines an integrative model linking AI literacy, perceived usefulness, attitude toward using AI, behavioral intention, and continuance intention in AI-supported learning.Methods – A quantitative cross-sectional survey was conducted with 324 students from the Educational Informatics and Computer Engineering and Computer Engineering programs at Universitas Negeri Makassar. Data were collected from January to February 2025 using a 25-item Likert-scale questionnaire and analyzed with PLS-SEM in SmartPLS 4 using bootstrapping with 10,000 subsamples.Findings – All hypothesized relationships were significant: AI literacy → perceived usefulness (β = 0.449, p < 0.001), AI literacy → attitude toward using AI (β = 0.177, p < 0.001), perceived usefulness → attitude toward using AI (β = 0.686, p < 0.001), attitude toward using AI → behavioral intention (β = 0.757, p < 0.001), and behavioral intention → continuance intention (β = 0.800, p < 0.001). Research implications – The cross-sectional design, self-reported data, purposive sampling, and single-institution context limit causal inference and broader generalizability.Originality – This study extends TAM by positioning AI literacy as a competence-based antecedent within a post-adoption sequence that explains continuance intention through perceived usefulness, attitude, and behavioral intention. Practically, the findings suggest that sustainable AI integration in higher education requires not only access to AI tools but also structured support for students’ AI literacy.
Explaining Students’ Continuance Intention to Use Artificial Intelligence in Higher Education: A Post-Adoption TAM Model Muhammad Nur Faiz; Muhammad Yahya; Rosidah; Sanatang
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.v5i1.267

Abstract

Purpose – This study examines continuance intention to use artificial intelligence in higher education by integrating system quality, self efficacy in artificial intelligence usage, and artificial intelligence literacy into a post adoption technology acceptance framework. The study clarifies whether sustained use is driven more by technical and usability related evaluations than by literacy alone. Design/methods/approach – A quantitative cross sectional survey was conducted with 324 undergraduate students from the Informatics and Computer Engineering Education and Computer Engineering study programs at Universitas Negeri Makassar. Data were collected through an online questionnaire and analyzed using Partial Least Squares Structural Equation Modeling with SmartPLS 4. Findings – Self-efficacy in artificial intelligence usage significantly affected artificial intelligence literacy (β = 0.447, p < 0.001), system quality significantly affected perceived ease of use (β = 0.733, p < 0.001) and perceived usefulness (β = 0.266, p < 0.001), perceived ease of use significantly affected perceived usefulness (β = 0.502, p < 0.001), and perceived usefulness significantly affected continuance intention (β = 0.637, p < 0.001). Artificial intelligence literacy did not significantly affect perceived usefulness (β = 0.072, p = 0.093). Research implications/limitations – The findings are limited by the cross-sectional design, self-reported data, and the focus on two technology-oriented study programs in one university. Originality/value – This study contributes a focused post-adoption explanation of sustained artificial intelligence use by showing that continuance is shaped more strongly by system performance and perceived academic value than by artificial intelligence literacy alone.
Co-Authors Abdul Muis Mappalotteng Fathahillah Fitria Arifyanti Muhammad Yahya Rosidah Rosidah Sanatang Selen Subasi