<![CDATA[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.]]>
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