<![CDATA[This study investigates the impact of artificial intelligence (AI) on science literacy, learning motivation, and self-efficacy among Indonesian higher education students, addressing a critical gap in educational innovation. Despite growing technological advances, science literacy in Indonesia remains below the OECD average, highlighting an urgent need for transformative learning strategies. Utilizing a Structural Equation Modeling (SEM) approach with Partial Least Squares (PLS-SEM) and bootstrapping (5,000 resamples) on a purposive sample of 180 students from science and technology programs, the research integrates three key constructs—science literacy, motivation, and self-efficacy—to provide a comprehensive analysis of their interrelationships in the context of AI-supported science education. Participants uniformly reported previous engagement with AI-integrated learning environments. Descriptive findings indicate high mean scores, notably a 4.339 average in basic science concept understanding and similarly strong results in self-efficacy and AI implementation. Outer loadings for all constructs exceeded the 0.70 threshold, ensuring robust measurement validity. Path analysis revealed that science literacy powerfully predicts AI implementation (coefficient: 0.853, p < 0.001, 95% CI [0.78, 0.92]) and moderately affects self-efficacy (coefficient: 0.143, p = 0.015, 95% CI [0.03, 0.26]), while AI implementation strongly influences motivation (coefficient: 0.404, p < 0.001, 95% CI [0.29, 0.52]). These results demonstrate the significant and integrated roles of literacy and self-efficacy in fostering student preparedness for AI-driven learning. The study offers valuable insights for enhancing science education and advancing digital competency in developing contexts.]]>
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