<![CDATA[Science learning in the era of Artificial Intelligence (AI) and data necessitates the development of students’ data literacy as an essential 21st-century competency. However, students’ data literacy remains underdeveloped, particularly in terms of data analysis and utilization in learning contexts. This study aims to examine students’ data literacy profile in AI-, learning analytics-, and ethnoscience-based science learning. A descriptive quantitative approach was employed, involving eighth-grade junior high school students selected through purposive sampling. Data were collected using a Likert-scale questionnaire validated for reliability (Cronbach’s Alpha = 0.871) and analyzed using descriptive statistics, including mean scores and categorical classification. The results indicate that students’ data literacy was categorized as moderate (mean = 2.92), and learning analytics was also categorized as moderate (mean = 2.91), while artificial intelligence (mean = 3.23) and ethnoscience (mean = 3.23) were categorized as high. These findings suggest that students are able to utilize technology and understand scientific concepts within local contexts; however, their ability to analyze and use data remains underdeveloped. These findings imply the importance of designing science learning in a more structured manner to promote data analysis activities through the contextual and meaningful integration of AI, learning analytics, and ethnoscience.]]>
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