<![CDATA[This study aimed to analyze the trends of pedagogical interventions, implementation contexts, and learning outcomes reported in studies on Artificial Intelligence (AI)-based chemistry learning. The study employed a Systematic Literature Review (SLR) design guided by the PRISMA 2020 framework. Data were collected through a literature search in relevant academic databases, namely Scopus, Web of Science, ERIC, and Google Scholar, using purposive sampling based on predetermined inclusion and exclusion criteria. The research instruments consisted of an article screening sheet and a data extraction matrix covering author, year, research context, chemistry topic, type of AI, research design, sample, instrument, main findings, and research gaps. The data were analyzed using thematic narrative analysis of 15 primary studies that met the eligibility criteria. The findings revealed four major themes in the use of AI in chemistry learning: AI as a tutor/chatbot/learning assistant, AI in virtual and immersive learning environments, perceptions and challenges of AI implementation, and AI as a support for pedagogical strategies. The review also showed that AI has primarily functioned as an instructional support system, a means of personalizing learning, and a tool for strengthening the visualization of abstract chemical concepts rather than replacing the role of the teacher. In addition, the benefits of AI were most consistently observed in improving engagement, satisfaction, and learning experience, whereas its effects on cognitive learning outcomes remained inconsistent across studies. Therefore, AI holds considerable potential for chemistry learning; however, its effective implementation requires adequate pedagogical, ethical, and institutional support.]]>
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