["Journal of Computer Assisted Learning, Volume 42, Issue 4, August 2026. ", "\nABSTRACT\n\nBackground\nThe BOPPPS instructional model (Bridge‐in, Objective, Pre‐assessment, Participatory Learning, Post‐assessment, Summary) is widely used in higher education to promote structured and student‐centred learning. With the rapid advancement of artificial intelligence (AI), educators are increasingly exploring how AI technologies can support teaching and learning within this framework. However, existing research on AI‐integrated BOPPPS instruction remains scattered, and a systematic synthesis of current evidence is still lacking.\n\n\nObjectives\nThis study aims to systematically review research on the integration of AI technologies into the BOPPPS instructional model in higher education and examine their effects on teaching and learning outcomes.\n\n\nMethods\nA systematic review was conducted following the PRISMA 2020 guidelines. Multiple academic databases were searched for empirical studies published between 2015 and 2025, and studies were screened using predefined inclusion criteria. Forty‐six eligible studies were included and analysed to identify the types of AI technologies used, their application across different BOPPPS stages, and their reported educational impacts.\n\n\nResults and Conclusions\nThe review shows a growing body of research, particularly after 2018, integrating technologies such as intelligent tutoring systems, machine learning, virtual and augmented reality, and generative AI into BOPPPS‐based teaching. These technologies were reported to support personalised learning, increase student engagement, and facilitate data‐informed instruction, leading to improved academic performance, enhanced higher‐order thinking skills, and stronger learning motivation. Nevertheless, many studies remain small‐scale and quasi‐experimental, and challenges related to data privacy, teacher readiness, and equitable access persist. Future research should prioritise large‐scale and theory‐driven studies to strengthen the evidence base and support effective implementation.\n\n"]