Purpose Artificial intelligence (AI)-driven flipped classroom is one of the novel approaches in the education field that combines multimedia resources with learner-centered instruction. This study aims to examine the impact of AI-driven flipped classrooms to enhance students’ self-regulated learning (SRL). Moreover, the joint effects of AI tools and flipped classroom (FC) on students’ SRL, is a gap area with less empirical evidence, particularly in developing countries. Unlike the previous FC research, their research is dedicated to AI-based FCs that use intelligent digital content, individualized learning pace and data-driven analytics for learner engagement. Design/methodology/approach A quantitative research approach was used and data was collected from the university students (n = 385) using a questionnaire administered through purposive. The statistical package for social sciences (SPSS) version 27.0 is used to analyze data. Findings The findings of the study reveal that there is a strong positive relationship between AI-driven flipped classrooms and SRL. The value of the correlation coefficient value of the variables is 0.731, which indicates a positive and strong relationship; hence, the null hypothesis is rejected and an alternative hypothesis is accepted. Practical implications The results of this study benefit diverse student learning preferences by supporting self-regulation. Besides, they provide some context-specific implications for AI-driven digital instruction in the Pakistani higher education domain. Instructors must ensure adequate digital competencies and resource accessibility to effectively implement artificial intelligence driven flipped classroom. Institutional efforts should focus on enhancing teachers’ readiness for integrating AI-driven tools to boost learners’ collaboration and critical thinking. Originality/value This study is relevant to the current AI-enhanced pedagogy in three significant aspects. First, it is an empirical study examining the integration of artificial intelligence within a flipped classroom setting (AIDFC), as opposed to exploring artificial intelligence or flipped learning itself. Second, it expands on the Technology Acceptance Model (TAM) by connecting student perceptions and attitudes to multidimensional SRL (cognitive, metacognitive and motivational elements) and past conventional outcomes of technology adoption. Third, it offers context-relevant empirical data of Pakistani higher education, an underrepresented context in the study of AI in education. The combination of TAM and SRL in a predictive framework provides the research with both theoretical enrichment and practical understanding of the practice of AI-assisted instruction in settings with the developing context.
Asad et al. (Wed,) studied this question.