AI-driven innovations in higher learning institutions: a review of pre-service teachers’ pedagogical competence through technology integration

This study reviews the literature on artificial intelligence (AI)-driven innovations in higher learning institutions with a focus on pre-service teachers’ pedagogical competence through technology integration. It aims to identify how AI-driven tools assist teacher preparation and what opportunities and challenges emerge. Despite growing interest in AI across education, research specifically examining pre-service teachers’ pedagogical competence in higher education contexts remains limited. This study reviewed literature published between 2015 and 2025, sourced from five databases: Google Scholar, PsycINFO, ERIC, SCOPUS, and Web of Science. Key findings identify six AI tools used in teacher education, including adaptive learning systems, VR/AR simulations, generative AI (ChatGPT, DeepSeek), and learning analytics. The study also highlights several gaps in the field, including a lack of longitudinal studies, limited cross-cultural research, insufficient attention to ethical and data privacy concerns, and inadequate AI literacy among pre-service teachers. The proposed Technological Pedagogical Content Knowledge (TPACK)-AI competency framework offers a three-tiered developmental progression, foundational literacy, applied pedagogical integration, and professional AI fluency, to guide curriculum design for AI-enhanced teacher preparation. However, empirical validation of this framework through longitudinal and intervention-based studies remains urgently needed.