Artificial Intelligence in Blended Learning for Higher Education: A Systematic Literature Review (2020–2025)
DOI:
https://doi.org/10.53905/edu.v1i01.07Keywords:
artificial intelligence, blended learning, higher education, intelligent tutoring systems, adaptive learning, learning analytics, educational technologyAbstract
Purpose of the study: This systematic literature review investigates the integration of Artificial Intelligence (AI) technologies within blended learning environments in higher education institutions globally, published between 2020 and 2025. The study aims to synthesize empirical evidence on AI-enhanced blended learning models, identify prevalent AI tools and pedagogical approaches, evaluate learning outcomes, and map research trends, challenges, and future directions.
Materials and methods: Following the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic search was conducted across five major academic databases: Scopus, Web of Science (WoS), ERIC, IEEE Xplore, and Google Scholar. Search terms combined AI-related terminology with blended learning and higher education concepts. Studies published between January 2020 and March 2025, written in English, employing empirical or quasi-experimental designs, and focusing on tertiary education were included. After rigorous screening and quality assessment using the Mixed Methods Appraisal Tool (MMAT), 47 studies met the inclusion criteria and were subjected to thematic synthesis and descriptive analysis.
Results: The analysis of 47 eligible studies revealed six dominant AI application categories in blended learning: intelligent tutoring systems (ITS) (27.7%), natural language processing and chatbots (23.4%), adaptive learning platforms (21.3%), AI-driven learning analytics (14.9%), AI-based assessment tools (8.5%), and generative AI tools (4.3%). The majority of studies reported statistically significant improvements in academic performance (85.1%), learner engagement (78.7%), and personalized learning experiences (72.3%). Key challenges identified include algorithmic bias, data privacy concerns, insufficient instructor AI literacy, and inequitable digital access.
Conclusions: AI integration in blended learning environments demonstrates significant promise in enhancing pedagogical effectiveness and learner outcomes in higher education. However, sustainable and equitable deployment requires robust ethical frameworks, targeted professional development for educators, and inclusive institutional policies. Future research should prioritize longitudinal studies and cross-cultural comparative analyses.
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