Role of AI in Blended Learning: A Systematic Literature Review
Open accessAbstract
As blended learning moved toward a new phase during the COVID-19 pandemic, advancements in artificial intelligence (AI) technology provided opportunities to develop more diverse and dynamic blended learning. This systematic review focused on publications related to the use of AI applications in blended learning. The original studies from January 2007 to October 2023 were extracted from the Google Scholar, ERIC, and Web of Science databases. Finally, 30 empirical studies under the inclusion criteria were reviewed based on two conceptual frameworks: four key challenges of blended learning and three roles of AI. We found that AI applications have been used mainly for the online asynchronous individual learning component in blended learning; little work has been conducted on AI applications that help connect online activities with classroom-based offline activities. Many studies have identified the role of AI as a direct mediator to help control flexibility and autonomy of students in blended learning. However, abundant studies have also identified AI as a supplementary assistant using advanced learning analytics technologies that promote effective interactions with students and facilitate the learning process. Finally, the fewest number of studies have explored the role of AI as a new subject such as use as pedagogical agents or robots. Considering the advancements of generative AI technologies, we expect more research on AI in blended learning. The findings of this study suggested that future studies should guide teachers and their smart AI partner to implement blended learning more effectively.
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AI research in K-12 continues to privilege traditional AI (e.g., ITS), emphasizing tool properties or AI literacy instead of tracing how AI is pedagogically enacted to enhance learning.
“AI research in k-12 continues to privilege traditional AI (e.g., ITS) [30], emphasizing tool properties [31,32] or AI literacy [31,33,34] instead of tracing how AI is pedagogically enacted to enhance learning.”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [32]
The Artificial Intelligence in Education (AIEd) section describes ITS as the most common application in AIEd generally. It does not establish that K–12 AI research specifically continues to privilege ITS. The claims about tool properties, AI literacy, and pedagogical enactment are attached to other citations in the printed sentence. (LLM, conf=0.83)
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Wang, J., & Fan, W. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70137 · Cited by 14 · see citing works
An AI-Driven News Impact Monitoring Framework Through Attention Tracking
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English Teachers’ Experiences with Artificial Intelligence-Driven Students’ Outputs in the Classroom
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From Fragmentation to Integration
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Towards a Flipped Learning Ecosystem: A Generative Artificial Intelligence-Enabled Framework
ElGamal, H. (2025) · European Journal of Open Distance and E-Learning · doi:10.65043/eurodl.151 · Cited by 1 · see citing works
Integration of Artificial Intelligence in Blended Business Education
Berraies, S., & Rejeb, W. B. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3815-6.ch008 · Cited by 1 · see citing works
Using Blended Learning and AI in Teaching Physics in Higher Education Institutions
Shapiyeva, A., Baigisova, K. B., Altybayev, G., Daineko, Y. A., Nurakhmetova, S., & Abdramanova, G. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.10.228 · Cited by 1 · see citing works
Exploring Equity, Diversity, and Inclusion in Blended Learning: Trends and Insights
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An AI-Enhanced, Case-Based Approach to Blended C Programming Instruction
Zhang, D., Zhang, J., Zhao, D., Ma, X., & Wang, J. (2025) · International Conference on Modern Education and Information Management (ICMEIM) · doi:10.1109/icmeim66684.2025.11307020 · Cited by 1 · see citing works
Predicting students’ continuous intention of blended design studio: a PLS-SEM and IPMA analysis
Fei, X., Zhang, Q., Xia, Y., & Xueting, H. (2026) · International Journal of Technology and Design Education · doi:10.1007/s10798-025-10051-w · Cited by 1 · see citing works
Etesse, M., Vidal, P. B., Gómez-Alegría, F., & Oliveira, D. V. (2025) · IEEE International Conference on Advanced Learning Technologies on Education & Research (ICALTER) · doi:10.1109/icalter69698.2025.11355077 · Cited by 1 · see citing works
BDGKT: Bidirectional dynamic graph knowledge tracing
Ou, X., Huang, T., Hu, S., Yang, H., Xu, Z., Hu, J., & Geng, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115532 · Cited by 1 · see citing works
Redes sociotécnicas en la apropiación tecnológica docente: un análisis desde la Teoría del Actor-Red
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Deutscher, V. K., Thomann, H., Zlatkin‐Troitschanskaia, O., Weyland, U., Abele, S., Danek, A. H., Greiff, S., Rausch, A., Seeber, S., Seifried, J., et al. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100628 · Cited by 4 · see citing works
A community of inquiry perspective on human–AI co-facilitation within micro-blended learning
Pan, Z., Jiang, Z., Ba, S., & Xie, K. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105707 · Cited by 2 · see citing works
Self-regulated approaches to students’ generative AI use in higher education
Hakola, O. J., Kukkonen, J., & Valtonen, T. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101340 · Cited by 1 · see citing works
Cost Analysis and Return on Investment for Blended Learning
Panda, S. (2024) · Springer eBooks · doi:10.1007/978-981-97-9388-4_7 · Cited by 1 · see citing works
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Defining Key Modality Parameters of Blended Courses
Kirby, C. K., Tetu, I. C., & Henley, C. L. (2025) · Journal of Educational Technology Systems · doi:10.1177/00472395251330649
How Effective are AI Tools for Diverse Learners? A Case Study on Python and Data Science Education
Kehar, A., Abro, A., & Fatima, S. (2025) · VAWKUM Transactions on Computer Sciences · doi:10.21015/vtcs.v13i1.2075
Teachers’ Perspectives on Employing Diffit for AI-Assisted EFL Reading Tasks
Nguyen, T. L., & Nguyen, H. L. (2025) · Studies in computational intelligence · doi:10.1007/978-3-032-01348-4_9
AI-driven Pedagogical Approaches to Foreign Language Teaching and Learning
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From One-Size-Fits-All to Just-for-Me
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Traces of the Past: A Glimpse into the Journey of Alternative Learning System Students
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Kannaujia, S. K., & Patel, M. (2025) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2025.1410000112
Universidad 5.0: integrando la inteligencia artificial para transformar la educación superior
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Predicting Student Academic Performance Via Deep Learning: PyTorch-Based Study on UCI Dataset
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Transforming Teaching and Learning in Business Education
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Harnessing AI in Blended Learning for Language Skill Development
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Use of generative AI in database courses: a role-based review
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AI-Enabled Blended Teaching Innovation and Practice in Military Academy Engineering Education
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The pedagogical landscape of blended learning in higher education: a bibliometric analysis
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Toward Intelligent Assessment in HyFlex Learning
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Personalized smart courses for university physics based on knowledge graph and large language models
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AI-Generated Visuals for Blended Learning: A Design Framework for Hybrid Classrooms
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Investigating the Impact of AI-Facilitated Socratic Dialogue on Critical Thinking Skills
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Artificial Intelligence-Based Blended Learning: A Mixed-Methods Study
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- Authors
- Park, Y., & Doo, M. Y.
- Affiliations
- Honam University (KR)
- Kangwon National University (KR)
- Corresponding author
- Park, Yeonjeong
- Doo, Min Young
- Venue
- The International Review of Research in Open and Distributed Learning
- Publisher
- Athabasca University Press
- Published
- 2024-03-01
- Volume
- 25
- Issue
- 1
- Pages
- 164-196
- ISSN
- 1492-3831 (ISSN-L)
- OpenAlex
- W4392351600
- Cited by
- 81
- References
- 85
- Citations by year
- 2026: 40 · 2025: 33 · 2024: 7 · 2020: 1
- FWCI
- 4.76
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- The International Review of Research in Open and Distributed Learning (publishedVersion, cc-by)
- Érudit (Université de Montréal) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Honam University
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Online and Blended Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- artificial intelligence in education, blended learning, COVID-19 pandemic, asynchronous online learning, student independence, learning analytics, educational agents, educational robots, generative AI, online-offline integration
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