Role of AI in Blended Learning: A Systematic Literature Review

Authors

DOI:

https://doi.org/10.19173/irrodl.v25i1.7566

Keywords:

blended learning, artificial intelligence, systematic review, AI in education

Abstract

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.

Author Biographies

Yeonjeong Park, Department of Early Childhood Education, Honam University

Yeonjeong Park is an assistant professor at the Department of Early Childhood Education at Honam University, Korea. Her research interests are social theories of learning, mobile learning, educational data mining, learning analytics, and emerging technologies in education and training. She can be reached at ypark@honam.ac.kr

Min Young Doo, Department of Education, Kangwon National University

Min Young Doo is an assistant professor in the Department of Education in the College of Education at Kangwon National University, Korea. Her research interests include instructional design, online learning, flipped learning, and human resource development. She can be reached at mydoo@Kangwon.ac.kr

References

*References marked with an asterisk indicate studies included in the systematic review.

Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152

*Al-Kaisi, A. N., Arkhangelskaya, A. L., & Rudenko-Morgun, O. I. (2021). The didactic potential of the voice assistant “Alice” for students of a foreign language at a university. Education and Information Technologies, 26, 715–732. https://doi.org/10.1007/s10639-020-10277-2

AlKhuzaey, S., Grasso, F., Payne, T. R., & Tamma, V. (2021). A systematic review of data-driven approaches to item difficulty prediction [Paper presentation]. International Conference on Artificial Intelligence in Education. https://doi.org/10.1007/978-3-030-78292-4_3

Allen, I. E., Seaman, J., & Garrett, R. (2007). Blending in: The extent and promise of blended education in the United States. The Sloan Consortium. https://files.eric.ed.gov/fulltext/ED529930.pdf

Alshahrani, A. (2023). The impact of ChatGPT on blended learning: Current trends and future research directions. International Journal of Data and Network Science, 7(4), 2029–2040. http://dx.doi.org/10.5267/j.ijdns.2023.6.010

*Ameloot, E., Rotsaert, T., & Schellens, T. (2022). The supporting role of learning analytics for a blended learning environment: Exploring students’ perceptions and the impact on relatedness. Journal of Computer Assisted Learning, 38(1), 90–102. https://doi.org/10.1111/jcal.12593

*Annamalai, N., Eltahir, M. E., Zyoud, S. H., Soundrarajan, D., Zakarneh, B., & Al Salhi, N. R. (2023). Exploring English language learning via Chabot: A case study from a self determination theory perspective. Computers and Education: Artificial Intelligence, 5, 100148. https://doi.org/10.1016/j.caeai.2023.100148

Arizmendi, C. J., Bernacki, M. L., Raković, M., Plumley, R. D., Urban, C. J., Panter, A., Greene, J. A., & Gates, K. M. (2022). Predicting student outcomes using digital logs of learning behaviors: Review, current standards, and suggestions for future work. Behavior Research Methods, 55, 1–29. https://doi.org/10.3758/s13428-022-01939-9

Balfour, S. P. (2013). Assessing writing in MOOCs: Automated essay scoring and Calibrated Peer Review™. Research & Practice in Assessment, 8, 40–48. https://files.eric.ed.gov/fulltext/EJ1062843.pdf

Bergdahl, N., Nouri, J., Karunaratne, T., Afzaal, M., & Saqr, M. (2020). Learning analytics for blended learning: A systematic review of theory, methodology, and ethical considerations. International Journal of Learning Analytics and Artificial Intelligence for Education, 2(2), 46–79. https://doi.org/10.3991/ijai.v2i2.17887

Bergmann, J., & Sams, A. (2014). Flipped learning: Gateway to student engagement. International Society for Technology in Education. https://doi.org/10.1007/s12528-013-9077-3

Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. Journal of Computing in Higher Education, 26(1), 87–122. https://doi.org/10.1007/s12528-013-9077-3

Bhutoria, A. (2022). Personalized education and artificial intelligence in United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 100068. https://doi.org/10.1016/j.caeai.2022.100068

bin Mohamed, M. Z., Hidayat, R., binti Suhaizi, N. N., bin Mahmud, M. K. H., & binti Baharuddin, S. N. (2022). Artificial intelligence in mathematics education: A systematic literature review. International Electronic Journal of Mathematics Education, 17(3), em0694. https://doi.org/10.29333/iejme/12132

Boelens, R., De Wever, B., & Voet, M. (2017). Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review, 22, 1–18. https://doi.org/10.1016/j.edurev.2017.06.001

Caner, M. (2012). The definition of blended learning in higher education. In P. Anastasiades (Ed.), Blended learning environments for adults: Evaluations and frameworks (pp. 19–34). IGI Global. https://doi.org/10.4018/978-1-4666-0939-6.ch002

Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, 616–630. https://doi.org/10.1007/s11528-022-00715-y

*Chatzara, E., Kotsakis, R., Tsipas, N., Vrysis, L., & Dimoulas, C. (2019). Machine-assisted learning in highly interdisciplinary media fields: A multimedia guide on modern art. Education Sciences, 9(3), 198. https://doi.org/10.3390/educsci9030198

*Chen, X., Breslow, L., & DeBoer, J. (2018). Analyzing productive learning behaviors for students using immediate corrective feedback in a blended learning environment. Computers & Education, 117, 59–74. https://doi.org/10.1016/j.compedu.2017.09.013

Chen, X., Xie, H., & Hwang, G.-J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, 100005. https://doi.org/10.1016/j.caeai.2020.100005

Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28–47. https://www.jstor.org/stable/48647028

Chu, H.-C., Hwang, G.-H., Tu, Y.-F., & Yang, K.-H. (2022). Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most-cited articles. Australasian Journal of Educational Technology, 38(3), 22–42. https://doi.org/10.14742/ajet.7526

Cooper, H. M. (1988). Organizing knowledge syntheses: A taxonomy of literature reviews. Knowledge in Society, 1(1), 104. https://doi.org/10.1007/BF03177550

Crompton, H., Jones, M. V., & Burke, D. (2022). Affordances and challenges of artificial intelligence in K–12 education: A systematic review. Journal of Research on Technology in Education, 1–21. http://dx.doi.org/10.1080/15391523.2022.2121344

Cronje, J. (2020). Towards a new definition of blended learning. Electronic Journal of e-Learning, 18(2), 114–121. https://doi.org/10.34190/EJEL.20.18.2.001

*Dingus, R., & Black, H. G. (2021). Choose your words carefully: An exercise to introduce artificial intelligence to the marketing classroom using tone analysis. Marketing Education Review, 31(2), 64–69. http://dx.doi.org/10.1080/10528008.2020.1843361

Driscoll, M. (2002). Blended learning: Let’s get beyond the hype. E-learning, 1(4), 1–4. https://www.academia.edu/download/7691892/blended_learning.pdf

Du, Y. (2021). Systematic review of artificial intelligence in language learning. 2021 International Conference on Intelligent Manufacturing Technology and Information Technology. http://166.62.7.99/conferences/AEASR/IMTIT%202021/IMTIT007.pdf

du Boulay, B. (2016). Artificial intelligence as an effective classroom assistant. IEEE Intelligent Systems, 31(6), 76–81. https://doi.org/10.1109/MIS.2016.93

Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: The new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15(1), 1–16. https://doi.org/10.1186/s41239-017-0087-5

*Fang, J.-W., Chang, S.-C., Hwang, G.-J., & Yang, G. (2021). An online collaborative peer-assessment approach to strengthening pre-service teachers’ digital content development competence and higher-order thinking tendency. Educational Technology Research and Development, 69(2), 1155–1181. https://doi.org/10.1007/s11423-021-09990-7

*Fang, Y., Lippert, A., Cai, Z., Chen, S., Frijters, J. C., Greenberg, D., & Graesser, A. C. (2021). Patterns of adults with low literacy skills interacting with an intelligent tutoring system. International Journal of Artificial Intelligence in Education, 32, 297–322. https://doi.org/10.1007/s40593-021-00266-y

Floridi, L. (2014). The 4th revolution: How the infosphere is reshaping human reality. Oxford University Press.

Friesen, N. (2012). Report: Defining blended learning. https://www.normfriesen.info/papers/Defining_Blended_Learning_NF.pdf

Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105. https://doi.org/10.1016/j.iheduc.2004.02.001

Garrison, D. R. (2016). Thinking collaboratively: Learning in a community of inquiry. New York & London: Routledge.

Gera, R., & Chadha, P. (2021). Systematic review of artificial intelligence in higher education (2000–2020) and future research directions. In W. B. James, C. Cobanoglu, & M. Cavusoglu (Eds.), Advances in global education and research (Vol. 4, pp. 1–12). USF M3 Publishing https://www.doi.org/10.5038/9781955833042

González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial intelligence for student assessment: A systematic review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/app11125467

Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions In C. J. Bonk & C. R. Graham (Eds.), Handbook of blended learning: Global perspectives, local designs (pp. 3–21). Pfeiffer Publishing.

Graham, C. R., Henrie, C. R., & Gibbons, A. S. (2013). Developing models and theory for blended learning research. In A. G. Picciano, C. D. Dziuban, & C. R. Graham (Eds.), Blended learning: Research perspective (Vol. 2). Routledge.

Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134–147. https://doi.org/10.1016/j.ijis.2020.09.001

Gunawardena, C. N., & Zittle, F. J. (1997). Social presence as a predictor of satisfaction within a computer‐mediated conferencing environment. American Journal of Distance Education, 11(3), 8–26. https://doi.org/10.1080/08923649709526970

Halaweh, M. (2023). ChatGPT in education: Strategies for responsible implementation. Contemporary Educational Technology, 15(2), ep421. https://doi.org/10.30935/cedtech/13036

Hashim, S., Omar, M. K., Ab Jalil, H., & Sharef, N. M. (2022). Trends on technologies and artificial intelligence in education for personalized learning: Systematic literature review. Journal of Academic Research in Progressive Education and Development, 12(1), 884–903. http://doi.org/10.6007/IJARPED/v11-i1/12230

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. The Center for Curriculum Redesign. https://doi.org/10.58863/20.500.12424%2F4273108

Hoofman, J., & Secord, E. (2021). The effect of COVID-19 on education. Pediatric Clinics, 68(5), 1071–1079. https://doi.org/10.1016/j.pcl.2021.05.009

Horn, M. B., & Staker, H. (2014). Blended: Using disruptive innovation to improve schools. John Wiley & Sons.

Hrastinski, S. (2019). What do we mean by blended learning? TechTrends, 63(5), 564–569. https://doi.org/10.1007/s11528-019-00375-5

*Huang, A. Y., Lu, O. H., & Yang, S. J. (2023). Effects of artificial intelligence-enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684

Hwang, G.-J., Lai, C.-L., & Wang, S.-Y. (2015). Seamless flipped learning: A mobile technology-enhanced flipped classroom with effective learning strategies. Journal of Computers in Education, 2, 449–473. https://doi.org/10.1007/s40692-015-0043-0

Hwang, G.-J., & Tu, Y.-F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584

Hwang, G.-J., Tu, Y.-F., & Tang, K.-Y. (2022). AI in online-learning research: Visualizing and interpreting the journal publications from 1997 to 2019. International Review of Research in Open and Distributed Learning, 23(1), 104–130. https://doi.org/10.19173/irrodl.v23i1.6319

*Hwang, G.-J., Zou, D., & Lin, J. (2020). Effects of a multi-level concept mapping-based question-posing approach on students’ ubiquitous learning performance and perceptions. Computers & Education, 149, 103815. https://doi.org/10.1016/j.compedu.2020.103815

*Jia, J., Chen, Y., Ding, Z., & Ruan, M. (2012). Effects of a vocabulary acquisition and assessment system on students’ performance in a blended learning class for English subject. Computers & Education, 58(1), 63–76. https://doi.org/10.1016/j.compedu.2011.08.002

*Jovanović, J., Gašević, D., Dawson, S., Pardo, A., & Mirriahi, N. (2017). Learning analytics to unveil learning strategies in a flipped classroom. The Internet and Higher Education, 33(4), 74–85. https://doi.org/10.1016/j.iheduc.2017.02.001

Kurdi, G., Leo, J., Parsia, B., Sattler, U., & Al-Emari, S. (2020). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30(1), 121–204. https://doi.org/10.1007/s40593-019-00186-y

*Lechuga, C. G., & Doroudi, S. (2022). Three algorithms for grouping students: A bridge between personalized tutoring system data and classroom pedagogy. International Journal of Artificial Intelligence in Education, 33, 1–42. https://doi.org/10.1007/s40593-022-00309-y

Li, Y., Jiang, A., Li, Q., & Zhu, C. (2022). The analysis of research hot spot and trend on artificial intelligence in education. International Journal of Learning and Teaching, 8(1), 49–52. http://www.ijlt.org/uploadfile/2022/0214/20220214024004480.pdf

Liang, J.-C., Hwang, G.-J., Chen, M.-R. A., & Darmawansah, D. (2021). Roles and research foci of artificial intelligence in language education: An integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 31, 1–27. https://doi.org/10.1080/10494820.2021.1958348

*Liao, C.-H., & Wu, J.-Y. (2022). Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance. Computers & Education, 190, 104599. https://doi.org/10.1016/j.compedu.2022.104599

*Lin, C.-J., & Mubarok, H. (2021). Learning analytics for investigating the mind map-guided AI chatbot approach in an EFL flipped speaking classroom. Educational Technology & Society, 24(4), 16–35. https://www.jstor.org/stable/48629242

*Liu, G.-Z., Lo, H.-Y., & Wang, H.-C. (2013). Design and usability testing of a learning and plagiarism avoidance tutorial system for paraphrasing and citing in English: A case study. Computers & Education, 69, 1–14. https://doi.org/10.1016/j.compedu.2013.06.011

*Lu, O. H., Huang, A. Y., Tsai, D. C., & Yang, S. J. (2021). Expert-authored and machine-generated short-answer questions for assessing students learning performance. Educational Technology & Society, 24(3), 159–173. https://www.jstor.org/stable/27032863

Mali, D., & Lim, H. (2021). How do students perceive face-to-face/blended learning as a result of the COVID-19 pandemic? The International Journal of Management Education, 19(3), 100552. https://doi.org/10.1016/j.ijme.2021.100552

Mantyla, K. (2001). Blended e-learning: The power is in the mix. American Society for Training and Development.

Margulieux, L. E., McCracken, W. M., & Catrambone, R. (2016). A taxonomy to define courses that mix face-to-face and online learning. Educational Research Review, 19, 104–118. https://doi.org/10.1016/j.edurev.2016.07.001

Martin, F., Wu, T., Wan, L., & Xie, K. (2022). A meta-analysis on the community of inquiry presences and learning outcomes in online and blended learning environments. Online Learning, 26(1), 325–359. https://files.eric.ed.gov/fulltext/EJ1340511.pdf

*Mavrikis, M., Geraniou, E., Gutierrez Santos, S., & Poulovassilis, A. (2019). Intelligent analysis and data visualisation for teacher assistance tools: The case of exploratory learning. British Journal of Educational Technology, 50(6), 2920–2942. https://doi.org/10.1111/bjet.12876

*Méndez, J. A., & González, E. J. (2010). A reactive blended learning proposal for an introductory control engineering course. Computers & Education, 54(4), 856–865. https://doi.org/10.1016/j.compedu.2009.09.015

Méndez, J. A., & González, E. J. (2013). A control system proposal for engineering education. Computers & Education, 68, 266–274. https://doi.org/10.1016/j.compedu.2013.05.014

*Montgomery, A. P., Mousavi, A., Carbonaro, M., Hayward, D. V., & Dunn, W. (2019). Using learning analytics to explore self‐regulated learning in flipped blended learning music teacher education. British Journal of Educational Technology, 50(1), 114–127. https://doi.org/10.1111/bjet.12590

Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2021). Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 29(1), 142–163. https://doi.org/10.1080/10494820.2018.1558257

Müller, C., & Mildenberger, T. (2021). Facilitating flexible learning by replacing classroom time with an online learning environment: A systematic review of blended learning in higher education. Educational Research Review, 34, 100394. https://doi.org/10.1016/j.edurev.2021.100394

Neo, M. (2022). The Merlin project3: Malaysian students’ acceptance of an AI chatbot in their learning process. Turkish Online Journal of Distance Education, 23(3), 31–48. https://doi.org/10.17718/tojde.1137122

*Ng, D. T. K., & Chu, S. K. W. (2021). Motivating students to learn AI through social networking sites: A case study in Hong Kong. Online Learning, 25(1), 195–208. http://files.eric.ed.gov/fulltext/EJ1287128.pdf

Norberg, A. (2017). From blended learning to learning onlife: ICTs, time and access in higher education [Doctoral dissertation, Umeå University]. https://umu.diva-portal.org/smash/record.jsf?pid=diva2%3A1068011&dswid=5553

Oliver, M., & Trigwell, K. (2005). Can ‘blended learning’ be redeemed? E-learning, 2(1), 17–26. https://doi.org/10.2304/elea.2005.2.1.17

*Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology, 50(1), 128–138. https://doi.org/10.1111/bjet.12592

Park, Y., Yu, J. H., & Jo, I.-H. (2016). Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute. The internet and higher education, 29, 1-11. https://doi.org/10.1016/j.iheduc.2015.11.001

*Phillips, A., Pane, J. F., Reumann-Moore, R., & Shenbanjo, O. (2020). Implementing an adaptive intelligent tutoring system as an instructional supplement. Educational Technology Research and Development, 68, 1409–1437. https://doi.org/10.1007/s11423-020-09745-w

*Sánchez-Ruiz, L. M., Moll-López, S., Nuñez-Pérez, A., Moraño-Fernández, J. A., & Vega-Fleitas, E. (2023). ChatGPT challenges blended learning methodologies in engineering education: A case study in mathematics. Applied Sciences, 13(10), 6039. https://doi.org/10.3390/app13106039

Singh, H. (2003). Building effective blended learning programs. Educational Technology, 43(6), 51–54. https://doi.org/10.4018/978-1-7998-7607-6.ch002

Song, P., & Wang, X. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), 473–486. https://doi.org/10.1007/s12564-020-09640-2

Straw, S., Quinlan, O., Harland, J., & Walker, M. (2015). Flipped learning practitioner guide. National Foundation for Educational Research (NFER) and Nesta. https://media.nesta.org.uk/documents/Flipped_Learning.pdf

Tahiru, F. (2021). AI in education: A systematic literature review. Journal of Cases on Information Technology, 23(1), 1–20. https://doi.org/10.4018/JCIT.2021010101

Talbert, R. (2017). Flipped learning: A guide for higher education faculty. Stylus Publishing.

Tan, S. C., Lee, A. V. Y., & Lee, M. (2022). A systematic review of artificial intelligence techniques for collaborative learning over the past two decades. Computers and Education: Artificial Intelligence, 3, 100097. https://doi.org/10.1016/j.caeai.2022.100097

Tang, K.-Y., Chang, C.-Y., & Hwang, G.-J. (2021). Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interactive Learning Environments, 31(4), 2134–2152. https://doi.org/10.1080/10494820.2021.1875001

*Tran, T. P., & Meacheam, D. (2020). Enhancing learners’ experience through extending learning systems. IEEE Transactions on Learning Technologies, 13(3), 540–551. https://doi.org/10.1109/TLT.2020.2989333

*Troussas, C., Krouska, A., & Sgouropoulou, C. (2020). Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education. Computers & Education, 144, 103698. https://doi.org/10.1016/j.compedu.2019.103698

*Van Leeuwen, A. (2019). Teachers’ perceptions of the usability of learning analytics reports in a flipped university course: When and how does information become actionable knowledge? Educational Technology Research and Development, 67, 1043–1064. https://doi.org/10.1007/s11423-018-09639-y

Wang, Q., & Huang, C. (2018). Pedagogical, social and technical designs of a blended synchronous learning environment. British Journal of Educational Technology, 49(3), 451–462. https://doi.org/10.1111/bjet.12558

Whatley, J. (2004). An agent system to support student teams working online. Journal of Information Technology Education: Research, 3(1), 53–63. https://www.learntechlib.org/p/111440/

Xu, W., & Ouyang, F. (2021). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies, 27(3), 4195–4223. https://doi.org/10.1007/s10639-021-10774-y

*Yang, Y., Leung, H., Yue, L., & Deng, L. (2013). Generating a two-phase lesson for guiding beginners to learn basic dance movements. Computers & Education, 61, 1–20. https://doi.org/10.1016/j.compedu.2012.09.006

Yu, H. (2023). Reflection on whether ChatGPT should be banned by academia from the perspective of education and teaching. Frontiers in Psychology, 14, 1181712. https://doi.org/10.3389/fpsyg.2023.1181712

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0

Zhao, Y. (2020). COVID-19 as a catalyst for educational change. Prospects, 49(1), 29–33. https://doi.org/10.1007/s11125-020-09477-y

Zydney, J. M., Warner, Z., & Angelone, L. (2020). Learning through experience: Using design based research to redesign protocols for blended synchronous learning environments. Computers & Education, 143, 103678. https://doi.org/10.1016/j.compedu.2019.103678

Published

2024-03-01

How to Cite

Park, Y., & Doo, M. Y. (2024). Role of AI in Blended Learning: A Systematic Literature Review. The International Review of Research in Open and Distributed Learning, 25(1), 164–196. https://doi.org/10.19173/irrodl.v25i1.7566

Issue

Section

Literature Reviews