Authors : Sivasankar A.

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/6r7hyf3e

Scribd : https://tinyurl.com/235xtfef

DOI : https://doi.org/10.38124/ijisrt/26May2020

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Across the globe, artificial intelligence is becoming more common in mixed classroom settings, changing both teaching methods and how students interact with material. Findings pulled from various research projects - in schools ranging from elementary to university level - reflect trends seen in regions like Asia and Europe, among others. These investigations looked at different AI tools: some involved smart chat systems, voice assistants driven by algorithms, datatracking software for learning progress, adaptive course planners, even advanced text-generating models similar to ChatGPT. Results, time after time, show gains in student involvement, drive to learn, and scores on assessments - especially noticeable in picking up new languages or tackling complex technical tasks. While gaps in teacher readiness persist, concerns about ethics in leadership and reliability of assessments over time also need scrutiny. Findings unfold by theme here, shaped alongside worldwide shifts in how tech enters classrooms. Instead of quick fixes, deeper study - tracking outcomes patiently, using stronger methods - could guide wiser use of AI where digital tools meet face-to-face teaching.

Keywords : Artificial Intelligence, Blended Learning, Educational Technology, Student Engagement, Learning Analytics, Chatgpt, Personalized Learning, Higher Education.

References :

1. Bishop, J. L., & Verleger, M. A. (2013). The flipped classroom: A survey of the research. ASEE Annual Conference & Exposition Proceedings.
2. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
3. Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105.
4. 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.
5. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
6. Ji, H., Suo, L., & Chen, H. (2024). AI performance assessment in blended learning: Mechanisms and effects on students' continuous learning motivation. Frontiers in Psychology.
7. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
8. Kobicheva, A. (2024). Students' intention to learn and academic performance in the blended learning environment: The role of artificial intelligence chatbots. International Journal of Information and Education Technology, 14(1).
9. Moskal, P., Dziuban, C., & Hartman, J. (2013). Blended learning: A dangerous idea? The Internet and Higher Education, 18, 15–23.
10. Nazaretsky, T., Bar, C., Walter, M., & Alexandron, G. (2021). Empowering teachers with AI: Co-designing a learning analytics tool for personalized instruction in the science classroom. International Conference on Learning Analytics and Knowledge.
11. Obari, H., & Lambacher, S. (2019). Improving the English skills of native Japanese using artificial intelligence in a blended learning program. Proceedings of the CALL Conference.
12. Sanchez-Ruiz, L., Moll-Lopez, S., Nunez-Perez, A., Morano-Fernandez, J., & Vega-Fleitas, E. (2023). ChatGPT challenges blended learning methodologies in engineering education: A case study in mathematics. Applied Sciences, 13(10), 6039.
13. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
14. Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252–254.
15. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.
16. Warschauer, M., & Grimes, D. (2008). Automated writing assessment in the classroom. Pedagogies: An International Journal, 3(1), 22–36.
17. Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE.
18. Wu, Z., Abdul Halim, H., & Mohd Saad, M. R. (2024). Artificial intelligence (AI) and gamification in blended learning: Enhancing language and literacy in Shanxi, China. Malaysian Journal of Social Sciences and Humanities, 9(1).
19. Yun, K., & Maeng, U. (2021). The effect of PBL based blended instruction utilizing AI platforms on L2 learning. Modern English Education, 22(1), 14–26.
20. Zawacki-Richter, O., Marin, 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), 39.