Volume 13 : Issue 13.2 (2026)

An Empirical Study on Human Teacher and AI Agent Collaboration for Pedagogical Transformation

The idea of the Artificial Intelligence (AI) is changing the education sector, leaving the aspect of automation behind to open new teacher and AI collaborations. Previous research suggests that rather than overtaking the role of educators, AI and assistance of humans are complementary since they supplement human intelligence (Luckin et al., 2016; Holmes, Bialik & Fadel, 2019). However, empirical research there on how such partnerships are made to work in actual classrooms, especially in multicultural settings as in India, is scanty. This paper fills this research gap by examining the effects of teacher-AI collaboration with respects to pedagogical practice, student learning experience and implementation issues in higher education. A mixed-methods design informed by socio-constructivist (Vygotsky, 1978), distributed cognition (Salomon, 1993; Hutchins, 1995) paradigms was used. A sample of 209 student B.Ed. and 83 faculty members in the Sabarmati University, Ahmedabad was used (2024-25). The use of AI tools, including generative models (i.e., ChatGPT), adaptive platform-based tools (i.e., ALEKS, Knewton Alta) and automated feedback tools, were incorporated into instruction. Paired-sample t-tests and ANOVA were used to interpret quantitative data gathered in the pre- and post-intervention surveys and thematic interpretation of interview, focus group and observations in the classroom setting covered the qualitative data. As the results confirm, AI is related to the advancement of personalization and the workload (Chen & Chan, 2023; Anderson, 2021). To be more precise, 78% of educators said that they feel more able to create custom learning trajectories and 81% has expressed that they could be helped to settle on lower-level teaching as a result of the decreased administrative workload. Likewise, 84% stated that AI provided them with the which also corresponds to this concept of co-agency in pedagogy proposed by Holmes & Tuomi (2022). In the eyes of the students, some 79% deemed AI greatly responsible in advancing their conceptual clarity and 82% appreciated its instant feedback, which glorifies the prospect of AI as an eminent scaffolder of learning. Nevertheless, as Selwyn (2019) warned about over-automation, just 68% of students favoured AI-mediated classrooms, which indicates the persistence of the teacher sensitivity and the human factor. Recurrent challenges were also identified: 62% of educators had technical barriers and only 54% were adequately trained-it was the results that Zawacki-Richter et al. (2019) found the institutionalized ill-preparedness. Equity fears and ethical issues can be traced in the words of UNESCO (2021) which say that an AI integration must be responsible and inclusive. However, three-quarters of teachers (77%) viewed AI as a distant pedagogical partner, provided that they are adequately trained or safeguarded. The findings reinforce the idea that the introduction of AI to sustainability must be framed in such a way that it implies that AI should be adopted as a cognitive companion and not a substitute. Such cooperation promotes individualization, interactivity and reflective teaching and pedagogical authority is left to the teachers. The paper proposes AI literacy as a part of the curriculum of teacher education, investing in the continued professional learning process and establishing the policies concerning equity to make it an ethically and inclusively implemented process.

AI in Education, Teacher–AI Collaboration, Pedagogical Transformation, Human–AI Partnership, Adaptive Learning Systems, Educational Technology Integration, AI Classrooms

• Vygotsky, L. S. (1978). Mind In Society: The Development Of Higher Psychological Processes. Cambridge, MA: Harvard University Press. • Bruner, J. S. (1986). Actual Minds, Possible Worlds. Cambridge, MA: Harvard University Press. • Papert, S. (1993). Mindstorms: Children, Computers and Powerful Ideas. New York, NY: Basic Books. • Salomon, G. (1993). Distributed Cognitions: Psychological And Educational Considerations. Cambridge: Cambridge University Press. • Scardamalia, M., & Bereiter, C. (1994). Computer Support for Knowledge-Building Communities. Journal of The Learning Sciences, 3(3), 265–283. • Jonassen, D. H. (1996). Computers in the Classroom: Mindtools for Critical Thinking. Columbus, OH: Merrill/Prentice Hall. • Laurillard, D. (1997). Rethinking University Teaching: A Conversational Framework for the Effective Use Of Learning Technologies. London: Routledge. • Gee, J. P. (2003). What video games have to teach us about learning and literacy? New York, NY: Palgrave Macmillan. • Siemens, G. (2005). Connectivism: A Learning Theory for the Digital Age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10. • Selwyn, N. (2011). Education and Technology: Key Issues and Debates. London: Continuum. • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston, MA: Center For Curriculum Redesign. • Luckin, R., & Cukurova, M. (2019). Designing Educational Technologies in the Age of AI. London: Pearson. • Sun, Y., Xie, K., & Anderman, L. H. (2020). The Role of Teacher Presence in AI- Supported Learning. Computers & Education, 161, 104061. • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic Review of Research on Artificial Intelligence Applications in Higher Education. International Journal Of Educational Technology In Higher Education, 16(1), 1–27. • Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston, MA: Center For Curriculum Redesign. • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2022). Intelligence Unleashed: An Argument For AI In Education. London: Pearson. • Selwyn, N. (2022). Education and Technology: Key Issues and Debates. London: Bloomsbury Publishing. • Chen, W., & Chan, C. (2023). Intelligent Tutoring Systems and Learner Engagement in AI-Mediated Classrooms. Computers & Education, 194, 104674. • Kukulska-Hulme, A. (2020). Will Mobile Learning Change Language Learning? ReCALL, 32(2), 162–178. • Roll, I., & Wylie, R. (2016). Evolution and Revolution in Artificial Intelligence In Education. International Journal of Artificial Intelligence in Education, 26(2), 582– 599. • Zhou, M., & Brown, D. (2015). Educational Learning Theories: 2nd Edition. Columbus, OH: Ohio State University Press. • Anderson, T., & Dron, J. (2011). Three Generations of Distance Education Pedagogy. International Review of Research in Open And Distributed Learning, 12(3), 80–97. • OECD. (2021). AI and the Future of Skills: Education and Training for a Digital World. Paris: OECD Publishing. • UNESCO. (2021). AI and Education: Guidance for Policy-Makers. Paris: UNESCO Publishing. • Elias, T. (2011). Universal Instructional Design Principles for Mobile Learning. International Review of Research in Open and Distributed Learning, 12(2), 143–156. • Baker, R. S., & Inventado, P. S. (2014). Educational Data Mining and Learning Analytics. In J. A. Larusson & B. White (Eds.), Learning Analytics: From Research to Practice (pp. 61–75). New York, NY: Springer. • Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 30–40. • Bower, M. (2017). Design of Technology-Enhanced Learning: Integrating Research and Practice. Bingley, UK: Emerald Publishing. • Gibson, D., Broadley, T., & Downie, J. (2016). Blending Pedagogy and Digital Technology to Transform Education. (pp. 97–120). New York, NY: Springer. • Veletsianos, G. (2010). Emerging Technologies in Distance Education. Edmonton, AB: Athabasca University Press. • Huang, R., Spector, J. M., & Yang, J. (2019). Educational Technology: A Primer For The 21st Century. Singapore: Springer. • Weller, M. (2020). 25 Years of Ed Tech. Edmonton, AB: Athabasca University Press. • Kimmons, R. (2020). Current Trends in Educational Technology Research: 2020. TechTrends, 64, 803–808. • Zhao, Y. (2012). World Class Learners: Educating Creative and Entrepreneurial Students. Thousand Oaks, CA: Corwin. • OECD. (2023). Artificial Intelligence in Education: Promise and Challenges. Paris: OECD Publishing. • UNESCO. (2023). Global Education Monitoring Report 2023: Technology in Education. Paris: U