Personalized Learning, Teacher Roles, and Chatbot Practices

This week, I would like to share three AI SoTL articles that were just published this week in the Turkish Journal of Distance Education (TJODE). I have been a reviewer for this journal for many years and have seen it progress to a Q2 journal. When read together, these articles illuminate how AI both draws upon and challenges foundational learning theories, particularly those that address how humans process, store, and apply information.

The first article is entitled, “The role of AI in enhancing personalised learning, automated assessment, intelligent tutoring, and student engagement” by Nhan (2025). The author examined AI’s contribution to personalized learning, automated assessment, intelligent tutoring, and student engagement. The study found that AI-driven systems tailor instructional content to individual needs, deliver timely feedback, and enhance motivation through interactive and gamified environments. These findings align closely with Cognitive Load Theory, which emphasizes optimizing learners’ limited working memory by segmenting and adapting content. By providing personalized scaffolds and reducing extraneous load, AI can help learners focus on core concepts. Equally, constructivist and connectivist theories appear in play as students engage more deeply when interacting with adaptive systems and networked platforms that extend opportunities for collaboration and meaning-making.

The second article entitled, “AI-assisted learning: A systematic review” is by Purba et al. (2025). The authors synthesis identified AI applications serving as teaching agents, peer agents, virtual students, and teaching assistants. Interestingly, while teachers are often positioned as facilitators, assessors, and instructors, the review suggests that additional roles such as designers of AI-embedded learning ecosystems or curators of authentic assessments remain underexplored. This shift speaks to social constructivism and Vygotsky’s Zone of Proximal Development, where human teachers scaffold learning by strategically integrating AI tools. Furthermore, the review underscores the information-processing model. AI supports encoding and retrieval by providing immediate, context-sensitive feedback, while teachers maintain the critical function of guiding learners’ metacognition and reflection.

The final article is entitled, “Instructional chatbot practise in distance education and investigation of its role in the learning process”, by Cevher and Yildirim (2025). The authors explored the use of an instructional chatbot (ARUChatbot). Their mixed-methods study revealed significant gains in academic achievement, particularly during high-demand periods such as exam weeks. Students valued the chatbot’s immediacy and efficiency for information retrieval, assignment completion, and content reinforcement. These findings resonate with self-regulated learning theory, as the chatbot supports planning, monitoring, and evaluating learning behaviors. Additionally, the chatbot’s concise delivery of information reflects dual coding theory—students may benefit from enhancements such as multimodal (text, audio, visual) interfaces to strengthen encoding across cognitive channels.

Together, these studies illustrate how AI tools amplify core learning processes identified in educational psychology. Personalized systems reduce cognitive load and enhance adaptive learning; AI agents reconfigure teacher and peer roles within constructivist and social learning frameworks and chatbots provide real-time scaffolding that supports self-regulation and retrieval practice. Yet, these same studies remind us of important limitations. AI lacks human-like empathy and contextual nuance, suggesting that teachers’ roles will become more not less important as they design meaningful integrations of AI into the curriculum. Moreover, while AI excels at streamlining feedback and engagement, it risks narrowing the learning experience if over-relied upon for surface-level processing.

References

Cevher, A. Y., & Yildirim, S. (2025). Instructional chatbot practise in distance education and investigation of its role in the learning process. Turkish Online Journal of Distance Education, 26(4), Article 10. https://dergipark.org.tr/en/download/article-file/4212735

Nhan, L. K. (2025). The role of AI in enhancing personalised learning, automated assessment, intelligent tutoring, and student engagement. Turkish Online Journal of Distance Education, 26(4), Article 4. https://dergipark.org.tr/en/download/article-file/4488257

Purba, S. W. D., Silitonga, B. N., & Yang, J. J. (2025). AI-assisted learning: A systematic review. Turkish Online Journal of Distance Education, 26(4), Article 5. https://dergipark.org.tr/en/download/article-file/4394067

Bandura, A. (1977). Social learning theory. Prentice Hall.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4 

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). Harvard University Press.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2