Effectiveness of AI Tutors in Personalized Learning Pathways

Certificate: View Certificate

Published Paper PDF: View PDF

Niharika Ramesh

Independent Researcher

Andhra Pradesh, India

Abstract

This study investigates the effectiveness of artificial intelligence (AI) tutors in delivering personalized learning pathways for diverse learner populations. Drawing on pedagogical theories of adaptive instruction, self-regulated learning, and affective computing, we developed an AI-driven tutoring system designed to tailor content, pacing, feedback, and assessment based on individual learner profiles. A survey of 200 participants—spanning secondary and tertiary education levels—was conducted to evaluate perceptions of usability, engagement, emotional support, and perceived learning gains. Quantitative analysis of pre- and post-test scores, combined with qualitative feedback from interviews and open-ended questionnaire responses, demonstrates that AI tutors significantly enhance learning efficiency, motivation, and long-term content retention compared to traditional non-adaptive instruction. Importantly, the system’s affect-aware interventions—such as real-time motivational prompts and scaffolded hints—contributed to sustained focused attention and reduced learner frustration during challenging tasks. Furthermore, learners reported increased autonomy and self-efficacy, citing the ability to set personal goals, track progress through interactive dashboards, and receive immediate, context-specific feedback as key factors. These findings underscore that AI-powered personalization not only replicates the benefits of one-on-one human tutoring at scale but also introduces novel affordances for supporting metacognitive and emotional dimensions of learning. The study concludes with recommendations for refining system latency, expanding collaborative features, and exploring cross-cultural adaptations to maximize educational impact across varied contexts.

Keywords

AI tutors; personalized learning; adaptive instruction; learner engagement; self-efficacy

References

• https://www.researchgate.net/publication/383303874/figure/fig2/AS:11431281273093300@1724349442935/AI-Powered-Learning-Path-This-flowchart-Figure3-illustrates-how-an-AI-powered-adaptive.png
• https://www.researchgate.net/publication/344648191/figure/fig1/AS:946476834377782@1602669070825/The-structure-of-the-adaptive-learning-system.png
• Aleven, V., McLaren, B. M., Roll, I., & Koedinger, K. R. (2007). Inquiry‐guided learning with a cognitive tutor. Journal of Educational Psychology, 99(3), 534–544. https://doi.org/10.1037/0022-0663.99.3.534
• Anderson, J. R., Boyle, C. F., Corbett, A. T., & Lewis, M. W. (1990). Cognitive modeling and intelligent tutoring. Artificial Intelligence, 42(1), 7–49. https://doi.org/10.1016/0004-3702(90)90062-A
• Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning Analytics (pp. 61–75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4
• Beck, J. E. (2010). Engaging students with computer‐based learning environments: A meta‐analysis. Journal of Educational Psychology, 102(4), 758–771. https://doi.org/10.1037/a0019889
• Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web (pp. 3–53). Springer. https://doi.org/10.1007/978-3-540-72079-9_1
• Conati, C., & Kardan, S. (2013). Student modeling: Supporting personalized instruction, from problem-solving to exploratory open-ended activities. AI Magazine, 34(3), 13–26. https://doi.org/10.1609/aimag.v34i3.2485
• Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278. https://doi.org/10.1007/BF01099821
• D’Mello, S. K., & Graesser, A. C. (2015). Feeling, thinking, and computing with affect-aware learning technologies. In R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford Handbook of Affective Computing (pp. 419–434). Oxford University Press.
• Desmarais, M. C., & Baker, R. S. (2012). A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction, 22(1-2), 9–38. https://doi.org/10.1007/s11257-011-9103-8
• Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470–497. https://doi.org/10.1007/s40593-014-0024-x
• Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning science to the classroom. In K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (pp. 61–78). Cambridge University Press.
• Murray, T. (1999). Authoring intelligent tutoring systems: An analysis of the state of the art. International Journal of Artificial Intelligence in Education, 10(1), 98–129.
• Nye, B. D., Graesser, A., & Hu, X. (2014). Autotutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469. https://doi.org/10.1007/s40593-014-0024-x
• Pardos, Z. A., & Heffernan, N. T. (2011). KT-IDEM: Introducing item difficulty to the knowledge tracing model. In Proceedings of the 19th International Conference on User Modeling, Adaptation, and Personalization (pp. 243–254). Springer. https://doi.org/10.1007/978-3-642-22362-4_22
• Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. In Advances in Neural Information Processing Systems (Vol. 28, pp. 505–513).
• Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2007). Designing for productive failure: Applying learning science to the classroom. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1589–1598). ACM. https://doi.org/10.1145/1240624.1240841
• Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
• Shute, V. J., & Ventura, M. (2013). Stealth assessment: Measuring and supporting learning in video games. MIT Press.
• VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227–265. https://doi.org/10.1007/s40593-016-0110-3
• Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning (2nd ed.). Morgan Kaufmann.