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A Survey Paper on AI - Powered Intelligent Tutoring Systems

Likitha Venugopal, Harleen Kaur

Abstract


Artificially Intelligent systems have greatly modified digital learning and given rise to Intelligent Tutoring Systems (ITSs) which have the ability to provide personalized feedback at a large scale. Today's ITSs combine methods of adaptive learning, natural language processing, large language models (LLMs), and multimodal content delivery to cater to the diverse needs of learners. This survey reviews the fundamentals of ITS, shift from data-driven and machine-learning adaptivity, the coming of age of conversational AI tutors, and contemporary advancements in multimodal instructional technologies. Major research trends, system taxonomies, assessment methodologies and unresolved challenges relevant for AI-enabled tutoring are highlighted. The survey aims to give an overview of AI in transforming tutoring systems collectively while outlining opportunities for fresh research.


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References


Woolf, B. P. (2009). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.

Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278.

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, 470–497.

ALEKS Corporation. (n.d.). ALEKS: A web-based intelligent tutoring system. ALEKS Corporation.

Piech, C., et al. (2015). Deep knowledge tracing. In Proceedings of the Advances in Neural Information Processing Systems (NIPS) (pp. 505–513).

Cho, Y., et al. (2020). EdNet: A large-scale hierarchical dataset in education. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK ’20).

Graesser, A., McNamara, S., & VanLehn, K. (2015). AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 25(1), 1–20.

Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education, 13, 159–172.

Park, H.-W., et al. (2022). A survey of multimodal intelligent tutoring systems. IEEE Transactions on Affective Computing. Advance online publication.

Wang, T., Liu, J., & Chen, H. (2023). Large language models in education: Opportunities and challenges. IEEE Transactions on Learning Technologies. Advance online publication.

Koedinger, K., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with cognitive tutors. Educational Psychology Review, 19, 239–264.

Balaji, S. (2016). Uplink channel performance and implementation of software for image communication in 4G network. In R. Silhavy, R. Senkerik, Z. Oplatkova, P. Silhavy, & Z. Prokopova (Eds.), Software engineering perspectives and application in intelligent systems (Advances in Intelligent Systems and Computing, Vol. 465). Springer. https://doi.org/10.1007/978-3-319-33622-0_10

Balaji, S. (2015). Performance analysis of MIMO-based transmission techniques for image quality in 4G wireless network. In Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1–5). IEEE. https://doi.org/10.1109/ICCIC.2015.7435774

Pal, S. R. (2024). Evaluation on mitigating cyber attacks and securing sensitive information with the adaptive secure metaverse guard (ASMG) algorithm using decentralized security. Journal of Computational Analysis and Applications, 33(2), 656–667.*

Abhishek, D. Y., Chandrashekar, V. D., & Jayasurya, K. (2024). LipNET: End-to-end level of visual and video lip reading using CNN network. Grenze International Journal of Engineering & Technology, 10(2, Part 5), 6326–6333.*

Rezni, S. (2024). Challenges and innovations in routing for flying ad hoc networks: A survey of current protocols. Journal of Computational Analysis and Applications, 33(2), 64–74.*

A., C. F., et al. (2024). On-road drivers and people with visual impairments: Real-time machine learning-based speed breaker and pothole detection. Grenze International Journal of Engineering & Technology, 10(2, Part 5), 5922–5930.*

M., K., & R., C. (2025). Real-time detection of unusual activities using CNN and deep learning. In Proceedings of the 2025 Third International Conference on Networks, Multimedia and Information Technology (NMITCON) (pp. 1–6). IEEE. https://doi.org/10.1109/NMITCON65824.2025.11188149

Suman, Y. R., Varun, M., Sathiya Prakash, G., & Nithin, H. (2025). Enhancing crop disease prediction: A hybrid approach integrating deep learning, ensemble methods, and explainable AI. In Proceedings of the 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICKECS65700.2025.11035873

Bhagappa, S. G. K. (2021, November). The smart sailing robot for navigational investigation is used to explore all the details on the zone of the water pura. Indian Journal of Signal Processing, 1(4).*

Dechamma, M. P. D., Ananya, M., Chandrika, M., Anusha, N. P., Deepak, N. R., & Shruti, B. (2025). FlowCast: A simple method for traffic prediction. In Engineering science and technology: Innovations for the future (1st ed., p. 9). CRC Press.


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