I Tutor: A Systematic Review of Artificially Intelligent Tutors for the Classroom
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Abstract
This systematic review aims to understand what research has been done on the use of AI as a classroom tutor and how that body of work should shape future research. A systematic review was conducted using key term searches in four major, peer-reviewed journal databases for relevant research and categorized them by research type, AI type, assistance type, and education level and analyzed them for study measures and key findings. After applying inclusion and exclusion criteria, a total of 35 research articles were analyzed for key findings. The review found that most studies have focused on chatbots supporting undergraduate level education. Key findings show that AI tutors may be most useful for students beginning at a lower level of expertise than their peers, and the use of techniques such as RAG may significantly enhance AI’s usefulness as a tutor with niche and higher-level subjects. This systematic review uniquely considers the last decade of research on AI use as a classroom tutor and provides insights into where future research on the subject may be most useful and impactful to the field of education.
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References
Abduljabbar, A., Gupta, N., Healy, L., Kumar, Y., Li, J. J., & Morreale, P. (2022). A self-served AI tutor for growth mindset teaching. In 2022 5th International Conference on Information and Computer Technologies (ICICT) (pp. 55–59). IEEE.
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247.
Baillifard, A., Gabella, M., Lavenex, P. B., & Martarelli, C. S. (2025). Effective learning with a personal AI tutor: A case study. Education and Information Technologies, 30(1), 297–312.
Banerjee, A., Lamrani, I., Hossain, S., Paudyal, P., & Gupta, S. K. S. (2020). AI enabled tutor for accessible training. In Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part I (Vol. 21, pp. 29–42). Springer International Publishing.
Belkacem, A. N., & Hireche, A. (2024). Exploring student engagement and learning preferences: A comparative study between virtual- and robot-based tutors. Measurement: Sensors, 101, 704.
Bharti, I., Chauhan, K., & Aggarwal, P. (2025). Generative AI: Next frontier for competitive advantage. In Enhancing Communication and Decision-Making With AI (pp. 1–36). IGI Global.
Bonde, L. (2024). A generative artificial intelligence-based tutor for personalized learning. In 2024 IEEE SmartBlock4Africa (pp. 1–10). IEEE.
Borchers, C., Wang, Y., Karumbaiah, S., Ashiq, M., Shaffer, D. W., & Aleven, V. (2024). Revealing networks: Understanding effective teacher practices in AI-supported classrooms using transmodal ordered network analysis. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 371–381).
Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute.
Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161–182.
Cheng, X., Zhang, X., Cohen, J., & Mou, J. (2022). Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Information Processing & Management, 59(3), 102940.
Cheng, Y., Fan, Y., Li, X., Chen, G., Gašević, D., & Swiecki, Z. (2025). Asking generative artificial intelligence the right questions improves writing performance. Computers and Education: Artificial Intelligence, 8, 100374.
Delios, A., Tung, R. L., & van Witteloostuijn, A. (2024). How to intelligently embrace generative AI: The first guardrails for the use of GenAI in IB research. Journal of International Business Studies, 1–10.
Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments, 10(1), 12.
Frank, L., Herth, F., Stuwe, P., Klaiber, M., Gerschner, F., & Theissler, A. (2024). Leveraging GenAI for an intelligent tutoring system for R: A quantitative evaluation of large language models. In 2024 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–9). IEEE.
Frankford, E., Sauerwein, C., Bassner, P., Krusche, S., & Breu, R. (2024). AI-tutoring in software engineering education. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training (pp. 309–319).
Gan, W., Sun, Y., Ye, S., Fan, Y., & Sun, Y. (2019). AI-tutor: Generating tailored remedial questions and answers based on cognitive diagnostic assessment. In 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC) (pp. 1–6). IEEE.
Gold, K., & Geng, S. (2024). On the helpfulness of a zero-shot Socratic tutor. In International Conference on Artificial Intelligence in Education Technology (pp. 154–170). Springer Nature Singapore.
Heaven, W. D. (2023). ChatGPT is everywhere. Here’s where it came from. MIT Technology Review, 10.
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Designing for complementarity: Teacher and student needs for orchestration support in AI-enhanced classrooms. In Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25–29, 2019, Proceedings, Part I (Vol. 20, pp. 157–171). Springer International Publishing.
Huo, H., Ding, X., Guo, Z., Shen, S., Ye, D., Pham, O., Milne, D., Mathieson, L., & Gardner, A. (2024). Accelerating learning with AI: Improving students’ capability to receive and build automated feedback for programming courses. In 2024 World Engineering Education Forum–Global Engineering Deans Council (WEEF-GEDC) (pp. 1–9). IEEE.
Hurt, J., Runyon, M., Hammond, T. A., & Linsey, J. S. (2020). A study on the impact of a statics sketch-based tutoring system through a truss design problem. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1–7). IEEE.
Jaramillo, J. J., & Chiappe, A. (2024). The AI-driven classroom: A review of 21st century curriculum trends. Prospects, 54(3), 645–660.
Karumbaiah, S., Borchers, C., Shou, T., Falhs, A.-C., Liu, P., Nagashima, T., Rummel, N., & Aleven, V. (2023). A spatiotemporal analysis of teacher practices in supporting student learning and engagement in an AI-enabled classroom. In International Conference on Artificial Intelligence in Education (pp. 450–462). Springer Nature Switzerland.
Khabarov, V., Volegzhanina, I., & Volegzhanina, E. (2023). Ontology-based AI mentor for training future “Digital Railway” engineers. In International Scientific Conference Fundamental and Applied Scientific Research in the Development of Agriculture in the Far East (pp. 31–42). Springer Nature Switzerland.
Kim, W.-H., & Kim, J.-H. (2020). Individualized AI tutor based on developmental learning networks. IEEE Access, 8, 27927–27937.
Kloos, C. D., Alario-Hoyos, C., Estévez-Ayres, I., Callejo Pinardo, P., Hombrados-Herrera, M. A., Muñoz-Merino, P. J., Moreno-Marcos, P. M., Muñoz-Organero, M., & Ibáñez, M. B. (2024). How can generative AI support education? In 2024 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–7). IEEE.
Lawal, A. (2024). STEM teacher shortage in American schools: A case study exploring the recruitment of STEM majors into teacher education programs (Doctoral dissertation). University of North Dakota.
Létourneau, A., Deslandes Martineau, M., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K–12 education. npj Science of Learning, 10(1), 29.
Llanos, R., Gonzales, G., & Morzan, J. (2024). Enhancing student success through AI integration: A study on the implementation of a virtual assistant in higher education courses. In 2024 IEEE 4th International Conference on Advanced Learning Technologies on Education & Research (ICALTER) (pp. 1–4). IEEE.
Lee, S. J., & Kwon, K. (2024). A systematic review of AI education in K-12 classrooms from 2018 to 2023: Topics, strategies, and learning outcomes. Computers and Education: Artificial Intelligence, 6, 100211.
Looi, C.-K., & Jia, F. (2025). Personalization capabilities of current technology chatbots in a learning environment: An analysis of student–tutor bot interactions. Education and Information Technologies, 1–31.
Lui, R. W. C., Bai, H., Zhang, A. W. Y., & Chu, E. T. H. (2024). GPTutor: A generative AI-powered intelligent tutoring system to support interactive learning with knowledge-grounded question answering. In 2024 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) (pp. 702–707). IEEE.
Madhuri, J. N., Fatma, G., Kumari, A., Pathak, P., & Mohamed Faizal, M. (2024). Creating an AI English tutor with personalized content and dynamic lessons. In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) (pp. 1–6). IEEE.
Mailach, A., Gorgosch, D., Siegmund, N., & Siegmund, J. (2025). “Ok Pal, we have to code that now”: Interaction patterns of programming beginners with a conversational chatbot. Empirical Software Engineering, 30(1), 34.
Maity, S., & Deroy, A. (2024). Generative AI and its impact on personalized intelligent tutoring systems. arXiv preprint, arXiv:2410.10650.
Makharia, R., Kim, Y. C., Jo, S. B., Kim, M. A., Jain, A., Agarwal, P., Srivastava, A., Agarwal, A. V., & Agarwal, P. (2024). AI tutor enhanced with prompt engineering and deep knowledge tracing. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) (Vol. 2, pp. 1–6). IEEE.
Maxwell, J. A. (2012). The importance of qualitative research for causal explanation in education. Qualitative Inquiry, 18(8), 655–661.
Mousavinasab, E., Zarifsanaiey, N., Niakan Kalhori, S. R., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2021). Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 29(1), 142–163.
Nwana, H. S. (1990). Intelligent tutoring systems: An overview. Artificial Intelligence Review, 4(4), 251–277.
Paudyal, P., Banerjee, A., & Gupta, S. (2020). On evaluating the effects of feedback for sign language learning using explainable AI. In Companion Proceedings of the 25th International Conference on Intelligent User Interfaces (pp. 83–84).
Peñalvo, F. J. G., & Vázquez Ingelmo, A. (2023). What do we mean by GenAI? A systematic mapping of the evolution, trends, and techniques involved in Generative AI. IJIMAI, 8(4), 7–16.
Peterson, R. A., & Merunka, D. R. (2014). Convenience samples of college students and research reproducibility. Journal of Business Research, 67(5), 1035–1041.
Roumeliotis, K. I., & Tselikas, N. D. (2023). ChatGPT and Open-AI models: A preliminary review. Future Internet, 15(6), 192.
Sarshartehrani, F., Mohammadrezaei, E., Behravan, M., & Gracanin, D. (2024). Enhancing e-learning experience through embodied AI tutors in immersive virtual environments: A multifaceted approach for personalized educational adaptation. In International Conference on Human-Computer Interaction (pp. 272–287). Springer Nature Switzerland.
Shahri, H., Emad, M., Ibrahim, N., Rais, R. N. B., & Al-Fayoumi, Y. (2024). Elevating education through AI tutor: Utilizing GPT-4 for personalized learning. In 2024 15th Annual Undergraduate Research Conference on Applied Computing (URC) (pp. 1–5). IEEE.
Soliman, H., Kravcik, M., Neumann, A. T., Yin, Y., Pengel, N., & Haag, M. (2024). Scalable mentoring support with a large language model chatbot. In European Conference on Technology Enhanced Learning (pp. 260–266). Springer Nature Switzerland.
Soudani, H., Kanoulas, E., & Hasibi, F. (2024). Fine tuning vs. retrieval augmented generation for less popular knowledge. In Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (pp. 12–22).
Vijay, V. C., Lees, M., & Vakaj, E. (2020). Introducing knowledge-based augmented reality environment in engineering learning: A comparative study. In 2020 IEEE Learning With MOOCS (LWMOOCS) (pp. 131–143). IEEE.
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.-L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122–1136.
Yang, J., Cooper-Stachowsky, M., & Kamal, Z. H. (2024). On implementing an effective intelligent tutor and its impact on teaching and learning experiences. In International Conference on Artificial Intelligence in Education Technology (pp. 171–190). Springer Nature Singapore.
Yang, K. B., Nagashima, T., Yao, J., Williams, J. J., Holstein, K., & Aleven, V. (2021). Can crowds customize instructional materials with minimal expert guidance? Exploring teacher-guided crowdsourcing for improving hints in an AI-based tutor. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–24.