Real Time Adaptive Teaching Digital Human System Based on Large Language Model

Wumeng Yang1
1 AI Science and Technology Department, Beijing SRT Education & Technology Co., Ltd., Beijing, China
International Scientific Technical and Economic Research 2026, Vol. 4, No. 2, pp. 166-185
DOI: 10.71451/ISTAER2620
Received: 27 January 2026; Revised: 1 March 2026; Accepted: 19 April 2026; Published: 26 April 2026
Abstract

With the diversification and personalization of educational needs, the traditional teaching model is facing many challenges, especially in meeting students' personalized learning needs and providing real-time feedback. This study proposes a real-time adaptive teaching digital human system based on a large language model, which aims to improve the quality of education and student engagement through intelligent technology. The system obtains students' learning status in real time through a variety of data acquisition devices (such as learning behavior tracking, question-answering records, and speech recognition) and uses the large language model to generate personalized teaching content and feedback. The system calculates a comprehensive learning status score by evaluating students' learning progress, answer accuracy, participation, and learning time, thereby dynamically adjusting the teaching content and learning path. Experimental results show that with the system, students' knowledge mastery rate increases by 15%, understanding depth by 17%, and learning interest by 20%. The system's response time is reduced from 5 seconds (in traditional systems) to 1.5 seconds, and its processing capacity is increased by 2.5 times, supporting more concurrent users. The successful implementation of the system provides a new solution for personalized education and has broad application prospects, especially in online education and distance learning.

Keywords
Large language model Real time adaptive Teaching digital people Individualized education System optimization
References
  1. Martin, A. D. (2022). Teacher identity perspectives. In Encyclopedia of Teacher Education (pp. 1856-1860). Singapore: Springer Nature Singapore. DOI: 10.1007/978-981-13-1179-6_222-1
  2. Tetzlaff, L., Schmiedek, F., & Brod, G. (2021). Developing personalized education: A dynamic framework. Educational Psychology Review, 33(3), 863-882. DOI: 10.1007/s10648-020-09570-w
  3. Dumont, H., & Ready, D. D. (2023). On the promise of personalized learning for educational equity. NPJ Science of Learning, 8(1), 26. DOI: 10.1038/s41539-023-00174-x
  4. Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. DOI: 10.35542/osf.io/5er8f
  5. Wang, S., Xu, T., Li, H., Zhang, C., Liang, J., Tang, J., ... & Wen, Q. (2026). Large language models for education: A survey and outlook. IEEE Signal Processing Magazine, 42(6), 51-63. DOI: 10.48550/arXiv.2403.18105
  6. Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., Bin Saleh, K., ... & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236-1242. DOI: 10.1016/j.sapharm.2023.05.016
  7. Oprea, S. V., & Bâra, A. (2025). Transforming Education with Large Language Models. Trends, Themes and Untapped Potential. IEEE Access. DOI: 10.1109/access.2025.3570649
  8. Chen, E., Heritage, M., & Lee, J. (2024). Identifying and monitoring students' learning needs with technology. In Transforming Data Into Knowledge (pp. 309-332). Routledge. DOI: 10.1207/s15327671espr1003_6
  9. Gm, D., Goudar, R. H., Kulkarni, A. A., Rathod, V. N., & Hukkeri, G. S. (2024). A digital recommendation system for personalized learning to enhance online education: A review. IEEE Access, 12, 34019-34041. DOI: 10.1109/access.2024.3369901
  10. Almufarreh, A., & Arshad, M. (2023). Promising emerging technologies for teaching and learning: Recent developments and future challenges. Sustainability, 15(8), 6917. DOI: 10.3390/su15086917
  11. Audras, D., Zhao, A., Isgar, C., & Tang, Y. (2022). Virtual teaching assistants: A survey of a novel teaching technology. International Journal of Chinese Education, 11(2), 2212585X221121674. DOI: 10.1177/2212585x221121674
  12. Zhou, Y., Xu, K., Yin, B., & Liu, N. (2024). Research on the application of digital humans in english oral teaching based on AI models. In Proceedings of the 2024 9th International Conference on Distance Education and Learning (pp. 49-56). DOI: 10.1145/3675812.3675820
  13. Aly, M. (2025). Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model. Multimedia Tools and Applications, 84(13), 12575-12614. DOI: 10.1007/s11042-024-19392-5
  14. Vistorte, A. O. R., Deroncele-Acosta, A., Ayala, J. L. M., Barrasa, A., López-Granero, C., & Martí-González, M. (2024). Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review. Frontiers in Psychology, 15, 1387089. DOI: 10.3389/fpsyg.2024.1387089
  15. Pabba, C., & Kumar, P. (2022). An intelligent system for monitoring students' engagement in large classroom teaching through facial expression recognition. Expert Systems, 39(1), e12839. DOI: 10.1111/exsy.12839
  16. Dahleez, K. A., El-Saleh, A. A., Al Alawi, A. M., & Abdelmuniem Abdelfattah, F. (2021). Higher education student engagement in times of pandemic: the role of e-learning system usability and teacher behavior. International Journal of Educational Management, 35(6), 1312-1329. DOI: 10.1108/ijem-04-2021-0120
  17. Kong, K., Isleem, H. F., Aluvalu, R., Tejani, G. G., & Metwally, A. S. M. (2025). Real-time cognitive and emotional state tracking in intelligent tutoring systems for enhanced learning outcomes. Journal of Big Data, 12(1), 266. DOI: 10.1186/s40537-025-01333-0
  18. Zhou, Z. (2026). Artificial Intelligence and Student Engagement in Online Learning: A Literature Review. American Journal of Distance Education, 40(1), 8-30. DOI: 10.1080/08923647.2025.2594282
  19. Song, Y., & Xiong, W. (2025). Large language model-driven 3D hyper-realistic interactive intelligent digital human system. Sensors, 25(6), 1855. DOI: 10.3390/s25061855
  20. Llanes-Jurado, J., Gómez-Zaragozá, L., Minissi, M. E., Alcañiz, M., & Marín-Morales, J. (2024). Developing conversational virtual humans for social emotion elicitation based on large language models. Expert Systems with Applications, 246, 123261. DOI: 10.1016/j.eswa.2024.123261
  21. Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., Bin Saleh, K., ... & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236-1242. DOI: 10.1016/j.sapharm.2023.05.016
  22. Song, C., Shin, S. Y., & Shin, K. S. (2024). Implementing the dynamic feedback-driven learning optimization framework: a machine learning approach to personalize educational pathways. Applied Sciences, 14(2), 916. DOI: 10.3390/app14020916
  23. Ruan, S., & Lu, K. (2025). Adaptive deep reinforcement learning for personalized learning pathways: A multimodal data-driven approach with real-time feedback optimization. Computers and Education: Artificial Intelligence, 100463. DOI: 10.1016/j.caeai.2025.100463
  24. Yu, M. (2024). Application of an Artificial Intelligence-based adaptive learning system to chinese language education in universities. In Proceedings of the 2024 International Symposium on Artificial Intelligence for Education (pp. 416-421). DOI: 10.1145/3700297.3700368
  25. Tian, X. (2024). Personalized translator training in the era of digital intelligence: Opportunities, challenges, and prospects. Heliyon, 10(20). DOI: 10.1016/j.heliyon.2024.e39354
  26. Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), 596. DOI: 10.3390/info15100596
  27. Raiaan, M. A. K., Mukta, M. S. H., Fatema, K., Fahad, N. M., Sakib, S., Mim, M. M. J., ... & Azam, S. (2024). A review on large language models: Architectures, applications, taxonomies, open issues and challenges. IEEE Access, 12, 26839-26874. DOI: 10.1109/access.2024.3365742
  28. Myers, D., Mohawesh, R., Chellaboina, V. I., Sathvik, A. L., Venkatesh, P., Ho, Y. H., ... & Jararweh, Y. (2024). Foundation and large language models: fundamentals, challenges, opportunities, and social impacts. Cluster Computing, 27(1), 1-26. DOI: 10.1007/s10586-023-04203-7
  29. Bharathi Mohan, G., Prasanna Kumar, R., Vishal Krishh, P., Keerthinathan, A., Lavanya, G., Meghana, M. K. U., ... & Doss, S. (2024). An analysis of large language models: their impact and potential applications. Knowledge and Information Systems, 66(9), 5047-5070. DOI: 10.1007/s10115-024-02120-8
  30. Annepaka, Y., & Pakray, P. (2025). Large language models: a survey of their development, capabilities, and applications. Knowledge and Information Systems, 67(3), 2967-3022. DOI: 10.1007/s10115-024-02310-4
  31. Grigoriev, V. V., Loginov, E. L., & Loginova, V. E. (2022). Agent-based human interaction with the digital educational environment, based on a set of digital intelligent agents. In Computer Applications for Management and Sustainable Development of Production and Industry (CMSD2021) (Vol. 12251, pp. 120-125). SPIE. DOI: 10.1117/12.2631456
  32. Tan, S. C., Chan, C., Bielaczyc, K., Ma, L., Scardamalia, M., & Bereiter, C. (2021). Knowledge building: Aligning education with needs for knowledge creation in the digital age. Educational Technology Research and Development, 69(4), 2243-2266. DOI: 10.1007/s11423-020-09914-x
  33. Yu, X., & Tian, Y. (2025). Enhancing Computer Education through IoT-Enabled Learning Environments Leveraging Mobile Edge Computing for Real-Time Feedback. Systems and Soft Computing, 200433. DOI: 10.1016/j.sasc.2025.200433
  34. Aly, M. (2025). Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model. Multimedia Tools and Applications, 84(13), 12575-12614. DOI: 10.1007/s11042-024-19392-5
  35. Li, X., Henriksson, A., Duneld, M., Nouri, J., & Wu, Y. (2023). Evaluating embeddings from pre-trained language models and knowledge graphs for educational content recommendation. Future Internet, 16(1), 12. DOI: 10.3390/fi16010012
  36. Yu, L. (2026). Research on the Construction and Reasoning of Educational Knowledge Graph Based on Pre-trained Language Model. Human-Centric Intelligent Systems, 1-18. DOI: 10.1007/s44230-026-00139-4
  37. Li, Y., Liang, Y., Yang, R., Qiu, J., Zhang, C., & Zhang, X. (2024). CourseKG: an educational knowledge graph based on course information for precision teaching. Applied Sciences, 14(7), 2710. DOI: 10.3390/app14072710