Research on Personalized Art Learning Path Recommendation Algorithm Based on Reinforcement Learning

Sinuo Sun1 , Yansong Li1*
1 Department of Art Studies, Harbin Conservatory of Music, Harbin, Heilongjiang, China
* Corresponding author: Yansong Li. Email: liyansong813@163.com
Journal of Discovery Core 2026, Vol. 1, No. 1, pp. 55-76
DOI: 10.67541/jdc2603
Received: 29 May 2026; Revised: 27 June 2026; Accepted: 11 July 2026; Published: 16 July 2026
Abstract

In online art education, the learning path has strong non-linear dependence and significant individual differences. Traditional sequential recommendation methods are inefficient because they struggle to dynamically model the skill topological constraints. This paper proposes a personalized art learning path recommendation algorithm named Art-DQN, which integrates graph attention networks and improved deep Q networks. Firstly, an art skill directed topological graph is constructed, and the graph attention network is used to extract high-order neighborhood features of the learner's skill state; Secondly, a dual-channel feature fusion module is designed, combining the skill graph features with the behavior sequence features encoded by LSTM and inputting them into the Q network; Finally, an ε-greedy exploration strategy based on skill topological constraints and a prioritized experience replay mechanism are proposed. Experiments on a dataset containing 500 real learners' logs and 1000 simulation trajectories show that the average skill mastery time of Art-DQN is 32.4 steps, which is 22.3% lower than that of the standard DQN, the target skill achievement rate reaches 92%, the number of prerequisite violations is 0, the path redundancy is as low as 0.09, and the convergence speed is increased by 41%. Ablation experiments verify the independent contributions of the dual-channel fusion and dynamic reward weights, and parameter sensitivity analysis and robustness tests further prove the stability of the algorithm. This algorithm provides an effective solution that combines structural constraints and personalized adaptation for intelligent recommendation in art education.

Keywords
Reinforcement learning Personalized learning path recommendation Graph attention network Art education Deep Q network
References
  1. Savage, T. (2024). Teaching to the line: how do visual arts technicians in higher education conceive of their pedagogies? (Doctoral dissertation, University for the Creative Arts). DOI: 10.13140/RG.2.2.18860.99203
  2. Shao, J. (2024). Personalized Learning for Art Major Students Based on Learner Characteristics (Doctoral dissertation, Chapman University). DOI: 10.36837/chapman.000560
  3. Ткач, М., Олексюк, О. М., Бобик, Л., Мимрик, М., & Вей, Л. (2024). Non-linear thinking strategies in post-non-classical higher art education: A synergistic concept. Scientific Herald of Uzhhorod University, 55, 1994-2005. DOI: 10.54919/physics/55.2024.199kv4
  4. Li, Y., & Shi, J. (2025). Multimodal deep learning for art behavior analysis and personalized teaching path generation. Discover Artificial Intelligence, 5(1), 215. DOI: 10.1007/s44163-025-00480-w
  5. Shokrzadeh, Z., Feizi-Derakhshi, M. R., Balafar, M. A., & Mohasefi, J. B. (2024). Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding. Ain Shams Engineering Journal, 15(1), 102263. DOI: 10.1016/j.asej.2023.102263
  6. Troussas, C., & Krouska, A. (2022). Path-based recommender system for learning activities using knowledge graphs. Information, 14(1), 9. DOI: 10.3390/info14010009
  7. Qian, D., & Luo, W. (2026). Revolutionizing arts education through 3D virtual reality: a mixed-method analysis of its impact on sculpting and carving skills among undergraduate art students. Educational Technology Research and Development, 1-36. DOI: 10.1007/s11423-025-10584-w
  8. Du, W., Zhao, Y., Wang, Y., Wang, H., & Yang, M. (2022). Novel machine learning approach for shape-finding design of tree-like structures. Computers & Structures, 261, 106731. DOI: 10.1016/j.compstruc.2021.106731
  9. Zhao, L. T., Wang, D. S., Liang, F. Y., & Chen, J. (2023). A recommendation system for effective learning strategies: An integrated approach using context-dependent DEA. Expert Systems with Applications, 211, 118535. DOI: 10.1016/j.eswa.2022.118535
  10. Garg, S., & Roy, D. (2022). A birds eye view on knowledge graph embeddings, software libraries, applications and challenges. arXiv preprint arXiv:2205.09088. DOI: 10.48550/arXiv.2205.09088
  11. Wani, A. A. (2025). Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions. PeerJ Computer Science, 11, e3025. DOI: 10.7717/peerj-cs.3025
  12. Wang, Z., Li, S., Liu, Q., Pan, Z., & Sun, X. (2026). MKAN-Refine: Fine-Grained Crisis Information Mining via Reliability-Aware Nonlinear Refinement and Kolmogorov–Arnold Networks. IEEE Access. DOI: 10.1109/access.2026.3688494
  13. Li, P., & Ding, Z. (2025). Application of deep learning-based personalized learning path prediction and resource recommendation for inheriting scientist spirit in graduate education. Computer Science and Information Systems, (00), 43-43. DOI: 10.2298/csis241125043l
  14. Angelaki, S., Triantafyllidis, G. A., & Besenecker, U. (2022). Lighting in kindergartens: Towards innovative design concepts for lighting design in kindergartens based on children's perception of space. Sustainability, 14(4), 2302. DOI: 10.3390/su14042302
  15. Newbury, R., Gu, M., Chumbley, L., Mousavian, A., Eppner, C., Leitner, J., ... & Cosgun, A. (2023). Deep learning approaches to grasp synthesis: A review. IEEE Transactions on Robotics, 39(5), 3994-4015. DOI: 10.48550/arXiv.2207.02556
  16. Bai, Y., Liu, Z., Guo, T., Hou, M., & Xiao, K. (2025). Prerequisite relation learning: a survey and outlook. ACM Computing Surveys, 57(11), 1-28. DOI: 10.1145/3733593
  17. Wang, Z., Yan, W., Zeng, C., Tian, Y., & Dong, S. (2023). A unified interpretable intelligent learning diagnosis framework for learning performance prediction in intelligent tutoring systems. International Journal of Intelligent Systems, 2023(1), 4468025. DOI: 10.1155/2023/4468025
  18. Winget, M., & Persky, A. M. (2022). A practical review of mastery learning. American Journal of Pharmaceutical Education, 86(10), ajpe8906. DOI: 10.5688/ajpe8906
  19. Yu, S., Zeng, Y., Yang, F., & Pan, Y. (2024). Causal-driven skill prerequisite structure discovery. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 18, pp. 20604-20612). DOI: 10.1609/aaai.v38i18.30046
  20. Vrahatis, A. G., Lazaros, K., & Kotsiantis, S. (2024). Graph attention networks: a comprehensive review of methods and applications. Future Internet, 16(9), 318. DOI: 10.3390/fi16090318
  21. Yu, X., Yang, S., Wang, Z., Song, S., Ma, H., Cao, Z., & Zhang, X. (2025). LIGHT: Enhancing Learning Path Recommendation via Knowledge Topology-Aware Sequence Optimization. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 306-315). DOI: 10.1145/3726302.3730022
  22. Amin, S., Uddin, M. I., Alarood, A. A., Mashwani, W. K., Alzahrani, A., & Alzahrani, A. O. (2023). Smart E-learning framework for personalized adaptive learning and sequential path recommendations using reinforcement learning. IEEE Access, 11, 89769-89790. DOI: 10.1109/access.2023.3305584
  23. Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., & Borodulin, A. (2024). Applications of long short-term memory (LSTM) networks in polymeric sciences: A review. Polymers, 16(18), 2607. DOI: 10.3390/polym16182607
  24. Xu, W., He, J., Li, W., He, Y., Wan, H., Qin, W., & Chen, Z. (2023). Long-short-term-memory-based deep stacked sequence-to-sequence autoencoder for health prediction of industrial workers in closed environments based on wearable devices. Sensors, 23(18), 7874. DOI: 10.3390/s23187874
  25. Jaramillo-Martínez, R., Chavero-Navarrete, E., & Ibarra-Pérez, T. (2024). Reinforcement-learning-based path planning: A reward function strategy. Applied Sciences, 14(17), 7654. DOI: 10.3390/app14177654
  26. Gallici, M., Fellows, M., Ellis, B., Pou, B., Masmitja, I., Foerster, J., & Martin, M. (2025). Simplifying deep temporal difference learning. In International Conference on Learning Representations (Vol. 2025, pp. 78148-78190). DOI: 10.48550/arXiv.2407.04811
  27. Hong, Z. W., Kumar, A., Karnik, S., Bhandwaldar, A., Srivastava, A., Pajarinen, J., ... & Agrawal, P. (2023). Beyond uniform sampling: Offline reinforcement learning with imbalanced datasets. Advances in Neural Information Processing Systems, 36, 4985-5009. DOI: 10.48550/arXiv.2310.04413
  28. Lockwood, O., & Si, M. (2022). A review of uncertainty for deep reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 18, No. 1, pp. 155-162). DOI: 10.1609/aiide.v18i1.21959
  29. Petravičius, T. (2023). Research and analysis of reinforcement learning methods in OpenAI Gym environment (Doctoral dissertation, Kauno technologijos universitetas).
  30. Yadav, R. K. (2025). Modeling Memory Retention with Ebbinghaus's Forgetting Curve and Interpretable Machine Learning on Behavioral Factors. Authorea Preprints. DOI: 10.36227/techrxiv.174495325.58680708/v1
  31. De Melo, C. M., Torralba, A., Guibas, L., DiCarlo, J., Chellappa, R., & Hodgins, J. (2022). Next-generation deep learning based on simulators and synthetic data. Trends in Cognitive Sciences, 26(2), 174-187. DOI: 10.1016/j.tics.2021.11.008
  32. Ghorbani, Z., Mirebeigi-Jamasbi, S. S., Hassannia Dargah, M., Nahvi, M., Hosseinikhah Manshadi, S. A., & Akbarzadeh Fathabadi, Z. (2025). A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs. BMC Oral Health, 25(1), 455. DOI: 10.1186/s12903-025-05803-y
  33. Zhang, Z. (2026). Dynamic Pricing Strategy Optimization Based on a Reinforcement Learning PPO Algorithm: An Empirical Study on Ride-Hailing Platforms. Journal of Organizational and End User Computing (JOEUC), 38(1), 1-43. DOI: 10.4018/JOEUC.406688

© 2026 The Author(s). Published by Digital Intelligence Press Limited. This is an open access article under the CC BY 4.0 license.

How to cite: Sun, S., & Li, Y. (2026). Research on Personalized Art Learning Path Recommendation Algorithm Based on Reinforcement Learning. Journal of Discovery Core, 1(1), 55-76. https://doi.org/10.67541/jdc2603