Adaptive AI Tutors Scale Personalized STEM Education Across Diverse Learner Populations

Zhenyu Luo; Haotian Ren; Matthew Carter

doi:10.54097/wrdscr15

Authors

Zhenyu Luo
Haotian Ren
Matthew Carter

DOI:

https://doi.org/10.54097/wrdscr15

Keywords:

Adaptive tutoring systems, Intelligent tutoring systems, Personalized STEM learning, Knowledge tracing, Learner diversity, Educational AI, Reinforcement learning

Abstract

The persistent challenge of delivering individualized instruction across heterogeneous learner populations in science, technology, engineering, and mathematics (STEM) contexts has driven significant interest in artificial intelligence (AI)-powered adaptive tutoring systems. This paper examines how adaptive AI tutors can be effectively scaled to meet the cognitive, motivational, and demographic diversity of students engaged in STEM learning. Drawing on a mixed-methods research design that integrates machine learning-based knowledge tracing, reinforcement learning (RL)-driven content sequencing, and learner profile modeling, this study evaluates an adaptive intelligent tutoring system (ITS) deployed across three secondary and tertiary STEM courses involving a combined cohort of 847 students. Quantitative findings indicate statistically significant improvements in learning gains, task completion rates, and knowledge retention among students who engaged with the adaptive system compared to those in conventional instructional settings. Qualitative data further reveals that learners from under-resourced backgrounds and high-achieving students both reported increased perceived relevance and self-regulatory engagement when the system dynamically adjusted content difficulty and scaffolding level. The study also identifies equity-related concerns regarding data sparsity for underrepresented learner groups and proposes mitigation strategies anchored in fairness-aware machine learning. These findings contribute to an emerging understanding of how scalable adaptive AI tutoring can bridge achievement gaps in STEM education without sacrificing instructional depth or pedagogical coherence.

Downloads

Download data is not yet available.

References

[1] Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators?. International journal of educational technology in higher education, 16(1), 39.

[2] Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001.

[3] Liu, J., Wang, Y., & Lin, H. (2025). Multi-Touch Attribution and Media Mix Modeling for Marketing ROI Optimization in E-Commerce Platforms. Frontiers in Business and Finance, 2(02), 378-398.

[4] Yang, Y., Wang, M., Wang, J., Li, P., & Zhou, M. (2025). Multi-agent deep reinforcement learning for integrated demand forecasting and inventory optimization in sensor-enabled retail supply chains. Sensors, 25(8), 2428.

[5] Zhang, X., Li, P., Han, X., Yang, Y., & Cui, Y. (2024). Enhancing time series product demand forecasting with hybrid attention-based deep learning models. IEEE Access, 12, 190079-190091.

[6] Li, P., Ren, S., Zhang, Q., Wang, X., & Liu, Y. (2024). Think4SCND: Reinforcement learning with thinking model for dynamic supply chain network design. IEEE Access, 12, 195974-195985.

[7] Yang, J., Li, P., Cui, Y., Han, X., & Zhou, M. (2025). Multi-sensor temporal fusion transformer for stock performance prediction: An adaptive Sharpe ratio approach. Sensors, 25(3), 976.

[8] Zhang, X., Sun, T., Han, X., Yang, Y., & Li, P. (2025). Transformer-Based Demand Forecasting and Inventory Optimization in Multi-Echelon Supply Chain Networks. Journal of Banking and Financial Dynamics, 9(12), 1-9.

[9] Liu, Y., Ren, S., Wang, X., & Zhou, M. (2024). Temporal logical attention network for log-based anomaly detection in distributed systems. Sensors, 24(24), 7949.

[10] Liu, Y., Hu, X., & Chen, S. (2024). Multi-material 3D printing and computational design in pharmaceutical tablet manufacturing. J. Comput. Sci. Artif. Intell, 1(1), 34-38.

[11] Liu, C. L., Tseng, C. J., Huang, T. H., Yang, J. S., & Huang, K. B. (2023). A multi-task learning model for building electrical load prediction. Energy and Buildings, 278, 112601.

[12] Liu, C. L., Chang, T. Y., Yang, J. S., & Huang, K. B. (2023). A deep learning sequence model based on self-attention and convolution for wind power prediction. Renewable Energy, 219, 119399.

[13] Xing, S., & Wang, Y. (2025). Proactive data placement in heterogeneous storage systems via predictive multi-objective reinforcement learning. IEEE Access.

[14] Xing, S., Wang, Y., & Liu, W. (2025). Self-adapting CPU scheduling for mixed database workloads via hierarchical deep reinforcement learning. Symmetry, 17(7), 1109.

[15] Sun, T., Yang, J., Li, J., Chen, J., Liu, M., Fan, L., & Wang, X. (2024). Enhancing auto insurance risk evaluation with transformer and SHAP. IEEE Access, 12, 116546-116557.

[16] Ma, Z., Chen, X., Sun, T., Wang, X., Wu, Y. C., & Zhou, M. (2024). Blockchain-based zero-trust supply chain security integrated with deep reinforcement learning for inventory optimization. Future Internet, 16(5), 163.

[17] Li, J., Fan, L., Wang, X., Sun, T., & Zhou, M. (2024). Product demand prediction with spatial graph neural networks. Applied Sciences, 14(16), 6989.

[18] Wei, Z., Sun, T., & Zhou, M. (2024). LIRL: Latent Imagination-Based Reinforcement Learning for Efficient Coverage Path Planning. Symmetry, 16(11), 1537.

[19] Wang, M. (2024). AI technologies in modern taxation: Applications, challenges, and strategic directions. International Journal of Finance and Investment, 1(1), 42-46.

[20] Liu, J., Wang, J., & Lin, H. (2025). Coordinated Physics-Informed Multi-Agent Reinforcement Learning for Risk-Aware Supply Chain Optimization. IEEE Access, 13, 190980-190993.

[21] Wang, J., Liu, J., Zheng, W., & Ge, Y. (2025). Temporal heterogeneous graph contrastive learning for fraud detection in credit card transactions. IEEE Access.

[22] Ge, Y., Wang, Y., Liu, J., & Wang, J. (2025). GAN-enhanced implied volatility surface reconstruction for option pricing error mitigation. IEEE Access.

[23] Chen, Z., Liu, J., & Chen, J. (2025). Machine Learning Methods for Financial Forecasting in Enterprise Planning: Transitioning from Rule-Based Models to Predictive Analytics. Frontiers in Artificial Intelligence Research, 2(3), 541-564.

[24] Liu, J., Wang, J., Chen, H., Guinness, J., Martin, R., & Kulkarni, C. S. (2019). Optimal Level Crossing Predictions for Electronic Prognostics. In AIAA Scitech 2019 Forum (p. 1962).

[25] Zakharova, O. V., & Lobuteva, A. V. (2025). A scoping review of contemporary frameworks, challenges, and future directions on educational technology for digital generations. Contemporary Educational Technology, 17(4), ep611.

[26] Sinha, T., & Kapur, M. (2021). When problem solving followed by instruction works: Evidence for produ