Artificial Intelligence in Personalized Learning: Enhancing Student Engagement through Adaptive Learning Systems
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Artificial Intelligence, Personalized Learning, Student Engagement
Abstract
Background. Advancements in artificial intelligence (AI) have transformed educational practices by enabling personalized learning experiences that adapt to individual student needs. Traditional instructional methods often fail to accommodate diverse learning paces, preferences, and competencies, leading to disengagement and suboptimal learning outcomes. AI-driven adaptive learning systems offer tailored content, real-time feedback, and data-driven recommendations to enhance student engagement and academic achievement.
Purpose. This study investigates the effectiveness of AI-based adaptive learning systems in promoting personalized learning and increasing student engagement across multiple educational contexts.
Method. A mixed-methods research design was employed, combining quantitative analysis of engagement metrics and academic performance with qualitative exploration through student interviews and teacher observations. Data were collected from 120 students using AI-enabled learning platforms over a 12-week intervention period.
Results. Results indicated significant improvements in engagement, motivation, and learning outcomes, with adaptive feedback and personalized content contributing to sustained participation and deeper comprehension. Students reported higher satisfaction and perceived control over their learning processes, while educators noted more efficient monitoring and instructional planning.
Conclusion. The study concludes that integrating AI into personalized learning systems can substantially enhance engagement and academic performance. Implications suggest that AI-driven adaptive platforms offer scalable, data-informed solutions to support individualized learning and optimize educational experiences.
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References
An, S., Kang, M., Kim, S., Chikontwe, P., Shen, L., & Park, S. H. (2026). Subject-adaptive meta-learning for personalized BCI: A fusion of resting-state EEG signal and task-specific information. Information Fusion, 125. Scopus. https://doi.org/10.1016/j.inffus.2025.103501
Balakrishnan, P., Leema, A. A., & Sangaiah, A. K. (2026). Ubiquitous Artificial Pancreas: Blockchain-Secured AI-Driven Digital Twin for IoT-Enabled Insulin Pumps in Type 1 Diabetes Management: Vol. 2380 CCIS (L. Hui, C.-H. Hsu, & S. Ruengittinun, Eds.; pp. 154–170). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-981-96-6291-3_13
Bui, P. H. D., Nguyen, L. Y. B., Ngo, L. D., & Nguyen, H. T. (2026). T-Test-Based Feature Selection on DNA Microarrays Gene Expression Data for Leukemia Classification: Vol. 15707 LNAI (H. Fujita, Y. Watanobe, M. Ali, & Y. Wang, Eds.; pp. 207–218). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-981-96-8892-0_18
Chandak, M. B., Raipurkar, A. R., & Rawat, S. G. (2026). Systemic Lupus Erythematosus prediction using Epistatic-Quantile Fusion Transformer network with integrated multi-omics and clinical data. Computational Biology and Chemistry, 120. Scopus. https://doi.org/10.1016/j.compbiolchem.2025.108617
Chen, H.-C., Chuang, J.-Y., Huang, Y.-M., Chen, K.-H., & Wang, Y.-C. (2026). The Impact of Generated AI-Supported Learning on Elementary Students’ Writing Skills: Vol. 15913 LNCS (W.-S. Wang, C.-F. Lai, Y.-M. Huang, F. E. Sandnes, & T. A. Sandtrø, Eds.; pp. 181–188). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-031-98185-2_20
Chen, S., Zhou, X., Wang, Y., Huang, Y., Chang, A., Ni, D., & Huang, R. (2026). Subtyping Breast Lesions via Generative Augmentation Based Long-Tailed Recognition in Ultrasound: Vol. 15967 LNCS (J. C. Gee, J. Hong, C. H. Sudre, P. Golland, D. C. Alexander, J. E. Iglesias, A. Venkataraman, & J. H. Kim, Eds.; pp. 519–529). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-032-04984-1_50
Cui, X., Zhang, J., Lan, Y., & Huang, S. (2026). Social-semantic enhanced dual-intent hypergraph collaborative filtering. Information Sciences, 725. Scopus. https://doi.org/10.1016/j.ins.2025.122714
Fang, W., Liu, Y., Huang, W., Guo, W., & Li, T. (2026). SCFGL: Soft clustering based federated graph learning on Non-IID graphs. Pattern Recognition, 172. Scopus. https://doi.org/10.1016/j.patcog.2025.112498
González-García, J. J., Pretel, E., López-Jaquero, V., Montero, F., & González, P. (2026). Smart-Pomodoro: A Tool to Gamify Children’s Study Sessions: Vol. 16108 LNCS (C. Ardito, S. Diniz Junqueira Barbosa, T. Conte, A. Freire, I. Gasparini, P. Palanque, & R. Prates, Eds.; pp. 255–274). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-032-04999-5_15
He, L., & Wei, Z. (2026). Towards structure-aware data augmentation for high-degree graph neural networks. Information Processing and Management, 63(1). Scopus. https://doi.org/10.1016/j.ipm.2025.104343
Huang, Y., Xu, T., Deng, W., Chen, S., & Liu, Q. (2026). Student Emotion Recognition Research Based on Deep Learning Techniques: An Example of Primary and Secondary School Classrooms: Vol. 2600 CCIS (Q. Liu, Y. Wei, J. Lei, & L. Zhang, Eds.; pp. 31–45). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-981-95-1525-7_3
Hung, L.-P., Chen, M.-H., Hsieh, J.-Y., & Chen, C.-L. (2026). Using Deep Learning Recognition to Aid Early Diagnosis and Rehabilitation of Senior Aphasia Care: Vol. 633 LNICST (D.-J. Deng & J.-C. Chen, Eds.; pp. 26–38). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-031-93825-2_4
Kim, S., Gao, Y., Purdie, T. G., & McIntosh, C. (2026). Treat: A Unified Text-Guided Conditioned Deep Learning Model for Generalized Radiotherapy Treatment Planning: Vol. 15964 LNCS (J. C. Gee, J. Hong, C. H. Sudre, P. Golland, D. C. Alexander, J. E. Iglesias, A. Venkataraman, & J. H. Kim, Eds.; pp. 614–624). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-032-04971-1_58
Li, Z., Wang, J., Gu, W., Yazdanpanah, V., Shi, L., Cristea, A. I., Kiden, S., & Stein, S. (2026). TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation: Vol. 16110 LNCS (C. Ardito, S. Diniz Junqueira Barbosa, T. Conte, A. Freire, I. Gasparini, P. Palanque, & R. Prates, Eds.; pp. 137–146). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-032-05005-2_8
Liu, Y., Fu, S., Ren, Q., Lv, X., & Li, J. (2026). SKGRec: Unifying temporal dynamics and knowledge graphs for robust recommendations. Expert Systems with Applications, 297. Scopus. https://doi.org/10.1016/j.eswa.2025.129354
Miao, T., Sha, L., Huang, K., Li, Y., & Liu, B. (2026). TFCA-TransNet: Convolutional time–frequency–spatial feature fusion with channel attention transformer network for EEG-MI signal decoding. Biomedical Signal Processing and Control, 112. Scopus. https://doi.org/10.1016/j.bspc.2025.108692
Mota, J., Romano, J., Grosso, A. R., Conde, J., Mendes, B., & Semedo, D. (2026). Unveiling MicroRNA Biomarkers for Breast Cancer Sub-typing Using Discriminative Models: Vol. 16121 LNAI (J. Valente de Oliveira, J. Rodrigues, J. Dias, P. Cardoso, & J. Leite, Eds.; pp. 68–80). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-032-05176-9_6
Novianti, N., Sumantri, M. S., & Wibowo, F. C. (2026). The impact of SCILARD augmented reality enhanced deep learning on science education in primary school: An empirical investigation. Multidisciplinary Science Journal, 8(3). Scopus. https://doi.org/10.31893/multiscience.2026155
Oh, K., Park, H., Lee, G. H., & Choi, J. K. (2026). Supervised contrastive learning-based stress detection for wearable sensor-based healthcare applications. Future Generation Computer Systems, 175. Scopus. https://doi.org/10.1016/j.future.2025.108058
Pan, M., Lai, C., & Guo, K. (2026). Self-regulation plus individual interests? A design-based study on the development of a GenAI-empowered platform for self-directed out-of-class reading. Computers and Education, 241. Scopus. https://doi.org/10.1016/j.compedu.2025.105474
Patias, I., Miteva, D., & Peltekova, E. (2026). Using Old Lessons for New AI – A Trainer for Project Risk Management: Vol. 16119 LNAI (G. De Tré, S. Sotirov, J. Kacprzyk, G. Psaila, G. Smits, T. Andreasen, G. Bordogna, & H. Legind Larsen, Eds.; pp. 155–167). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-032-05607-8_16
Qian, K., Liu, S., Li, T., Rakovi?, M., Li, X., Guan, R., Molenaar, I., Nawaz, S., Swiecki, Z., Yan, L., & Gaševi?, D. (2026). Towards reliable generative AI-driven scaffolding: Reducing hallucinations and enhancing quality in self-regulated learning support. Computers and Education, 240. Scopus. https://doi.org/10.1016/j.compedu.2025.105448
Singh, S., Gupta, H., Sinha, S., Kaushik, A. C., Kapoor, S., Awasthi, A. K., Qamar, I., & Sahi, S. (2026). Unveiling therapeutic potential: In Silico discovery of prognostic markers and potential inhibitors for TGFßR1 in pancreatic cancer. Computational Biology and Chemistry, 120. Scopus. https://doi.org/10.1016/j.compbiolchem.2025.108646
Šolín, P. (2026). Self-paced, instructor-assisted approach to teaching SQL. Journal of Computational and Applied Mathematics, 472. Scopus. https://doi.org/10.1016/j.cam.2025.116837
Umate, R., & Mohod, S. (2026). Unveiling the Nexus: A bibliometric review of mental stress detection in students. Multidisciplinary Reviews, 9(2). Scopus. https://doi.org/10.31893/multirev.2026061
Van Campenhout, R., Dittel, J. S., & Johnson, B. G. (2026). Scaling Effective Characteristics of ITSs: A Preliminary Analysis of LLM-Based Personalized Feedback: Vol. 15723 LNCS (S. Graf & A. Markos, Eds.; pp. 171–181). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-031-98281-1_13
Wu, J., Mei, X., Mao, R., He, K., & Cambria, E. (2026). TAKECare: A temporal-hierarchical framework with knowledge fusion for personalized clinical predictive modeling. Information Fusion, 126. Scopus. https://doi.org/10.1016/j.inffus.2025.103620
Xie, R., Liang, W., Chen, Y., He, D., Jin, K., Li, K., & Tsang, K. F. (2026). StarCPFL: Star-Centric Personalized Federated Learning with layer-wised clustering. Future Generation Computer Systems, 175. Scopus. https://doi.org/10.1016/j.future.2025.108037
Zhang, R., Fang, M., Liu, S., Wang, Z., Tian, J., & Dong, D. (2026). TMSE: Tri-Modal Survival Estimation with Context-Aware Tissue Prototype and Attention-Entropy Interaction: Vol. 15960 LNCS (J. C. Gee, J. Hong, C. H. Sudre, P. Golland, D. C. Alexander, J. E. Iglesias, A. Venkataraman, & J. H. Kim, Eds.; pp. 640–650). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-032-04927-8_61
Zhang, S., Zhang, L., Liu, S., Weng, J., Jian, W., & Guo, J. (2026). The similarities and differences of multiple chronic diseases risk factors across depressive symptoms trajectories among middle-aged and older Chinese adults: A 10-year longitudinal cohort study. Journal of Affective Disorders, 393. Scopus. https://doi.org/10.1016/j.jad.2025.120395
Zhao, J., Zhang, X., Zhang, J., Sun, H., Nie, Q., Xiao, Z., Xiao, L., Zhang, F., Hu, Y., & Liu, J. (2026). Token pyramid pooling-driven style adapter learning with dual-view balanced loss for imbalanced diabetic retinopathy grading. Pattern Recognition, 171. Scopus. https://doi.org/10.1016/j.patcog.2025.112194
Zhong, Y., Zheng, X., Shen, X., Wang, J., Zhao, L., Song, Z., & Zhang, Z. (2026). Thread the Needle: Genomics-Guided Prompt-Bridged Attention Model for Survival Prediction of Glioma Based on MRI Images: Vol. 15966 LNCS (J. C. Gee, J. Hong, C. H. Sudre, P. Golland, D. C. Alexander, J. E. Iglesias, A. Venkataraman, & J. H. Kim, Eds.; pp. 625–635). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-032-04981-0_59
Zhou, C., Wang, M., Shi, Y., Zhang, A., & Li, A. (2026). Understanding and tackling the modality imbalance problem in multimodal survival prediction. Pattern Recognition, 172. Scopus. https://doi.org/10.1016/j.patcog.2025.112398
