The Effectiveness of Artificial Intelligence-Assisted Learning Stations for Differentiated Learning Based on Students' Learning Styles
DOI:
https://doi.org/10.37329/cetta.v9i1.4824Keywords:
artificial intelligence, differentiated learning, learning stations, learning stylesAbstract
The integration of learning stations in higher education continues to face challenges in effectively addressing diverse student learning styles. Although differentiated instruction offers substantial promise, its implementation is often hindered by limited resources and the complexity of delivering personalized learning experiences at scale. This study explores the effectiveness of artificial intelligence (AI)-enhanced learning stations in supporting differentiated instruction aligned with individual learning preferences. Using a mixed-methods explanatory design, the research involved 82 students from an Educational Technology Study Program, divided into an experimental group (utilizing AI-supported learning stations) and a control group (traditional stations without AI). Data collection methods included pre- and post-tests, structured observations, VARK learning style inventories, and semi-structured interviews. Quantitative results indicated statistically significant improvements in learning outcomes for the experimental group, reflected in higher post-test scores and greater normalized gains. T-test and ANOVA analyses confirmed the intervention’s overall effectiveness, with no significant variation in learning gains across learning style categories within the experimental group. Qualitative findings supported these outcomes, with participants reporting that the AI-assisted environment fostered more personalized, relevant, and reflective learning experiences. Moreover, the integration of AI was associated with increased learner engagement, heightened motivation, and improved metacognitive awareness of learning preferences. This study contributes empirical evidence supporting the role of AI in enabling differentiated instruction within higher education contexts, highlighting its potential to provide scalable, personalized learning experiences. The findings suggest that AI-driven solutions may address key limitations in traditional instructional design by offering inclusive and adaptive strategies responsive to individual learner needs.
References
Alam, A., & Mohanty, A. (2023). Educational Technology: Exploring The Convergence Of Technology And Pedagogy Through Mobility, Interactivity, AI, and Learning Tools. Cogent Engineering, 10(2), 1-37.
Aldino, A. A., Tsai, Y. S., Gupte, S., Henderson, M., Nath, D., Gašević, D., & Chen, G. (2025). Analytics of Learner-Centered Feedback: A Large-Scale Case Study in Higher Education. Computers and Education, 237(2024), 105360.
Allen, M., Webb, A. W., & Matthews, C. E. (2016). Adaptive Teaching in STEM: Characteristics for Effectiveness. Theory into Practice, 55(3), 217-224.
Aydogmus, M., & Senturk, C. (2019). The Effects Of Learning Stations Technique On Academic Achievement: A Meta-Analytic Study. Research in Pedagogy, 9(1), 1-15.
Benkhalfallah, F., Laouar, M. R., & Benkhalfallah, M. S. (2024). Empowering Education: Harnessing Artificial Intelligence for Adaptive E-Learning Excellence. Cham: Springer Nature Switzerland.
Bernard, J., Chang, T. W., Popescu, E., & Graf, S. (2017). Learning Style Identifier: Improving The Precision Of Learning Style Identification Through Computational Intelligence Algorithms. Expert Systems with Applications, 75, 94.
Bhardwaj, V., Zhang, S., Tan, Y. Q., & Pandey, V. (2025). Redefining Learning: Student-Centered Strategies For Academic And Personal Growth. Frontiers in Education, 10(2), 1-15.
Boudjemaa, Y., & Belkacem, K. (2024). Enhancing Knowledge Acquisition and Retention through Adaptive Learning by Reading Systems with Personalized Content Delivery. Studies in Knowledge Discovery, Intelligent Systems, and Distributed Analytics, 14(11), 1-12.
Chien, C. W. (2017). Undergraduates’ Implementations Of Learning Stations As Their Service Learning Among Elementary School Students. Education 3-13, 45(2), 209-226.
Darrow, A. A. (2015). Differentiated Instruction for Students With Disabilities. General Music Today, 28(2), 29-32.
Darwesh, D., & Fayed, S. (2024). Combining Virtual Learning Stations with Gamification and its Impact on Developing Positive Thinking in History Among Secondary School Students. International Journal of Instructional Technology and Educational Studies (IJITES), 5(4), 1-13.
Dominguez, L. G. I., Robles-Gomez, A., & Pastor-Vargas, R. (2025). A data-Driven Approach To Engineering Instruction: Exploring Learning Styles, Study Habits, And Machine Learning. IEEE Access, 13(1), 10978-11002.
Eickholt, J., Johnson, M. R., & Seeling, P. (2021). Practical Active Learning Stations to Transform Existing Learning Environments into Flexible, Active Learning Classrooms. IEEE Transactions on Education, 64(2), 95-102.
Ellikkal, A., & Rajamohan, S. (2025). AI-enabled Personalized Learning: Empowering Management Students For Improving Engagement And Academic Performance. Vilakshan - XIMB Journal of Management, 22(1), 28-44.
Feldman, J., Monteserin, A., & Amandi, A. (2015). Automatic Detection Of Learning Styles: State Of The Art. Artificial Intelligence Review, 44(2), 157-186.
Gobiberia, I., & Kevkhishvili, M. (2021). Effectiveness of Differentiated Instruction in Higher Education. International Journal of Social Science and Human Research, 4(10), 2983-2984.
Goyibova, N., Muslimov, N., Sabirova, G., Kadirova, N., & Samatova, B. (2025). Differentiation Approach In Education: Tailoring Instruction For Diverse Learner Needs. MethodsX, 14(2024), 103163.
Hesham, M., Maha, F., Hassanein, A., & Raemp, I. C. (2020). Towards a Learning Style and Knowledge Level-Based Adaptive Personalized Platform for an Effective and Advanced Learning for School Students. Berlin: Springer.
HN, H. P., Syaiful, & Syamsurizal. (2024). Effect of Implementing Differentiated Learning Based on Learning Style on Abilities in Mathematical Problem-solving. Asian Journal of Education and Social Studies, 50(8), 181-190.
Holmes, W., & Tuomi, I. (2022). State of The Art And Practice In AI in Education. European Journal of Education, 57(4), 542-570.
Hu, P., & Zhang, J. (2017). A Pathway To Learner Autonomy: A Self-Determination Theory Perspective. Asia Pacific Education Review, 18(1), 147-157.
Humburg, M., Dragnić-Cindrić, D., Hmelo-Silver, C. E., Glazewski, K., Lester, J. C., & Danish, J. A. (2024). Integrating Youth Perspectives into the Design of AI-Supported Collaborative Learning Environments. Education Sciences, 14(11).
Hunter, B. (2015). Teaching For Engagement: Part 1: Constructivist Principles, Case-Based Teaching, And Active Learning. College Quarterly, 18(2).
Jääskä, E., & Aaltonen, K. (2022). Teachers’ Experiences Of Using Game-Based Learning Methods In Project Management Higher Education. Project Leadership and Society, 3(1), 100041.
Kellman, P. J., Jacoby, V., Massey, C., & Krasne, S. (2022). Perceptual Learning, Adaptive Learning, and Gamification: Educational Technologies for Pattern Recognition, Problem Solving, and Knowledge Retention in Medical Learning. Springer Cham.
Kolil, V. K., Prasanna, P. S., & Achuthan, K. (2025). The Role Of Integrating Augmented Reality And Cognitive Constructivism In Enhancing Sustainable Education. Education and Information Technologies 2025, 1-36.
Lin, Y. L., Wang, W. T., & Hsieh, M. J. (2024). The Effects Of Students’ Self-Efficacy, Self-Regulated Learning Strategy, Perceived And Actual Learning Effectiveness: A Digital Game-Based Learning System. Education and Information Technologies, 29(16), 22213-22245.
Liu, Z., Zhang, X., Liu, W., Chen, W., Li, Y., & Zhou, Y. (2025). Application And Optimization Of Digital Situated Teaching In University Finance Courses From A Constructivist Perspective: An Analysis Based On Machine Learning Algorithms. Education and Information Technologies, 30(13), 18059-18088.
Loughlin, C., Lygo-Baker, S., & Lindberg-Sand, Å. (2021). Reclaiming Constructive Alignment. European Journal of Higher Education, 11(2), 119-136.
Luckin, R., Cukurova, M., Kent, C., & Boulay, B. D. (2022). Empowering Educators To Be AI-ready. Computers and Education: Artificial Intelligence, 3, 1-11.
Majidah, Rullyana, G., & Triandari, R. (2025). Google Gemini as a Learning Assistant: Exploring Student Perceptions. Jurnal PAJAR (Pendidikan Dan Pengajaran), 9(2), 163-172.
Mayer, R. E. (2021). Evidence-Based Principles for How to Design Effective Instructional Videos. Journal of Applied Research in Memory and Cognition, 10(2), 229-240.
Merikko, J., & Kivimäki, V. (2022). “Replacing teachers? Doubt it.” Practitioners’ Views On Adaptive Learning Technologies’ Impact On The Teaching Profession. Frontiers in Education, 7(1010255), 1-11.
Nhan, L. K. (2025). The role of AI in Enhancing Personalised Learning, Automated Assessment, Intelligent Tutoring, And Student Engagement. Turkish Online Journal of Distance Education, 26(4), 55-76.
Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, And Relatedness In The Classroom:Applying Self-Determination Theory To Educational Practice. Theory and Research in Education, 7(2), 133-144.
Pashler, H., Mcdaniel, M., Rohrer, D., & Bjork, R. (2009). Concepts and Evidence. Psychological Science, 9(3), 105-119.
Pesovski, I., Santos, R., Henriques, R., & Trajkovik, V. (2024). Generative AI for Customizable Learning Experiences. Sustainability (Switzerland), 16(7), 1-23.
Pho, D. H., Nguyen, H. T., Nguyen, H. M., & Nguyen, T. T. N. (2021). The Use Of Learning Station Method According To Competency Development For Elementary Students In Vietnam. Cogent Education, 8(1), 1-27.
Rasheed, F., & Wahid, A. (2021). Learning Style Detection In E-Learning Systems Using Machine Learning Techniques. Expert Systems with Applications, 174, 1-24.
Ryan, R. M., & Deci, E. L. (2000). Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being. American Psychologist, 55(1), 68-78.
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2023). Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education. Information, 15(10), 1-23.
Soselisa, C. M., Rusijono, & Bachri, B. S. (2020). Station Rotation Method Based on Differentiated Instruction to Improve Higher Order Thinking Skills. 3rd International Conference on Education Innovation (ICEI 2019), 387(ICEI), 109.
Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI-driven Adaptive Learning For Sustainable Educational Transformation. Sustainable Development, 33(2), 1921-1947.
Sulistiani, A., Suyatna, A., & Rosidin, U. (2024). Differentiated Learning Assisted by Student Worksheets with STEM Content on Alternative Energy Materials to Improve Science Process Skills and Creative Problem Solving Sulistiani1. Jurnal Penelitian Pendidikan IPA, 10(1), 385-395.
Tomlinson, C. A. (2017). Differentiated Instruction. Fundamentals of Gifted Education. London: Routledge.
Tomlinson, C. A., & Jarvis, J. M. (2009). Differentiation: Making Curriculum Work For All Students Through Responsive Planning And Instruction. London: Routledge.
Trifatmasari, M., Oktoviana, L. T., & Puspitasari, E. D. (2023). Analysis of Student Learning Styles in Differentiation Learning. ICITEP International Conference on Innovation and Teacher Professionalism, 202, 46- 57.
Wu, S., Cao, Y., Cui, J., Li, R., Qian, H., Jiang, B., & Zhang, W. (2024). A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations. Journal of the ACM, 37(4), 1-82.
Xiangze, Z., & Abdullah, Z. (2023). Station Rotation with Gamification Approach to Increase Students’ Engagement in Learning English Online. Arab World English Journal, 9, 105-121.
Yan, V. X., & Fralick, C. M. (2022). Consequences of Endorsing the Individual Learning Styles Myth: Helpful, Harmful, or Harmless?. Learning Styles, Classroom Instruction, and Student Achievement, 59-74.
Yekollu, R. K., Ghuge, T. B., Biradar, S. S., Haldikar, S. V., & Kader, O. F. M. A. (2024). AI-Driven Personalized Learning Paths: Enhancing Education Through Adaptive Systems. ICSMDI 2024, 507-517.
Yusuf, H., Money, A., & Daylamani-Zad, D. (2025). Pedagogical AI Conversational Agents In Higher Education: A Conceptual Framework And Survey Of The State Of The Art. Educational Technology Research and Development, 73(2), 815-874.
Zajda, J. (2021). Constructivist Learning Theory and Creating Effective Learning Environments. Springer Cham.
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), 1-27.
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