Utilizing Artificial Intelligence to Design Adaptive Learning Systems Aligned with the 2024 Curriculum
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Abstract
This research explores the application of Artificial Intelligence (AI) in designing adaptive learning systems aligned with the 2024 curriculum goals. The study investigates how AI technologies, including neural networks, decision trees, and Bayesian networks, can personalize learning paths to meet the diverse needs of students. By analyzing recent developments and implementing AI-driven adaptive platforms in real classroom settings, the research examines the impact of personalized learning on student engagement, academic performance, and inclusivity. Results show that AI significantly enhances the alignment between instructional content and individual learner profiles, supports timely teacher interventions through real-time analytics, and promotes more equitable learning opportunities. Compared to previous studies, this research extends the understanding of AI’s role by focusing on a broader set of competencies beyond subject-specific knowledge, such as critical thinking and digital literacy. The findings conclude that AI-powered adaptive learning, when integrated thoughtfully and supported by effective teacher training, holds substantial promise for transforming education into a more flexible, efficient, and student-centered process.
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Bailey, J. P., & Williams-Black, T. H. (2008). Differentiated instruction: Three teachers’ perspectives. College Reading Association Yearbook, 29, 133–151.
Bray, N. R. (2017). Faculty perceptions of implementing adaptive learning: A case study.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264–75278.
Ciaburro, G., Ayyadevara, V. K., & Perrier, A. (2018). Hands-on machine learning on google cloud platform: Implementing smart and efficient analytics using cloud ml engine. Packt Publishing Ltd.
Colchester, K., Hagras, H., Alghazzawi, D., & Aldabbagh, G. (2017). A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1), 47–64.
Ford, C., McNally, D., & Ford, K. (2017). Using design-based research in higher education innovation. Online Learning, 21(3), 50–67.
Gray, K., & Tobin, J. (2010). Introducing an online community into a clinical education setting: a pilot study of student and staff engagement and outcomes using blended learning. BMC Medical Education, 10, 1–9.
Gutiérrez, R. (2002). Enabling the practice of mathematics teachers in context: Toward a new equity research agenda. Mathematical Thinking and Learning, 4(2–3), 145–187.
Huang, S.-L., & Shiu, J.-H. (2012). A user-centric adaptive learning system for e-learning 2.0. Journal of Educational Technology & Society, 15(3), 214–225.
Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B. (2011). An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems, 51(1), 141–154.
Injadat, M. A. M. (2020). Optimized machine learning models towards intelligent systems. The University of Western Ontario (Canada).
Justice, L. M. (2006). Evidence-based practice, response to intervention, and the prevention of reading difficulties. Language, Speech, and Hearing Services in Schools, 37(4), 284–297.
Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2020). Enhancing Supply Chain Visibility through AI: Implementing Neural Networks and Reinforcement Learning Algorithms. International Journal of AI and ML, 1(2).
Khosravi, H., Sadiq, S., & Gasevic, D. (2020). Development and adoption of an adaptive learning system: Reflections and lessons learned. Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 58–64.
Kolluru, V., Mungara, S., & Chintakunta, A. N. (2018). Adaptive learning systems: Harnessing AI for customized educational experiences. International Journal of Computational Science and Information Technology, 6(3), 10–5121.
Marienko, M., Nosenko, Y., Sukhikh, A., Tataurov, V., & Shyshkina, M. (2020). Personalization of learning through adaptive technologies in the context of sustainable development of teachers education. ArXiv Preprint ArXiv:2006.05810.
Martin, S. M. (2019). Artificial intelligence, mixed reality, and the redefinition of the classroom. Rowman & Littlefield.
Modell, H., Michael, J., & Wenderoth, M. P. (2005). Helping the learner to learn: the role of uncovering misconceptions. The American Biology Teacher, 67(1), 20–26.
Mousavi, S. S., Schukat, M., & Howley, E. (2016). Deep reinforcement learning: an overview. Proceedings of SAI Intelligent Systems Conference, 426–440.
Nasir, N. S., de Royston, M. M., Barron, B., Bell, P., Pea, R., Stevens, R., & Goldman, S. (2020). Learning pathways: How learning is culturally organized. In Handbook of the cultural foundations of learning (pp. 195–211). Routledge.
Shanmuganathan, S. (2016). Artificial neural network modelling: An introduction. Springer.
Tileston, D. W. (2005). Ten best teaching practices: How brain research, learning styles, and standards define teaching competencies. Corwin Press.
Vaezi, M., & Rezaei, S. (2019). Development of a rubric for evaluating creative writing: a multi-phase research. New Writing, 16(3), 303–317.
Walkington, C. A. (2013). Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932.
Wang, B., Liu, H., An, P., Li, Q., Li, K., Chen, L., Zhang, Q., Zhang, J., Zhang, X., & Gu, S. (2018). Artificial intelligence and education. Springer.
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