Development and Implementation of An Adaptive Learning Platform Based on Artificial Intelligence Technology
DOI:
https://doi.org/10.55640/eijp-06-02-23Keywords:
Adaptive learning, Artificial Intelligence (AI), Generative AI, Cognitive Load TheoryAbstract
This article presents the development and implementation process of the Adaptive Digital Didactic–Content Remediation System (ADD–CRS), built on artificial intelligence (AI) technology. The purpose of the research is to identify knowledge gaps in digital learning, adaptively remediate them, and ensure transparency in the teaching process. The ADD–CRS system was designed based on a two-stage adaptive retraining methodology, aimed at reducing cognitive load and enhancing learning efficiency. Additionally, the system enables teachers to automatically generate and analyze tests and assignments linked to specific source locations. Future research is planned to focus on automated task assessment, integration with electronic gradebooks, and full automation of formative, midterm, and final assessment processes.
References
Watson, W. R., & Watson, S. L. (2007). An argument for clarity: What are learning management systems, what are they not, and what should they become? TechTrends, 51(2), 28–34.
Ellis, R. K. (2009). Field guide to learning management systems. ASTD Learning Circuits.
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.
Perkins, M., & Roe, J. (2023). Assessments could and should be designed differently given the rise of generative artificial intelligence: A call to action for university assessment policies. Assessment & Evaluation in Higher Education, 48(7), 1068–1082.
Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The knowledge-learning-instruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757–798.
Wang, F., & Hannafin, M. J. (2005). Design-based research and technology-enhanced learning environments. Educational Technology Research and Development, 53(4), 5–23.
McKenney, S., & Reeves, T. C. (2012). Conducting educational design research. Routledge.
Django Software Foundation. (2024). Django documentation.
Vue.js Team. (2024). Vue.js guide.
OpenAI. (2024). GPT-4 technical report. arXiv preprint arXiv:2303.08774.
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.
Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1), 342–363.
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239.
Bearman, M., Ryan, J., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(6), 1550–1566.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Allamova Sh.Sh.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Individual articles are published Open Access under the Creative Commons Licence: CC-BY 4.0.
