CREATION OF A PERSONALIZED LEARNING ENVIRONMENT FOR STUDENTS USING ARTIFICIAL INTELLIGENCE
Keywords:
Personalized Learning, Artificial Intelligence, Educational Technology, Machine Learning, Adaptive Learning Systems, Natural Language ProcessingAbstract
In education, artificial intelligence (AI) has become a disruptive force, especially in the development of personalized learning environments (PLEs). By adjusting instructional materials, tempo, and evaluation techniques to each student's particular requirements, these systems raise student engagement and academic performance. In this study, the design, implementation, and assessment of an AI-powered PLE that dynamically adapts learning pathways through the use of machine learning (ML) and natural language processing (NLP) techniques are investigated. Over the course of eight weeks, 120 secondary school students participated in a pilot program that compared the AI-based system with a conventional e-learning platform. The results show that the AI-supported group significantly improved in terms of academic performance, learner engagement, and student satisfaction. The paper concludes that AI-driven personalization holds great promise in addressing learning diversity and enhancing educational effectiveness.
References
1. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston, MA: Center for Curriculum Redesign.
2. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 1–27.
3. Woolf, B. P. (2020). AI in Education: An Overview. In Spector, J. M. (Ed.), The SAGE Encyclopedia of Educational Technology (pp. 34–42). Sage.
4. Chen, C. M., & Li, Y. L. (2010). Personalised context-aware ubiquitous learning system for supporting effective English vocabulary learning. Interactive Learning Environments, 18(4), 341–364.
5. Baker, R. S. J. d., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning Analytics (pp. 61–75). Springer.
