Artificial Intelligence (AI) in Education: Personalized Learning Paths and Insights
Abstract
Artificial Intelligence (AI) has emerged as a transformative force in education, revolutionizing the way personalized learning paths are created and insights are derived from student data. This paper explores the integration of AI in education, focusing on its role in tailoring learning experiences to individual students and extracting valuable insights to inform instructional strategies. It delves into the principles of AI-driven personalization, including adaptive learning algorithms, intelligent tutoring systems, and recommendation engines. The discussion highlights the benefits of AI in education, such as increased student engagement, improved learning outcomes, and enhanced efficiency in educational delivery. Moreover, the paper addresses the ethical and privacy considerations associated with AI, emphasizing the importance of responsible data usage and transparency in AI-driven educational systems. Through a review of empirical studies and case examples, the paper underscores the effectiveness of AI-driven personalized learning and the potential for AI to support educators in making data-informed decisions. The conclusion offers recommendations for educators, institutions, and policymakers on harnessing AI's potential to advance education while addressing ethical and privacy concerns.
Share and Cite
Article Metrics
References
- Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167-207.
- Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1-2), 205-220.
- Conati, C., & VanLehn, K. (2000). Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education, 11(4), 398-415.
- Dillenbourg, P., Baker, M., Blaye, A., & O'Malley, C. (1995). The evolution of research on collaborative learning. In P. Reimann & H. Spada (Eds.), Learning in Humans and Machine: Towards an Interdisciplinary Learning Science (pp. 189-211). Elsevier.
- Jordan, P. W., & Henderson, A. (1995). Interaction analysis: Foundations and practice. The Journal of the Learning Sciences, 4(1), 39-103.
- Koedinger, K. R., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with cognitive tutors. Educational Psychology Review, 19(3), 239-264.
- Pardos, Z. A., & Heffernan, N. T. (2010). Modeling individualization in a bay.