Leveraging Artificial Intelligence for Personalized Learning and Student Success: Evidence from Economics Education

Öz

Integrating an Artificial Intelligence into education has emerged as a promising approach to enhancing student success across diverse academic settings. This study examines how student outcomes, including academic performance, engagement, and overall well-being, can be improved through the strategic use of aArtificial Intelligence tool. By leveraging AI-driven based tools and technologies, educators can personalize learning experiences, deliver timely feedback, and identify individualized learning pathways tailored to students' specific needs. Drawing on primary data collected from undergraduate economics courses, this research presents a model for navigating AI integration to support personalized learning and academic achievement. We used A simple correlation analysis was used to explore the relationship between assignments, tasks, and student performance, allowing for the early identification of students at risk. These insights were used to inform targeted interventions through AI-supported learning with course specific Chat Bot (AI agent.). Further the study analyzed how much overall course grade improved after the integration of personalized learning tool given to targeted at Risk Students. Results gave evidence of improvement, which further open the door for all the educator with any disciple to include in any class size to improve and help the students in their academic success pathways. This study demonstrates the result oriented idea, and could be base for further study in this area.

Anahtar Kelimeler

• Artificial intelligence
• Adaptive Learning education
• Chat-Bot
• Digital Literacy
• Personalized learning solutions
• Pedagogy
• Prompt engineering

Kaynakça

1. Adeshola, I., & Adepoju, A. P. (2023). The opportunities and challenges of ChatGPT in education. Interactive Learning Environments. https://doi.org/10.1080/10494820.2023.2253858
2. Brusilovsky, P. (2024). AI in education, learner control, and human–AI collaboration. International Journal of Artificial Intelligence in Education, 34, 122–135. https://doi.org/10.1007/s40593-023-00356-z
3. Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/our-work/artificial-intelligence-in-education
4. Korinek, A. (2023). Generative AI for economic research: Use cases and implications for economists. Journal of Economic Literature, 61(4), 1281–1317. https://doi.org/10.1257/jel.20231736
5. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008
6. Nguyen, A., Ngo, H. N., Hong, Y., & Dang, B. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11316-w
7. Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ’12) (pp. 252–254). ACM. https://doi.org/10.1145/2330601.2330661
8. U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations (Report). https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf

9. Vatandoust Mohammadieh, M., Mohajeri, M. M., Keramati, A., & Ahmadabadi, M. N. (2024). AI-powered digital framework for personalized economical quality learning at scale (arXiv preprint). https://doi.org/10.48550/arXiv.2412.04483
10. Wang, T., Lund, B. D., Marengo, A., Pagano, A., Mannuru, N. R., Teel, Z. A., & Pange, J. (2023). Exploring the potential impact of artificial intelligence (AI) on international students in higher education: Generative AI, chatbots, analytics, and international student success. Applied Sciences, 13(11), 6716. https://doi.org/10.3390/app13116716
11. Xie, H., Chu, H.–C., Hwang, G.–J., & Wang, C.–C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599. https://doi.org/10.1016/j.compedu.2019.103599
12. 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, Article 39, 1–27. https://doi.org/10.1186/s41239-019-0171-0