The Role of AI in Enhancing the Academic Achievement of Slow Learners in the Batticaloa District
DOI:
https://doi.org/10.64229/aryhjn02Keywords:
Slow Learners, AI, Inclusive Education, Junior Secondary, Achievement, Enhance, TechnologyAbstract
This study investigates the impact of AI-powered learning systems on improving the academic achievement of slow learners within the 21st-century educational landscape of inclusive education. The research seeks to determine how AI technologies can help identify specific student challenges, reduce learning gaps, and provide customized learning experiences. The ultimate goal is to build self-confidence, increase student engagement, and improve overall learning efficiency in the classroom. A main challenge identified in the study is the digital divide-the lack of technical facilities and internet access for students in rural and underdeveloped areas. To address this, a mixed-methods approach was used, involving 200 students, 250 teachers, 10 in-service advisors (ADS/ISAs), and 80 parents from all five zones in the Batticaloa District. Data on performance were collected through questionnaires, interviews, observations, and document analysis, with subsequent analysis performed using SPSS software. Findings indicate that AI-based interventions can significantly improve student achievement by creating customized learning paths and adapting the curriculum to individual learning speeds. AI's impact has fundamentally changed the roles of teachers, the tools they use, and the content they deliver. The research also revealed that AI tools can empower teachers to provide immediate and necessary assistance. The study recommends creating localized, interactive AI learning resources, offering comprehensive professional training for teachers, improving the distribution of technical facilities, and fostering collaboration with policymakers. By implementing these recommendations, the country can better achieve its future educational goals and adapt to timely technological changes. This study also emphasizes the importance of conducting research not only on future technological developments but also on the rapid change occurring in technology today.
References
[1]Baker, R.S., Inventado, P.S. (2014). Educational Data Mining and Learning Analytics. In: Larusson, J., White, B. (eds) Learning Analytics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3305-7_4
[2]Roll, I., Wylie, R. Evolution and Revolution in Artificial Intelligence in Education. Int J Artif Intell Educ 26, 582–599 (2016). https://doi.org/10.1007/s40593-016-0110-3
[3]Zawacki-Richter, O., Marín, V.I., Bond, M. et al. Systematic review of research on artificial intelligence applications in higher education – where are the educators?. Int J Educ Technol High Educ 16, 39 (2019). https://doi.org/10.1186/s41239-019-0171-0
[4]L. Chen, P. Chen and Z. Lin, "Artificial Intelligence in Education: A Review," in IEEE Access, vol. 8, pp. 75264-75278, 2020, https://doi.org/10.1109/ACCESS.2020.2988510
[5]Chiu, T. K. F., & Chai, C.-s. (2020). Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective. Sustainability, 12(14), 5568. https://doi.org/10.3390/su12145568
[6]Yang, QF., Lian, LW. & Zhao, JH. Developing a gamified artificial intelligence educational robot to promote learning effectiveness and behavior in laboratory safety courses for undergraduate students. Int J Educ Technol High Educ 20, 18 (2023). https://doi.org/10.1186/s41239-023-00391-9
[7]Popenici, S.A.D., Kerr, S. Exploring the impact of artificial intelligence on teaching and learning in higher education. RPTEL 12, 22 (2017). https://doi.org/10.1186/s41039-017-0062-8
[8]VanLEHN, K. (2011). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
[9]Qi Xia, Thomas K.F. Chiu, Min Lee, Ismaila Temitayo Sanusi, Yun Dai, Ching Sing Chai, A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education, Computers & Education. https://doi.org/10.1016/j.compedu.2022.104582
[10]Ke Zhang, Ayse Begum Aslan, AI technologies for education: Recent research & future directions, Computers and Education: Artificial Intelligence. https://doi.org/10.1016/j.caeai.2021.100025
[11]Pargmann, J., Berding, F., Rebmann, K. et al. How AI feedback supports lesson planning in vocational teacher education: a longitudinal intervention study using an analytical AI platform. Empirical Res Voc Ed Train 17, 26 (2025). https://doi.org/10.1186/s40461-025-00202-7
[12]Shemshack, A., Kinshuk & Spector, J.M. A comprehensive analysis of personalized learning components. J. Comput. Educ. 8, 485–503 (2021). https://doi.org/10.1007/s40692-021-00188-7
[13]Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, *2*, 100020. https://doi.org/10.1016/j.caeai.2021.100020
[14]Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press. https://doi.org/10.7330/9781607329305
Downloads
Published
Issue
Section
License
Copyright (c) 2025 K. Abilash, N. Mahthi Hassan (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.