AI-Supported Digital Tools for Enhancing Learning Outcomes in Science and Mathematics Education
DOI:
https://doi.org/10.70232/jcsml.v3i2.57Keywords:
Artificial Intelligence, Digital Learning Tool, Learning Outcome, Mathematics Education, Science EducationAbstract
Particularly in science and math classes where students struggle with abstract thinking, conceptual comprehension, problem-solving, and sustained academic engagement, Artificial Intelligence (AI) has grown in importance as a component of instructional technology. As a result, AI-enabled digital solutions provide chances for data-driven teaching support, adaptive feedback, and personalized learning pathways. With a focus on conceptual comprehension, problem-solving skills, academic engagement, and self-directed learning, this research sought to determine the degree to which AI-powered digital tools improve students’ learning outcomes in science and mathematics. The study used a mixed-methods approach, combining qualitative information from student comments and classroom observations with quantitative assessments of academic progress. The research was conducted in upper secondary and postsecondary educational environments. Science and math classrooms are using AI-supported technology like data-driven instructional dashboards, intelligent feedback mechanisms, and adaptive learning systems. While qualitative data offered insight into student participation, classroom interaction, and perceived learning assistance, quantitative data were used to evaluate students’ learning performance before and after the intervention. The results show that using AI-supported digital tools enhanced learning outcomes, especially in courses that call for gradual conceptual expansion, abstract cognition, and iterative reasoning. Additionally, qualitative data indicated that students benefited from increased autonomy during learning activities, timely feedback, ongoing observation, and personalized learning paths. The research comes to the conclusion that, when paired with curriculum goals and efficient instructional design, AI-supported digital tools should be seen as cognitive and pedagogical support systems rather than just technical advancements that might enhance science and math teaching.
References
Acuña Acuña, E. G. (2023). Estrategias para promover la investigación en estudiantes de ingeniería en universidades latinoamericanas. New Trends in Qualitative Research, 17, Article e867. https://doi.org/10.36367/ntqr.17.2023.e867
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587
Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications. https://books.google.com/books?id=-LvwjwEACAAJ
DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). SAGE Publications. https://books.google.com/books?id=48ACCwAAQBAJ
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. https://doi.org/10.4324/9780203887332
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AIED-Book-Excerpt-CCR.pdf
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson. Retrieved from https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/Intelligence-Unleashed-Publication.pdf
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918. https://doi.org/10.1037/a0037123
OECD. (2021). OECD Digital Education Outlook 2021: Pushing the frontiers with artificial intelligence, blockchain and robots. OECD Publishing. https://doi.org/10.1787/589b283f-en
Ritter, S., Anderson, J. R., Koedinger, K. R., & Corbett, A. (2007). Cognitive Tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14(2), 249–255. https://doi.org/10.3758/BF03194060
Schindler, L. A., Burkholder, G. J., Morad, O. A., & Marsh, C. (2017). Computer-based technology and student engagement: A critical review of the literature. International Journal of Educational Technology in Higher Education, 14, Article 25. https://doi.org/10.1186/s41239-017-0063-0
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press. https://www.wiley.com/en-se/Should%2BRobots%2BReplace%2BTeachers%3F%3A%2BAI%2Band%2Bthe%2BFuture%2Bof%2BEducation-p-9781509528967
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin. https://books.google.com/books?id=o7jaAAAAMAAJ
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
Zhai, X., He, P., & Krajcik, J. (2022). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching, 59(10), 1765–1794. https://doi.org/10.1002/tea.21773
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Edwin Gerardo Acuña Acuña
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
