Effect of Magic School AI Tool on Elementary Students’ Academic Achievement
Keywords:
Artificial intelligence, Magic school AI, Academic achievement, Engagement, Problem solving skills, MotivationAbstract
This research was designed to compare the academic achievement of elementary students by using Magic School AI (Artificial intelligence tool) and traditional teaching methods. The main objective of the study was to assess the effectiveness of Magic School in enhancing the academic achievement of elementary-level students. The research was quantitative in nature and it used a quasi-experimental design. The tool of the research was a self-developed achievement test from seventh grade General Science textbook. Tool was developed using Magic School AI. Simple random sampling technique was used to select two sample classes from among the population. These two classes and their students served as sample of the study. Selected classes were assigned to as control and experimental groups. The treatment span was six weeks. Collected data was analyzed using statistical techniques (i.e., Mean, SD, and t-test). On the basis of the findings of the study it was concluded that students' academic achievement was almost the same before the treatment. After the treatment, the academic achievement of students in the experimental group was higher than that of the control group. Thus, equal-ability students when taught through the Magic School AI tool, performed better after the treatment. Hence, the integration of Magic School AI in the teaching method proved a better alternative than the traditional method for teaching science at the elementary level.
References
Al-Abdullatif, A. M., & Gameil, A. A. (2021). The Effect of Digital Technology Integration on Students' Academic achievement through Project-Based Learning in an E-Learning Environment. International Journal of Emerging Technologies in Learning, 16(11). DOI:10.3991/ijet.v16i11.19421
Borg, K., & Smith, L. (2018). Digital inclusion and online behaviour: five typologies of Australian internet users. Behaviour & information technology, 37(4), 367-380. DOI: 10.1080/0144929X.2018.1436593
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616-630. DOI: 10.1007/s11528-022-00715-y
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278. DOI: 10.59992/IJCI.2025.v4n1p1
Fahsyah Nurzaman, A., Teresa Tedja, R., Kusuma, L., Kurniawan, Y., Bhutkar, G., & Johan, J. (2021, June). LAISES–Learning AI Integrating System for Elementary Students. In Proceedings of the 5th International Conference on Graphics and Signal Processing (pp. 65-71). DOI: 10.1145/3474906.3474913
Guilherme, A. (2019). AI and education: the importance of teacher and student relations. AI & society, 34, 47-54. DOI: 10.1007/s00146-017-0693-8
Hwang, G. J., Xie, H., Wah, B. W., & Gaševi?, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001.
Jing. (2018). China wants to bring artificial intelligence to its classrooms to boost its education system. Science & Research, South China Morning Post. Available at: htps://
www.scmp.com/tech/science-research/ artcle/2115271/china-wants-bring-artfcialintelligence-its-classrooms-boost DOI: 10.13140/RG.2.2.19322.09929
Lee, D., & Kwon, H. (2024). Meta-analysis on effects of artificial intelligence education in K-12 South Korean classrooms. Education and Information Technologies, 1-36. DOI: 10.1007/s10639-024-12738-4
Mishra, R., & Varshney, D. (2024). Comprehensive analysis of human and AI task allocation in the education sector: Defining futuristic roles and responsibilities. World Journal of Advanced Research and Reviews, 22(3), 1883-1893. DOI: https://doi.org/10.30574/wjarr.2024.22.3.1949 DOI: 10.30574/wjarr.2024.22.3.1949
Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. International Journal of Educational Technology in Higher Education, 20(1), 4. DOI: 10.1186/s41239-022-00372-4
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.
Pihir, I., Tomi?i?-Pupek, K., & Furjan, M. T. (2018). Digital transformation insights and trends. In Central European Conference on Information and Intelligent Systems (pp. 141-149). Faculty of Organization and Informatics Varazdin. DOI: 10.54254/2754-1169/2025.20673
Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and practice in technology enhanced learning, 12(1), 22.
Rigby, C. (2016). ‘How Software that Learns as It Teaches is Upgrading Brazilian Education’. The Guardian. Available at:https://www.theguardian.com/technology/2016/jan/10/geekie-educational-software-brazil-machinelearning
Saini, R., Mussbacher, G., Guo, J. L., & Kienzle, J. (2019, September). Teaching modelling literacy: An artificial intelligence approach. In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELSC) (pp. 714-719). IEEE.
Smith, J., & Johnson, L. (2020). The impact of artificial intelligence on personalized learning. Journal of Educational Technology, 35(2), 45-60.
Steffi Grado. (June 28, 2024) Enhancing Student Outcomes and Restorative Practices using Magic School AI
Thomas, D. R., Lin, J., Gatz, E., Gurung, A., Gupta, S., Norberg, K., & Koedinger, K. R. (2024, March). Improving student learning with hybrid human-AI tutoring: A three-study quasi-experimental investigation. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 404-415). DOI: 10.1145/3636555.3636896
WISE (2011). ‘Geekie: Personalized Learning for All’. WISE Initiative. Available at: htps://www.wise-qatar.org/geekie-personalized-learningall-brazil
XPrize Global Learning (2019). Empowering children to take control of their own learning. Available at: htps://www.xprize.org/prizes/global-learning
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19. https://doi.org/10.1145/3298981
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(1), 127. DOI: 10.1186/s41239-019-0171-0
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., ... & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021(1), 8812542. DOI: 10.1155/2021/8812542
Zhou, Y., & Lee, K. (2021). AI in education: Advancements in automated grading systems in China. Journal of Educational Innovation, 42(1), 23-35.
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. doi: 10.1016/j.caeai.2021.100025
