Attitude Towards Artificial Intelligence, Academic Motivation and Performance of Students

This study examined the levels and relationships among attitude toward artificial intelligence (AI), academic motivation, and academic performance of Grade 6 students. Employing a quantitative descriptive-correlational research design, the study involved 175 Grade 6 learners enrolled in a local elementary school in the Negros Island Region, Philippines, during the School Year 2025–2026. Data were gathered using a researcher-developed questionnaire measuring students’ attitude toward AI and academic motivation, while academic performance was determined from their final general weighted averages. Descriptive statistics, including mean and standard deviation, and inferential statistics, specifically Spearman rank-order correlation were utilized to analyze the data. Results revealed that students demonstrated a high level of attitude toward AI and academic motivation, together with a very satisfactory level of academic performance. Attitude toward AI showed no significant relationship with academic performance, and academic motivation likewise exhibited no significant relationship with academic performance. The findings indicate that although students possessed favorable perceptions of AI and strong academic motivation, neither variable independently corresponded with their academic achievement. The study underscores the importance of implementing responsible AI integration and learner-support initiatives while addressing cognitive, instructional, and environmental factors that contribute to improved academic performance.

Artificial Intelligence, Attitude Toward AI, Academic Motivation, Academic Performance

1. Abdullah, M., Hussin, S., & Ismail, K. (2024). Students' acceptance and readiness toward artificial intelligence in higher education: A systematic review. Education and Information Technologies, 29(2), 1845–1868. https://doi.org/10.1007/s10639-023-12074-8 [Google Scholar] [Crossref]

2. Asio, J. M. R., & Gadia, E. D. (2024). Predictors of student attitudes towards artificial intelligence: Implications and relevance to higher education institutions. International Journal of Didactical Studies, 5(2). https://doi.org/10.33902/ijods.202427763 [Google Scholar] [Crossref]

3. Bureau, J. S., Howard, J. L., Chong, J. X. Y., & Guay, F. (2022). Pathways to student motivation: A meta-analysis of antecedents of autonomous and controlled motivations. Review of Educational Research, 92(1), 46–72. https://doi.org/10.3102/00346543211042426 [Google Scholar] [Crossref]

4. Camacho, A. C. L. F., Vera, M. A. M., & Roldan, M. D. C. G. (2021). Academic motivation and engagement among students during online learning: A self-determination theory perspective. International Journal of Learning, Teaching and Educational Research, 20(11), 1–18. https://doi.org/10.26803/ijlter.20.11.1 [Google Scholar] [Crossref]

5. Chan, C. K. Y., & Hu, W. (2023). Students' voices on generative AI: Perceptions, benefits, challenges, and acceptance of ChatGPT in higher education. Education and Information Technologies, 29, 1–25. https://doi.org/10.1007/s10639-023-12195-7 [Google Scholar] [Crossref]

6. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, Article 22. https://doi.org/10.1186/s41239-023-00392-8 [Google Scholar] [Crossref]

7. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., & Albanna, H., et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 [Google Scholar] [Crossref]

8. Guay, F. (2022). Applying self-determination theory to education: Regulations, motivation, and achievement. Canadian Psychology, 63(4), 331–342. [Google Scholar] [Crossref]

9. Howard, J. L., Bureau, J. S., Guay, F., Chong, J. X. Y., & Ryan, R. M. (2021). Student motivation and associated outcomes: A meta-analysis from self-determination theory. Perspectives on Psychological Science, 16(6), 1300–1323. https://doi.org/10.1177/1745691620966789 [Google Scholar] [Crossref]

10. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeiffer, F., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274 [Google Scholar] [Crossref]

11. Liu, Y., Wang, Z., & Zhang, Y. (2022). Academic motivation and student engagement: A systematic review of recent evidence. Frontiers in Psychology, 13, 898609. https://doi.org/10.3389/fpsyg.2022.898609 [Google Scholar] [Crossref]

12. Mega, C., Ronconi, L., & De Beni, R. (2021). What makes a successful student? Revisiting the relationships among motivation, self-regulated learning, and academic achievement. Educational Psychology Review, 33(4), 1315–1337. [Google Scholar] [Crossref]

13. Organisation for Economic Co-operation and Development. (2023). PISA 2022 results (Volume I): The state of learning and equity in education. OECD Publishing. https://doi.org/10.1787/53f23881-en [Google Scholar] [Crossref]

14. Ryan, R. M., & Deci, E. L. (2023). Self-determination theory: Basic psychological needs in motivation, development, and wellness (2nd ed.). Guilford Press. [Google Scholar] [Crossref]

15. Sailer, M., Schultz-Pernice, F., & Fischer, F. (2023). The role of motivation and self-regulated learning in explaining students’ academic success in digital learning environments. Computers and Education Open, 4, 100127. https://doi.org/10.1016/j.caeo.2023.100127 [Google Scholar] [Crossref]

16. Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11(6), 887. https://doi.org/10.3390/healthcare11060887 [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study