Use of Motives and Utilization of Artificial Intelligence as Predictors of Information Retention in Science

Authors

Paolo O. Baquial

Graduate School, Holy Cross of Davao College (Philippines)

Article Information

DOI: 10.47772/IJRISS.2026.100400471

Subject Category: Science Education

Volume/Issue: 10/4 | Page No: 6576-6588

Publication Timeline

Submitted: 2026-04-22

Accepted: 2026-04-27

Published: 2026-05-14

Abstract

The ongoing issue of poor information retention continues to hinder students' mastery of scientific concepts. This study examined the predictive power of the Use of Motives and Utilization of Artificial Intelligence on Information Retention in Science, as measured by students’ subjective assessment of their ability to recall and apply scientific concepts. The study was conducted among 266 Grade 12 STEM students in Davao City. Adopting a predictive research design, data were selected through total enumeration and rigorously analyzed using descriptive statistics, Pearson Product-moment correlation, and multiple linear regression analysis.
Descriptive results revealed that Use of Motives, Utilization of AI, and Information Retention all achieved "High" descriptive levels, suggesting that students possess strong internal drives and frequently engage with AI tools. Correlation analysis further indicated significant, moderately high positive relationships between both Use of Motives (r=0.674, p<0.05) and Utilization of AI (r=0.677, p<0.05) with the criterion variable. The multiple regression results demonstrated a significant combined predictive relationship, with the model (F=143.1, p<0.001) accounting for 52.1% of the total variance in perceived retention levels.
These findings partially confirm the Technology Acceptance Model (TAM), suggesting that students' cognitive success is significantly associated with the interplay between perceived usefulness and strategic digital engagement. The results are significant for school leaders and administrators in formulating technology-integrated policies to mitigate retention gaps. Furthermore, this study offers a foundational framework for future research to explore the remaining 47.9% of unexplained variance through qualitative factors or additional predictive variables.

Keywords

Motives and utilization of artificial intelligence, predictors of information retention, science

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References

1. Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in human behavior, 56, 238–256. [Google Scholar] [Crossref]

2. Abucayon, M. J., Araza, N. J., Gargar, S. M., & Haspe, A. (2025). The quality of instruction and services as predictors of re-enrollment intent among Grade 7–9 students in private Catholic school in Davao City. [Google Scholar] [Crossref]

3. Akgun, A., & Toker, S. (2024). Evaluating the effect of pretesting with conversational AI on retention of needed information. Journal of Educational Psychology, 116(1), 123–138. [Google Scholar] [Crossref]

4. Al-Azawei, A., & Al-Maroof, R. S. (2023). Integrating the technology acceptance model and self-determination theory to investigate students’ acceptance of AI-powered learning tools. Journal of Educational Computing Research, 61(4), 850–875.https://doi.org/10.1177/07356331231165142 [Google Scholar] [Crossref]

5. Armstrong, R. A. (2019). Should Pearson’s correlation coefficient be avoided? Ophthalmic and Physiological Optics, 39(5), 316–327.https://doi.org/10.1111/opo.12636 [Google Scholar] [Crossref]

6. Babasola, K. M., Okhiria, A., Bale, S. I., Sorunke, T. A. A., & Olasunkanmi, U. (2024). A periodicalof the Faculty of Natural and Applied Sciences, UMYU, Katsina. [Google Scholar] [Crossref]

7. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in socialpsychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173 [Google Scholar] [Crossref]

8. Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32(4), 1052–1092. https://doi.org/10.1007/s40593-021-00285-9 [Google Scholar] [Crossref]

9. Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., & Zawacki-Richter, O. (2023). Speculative futures of artificial intelligence and artificial general intelligence in education. Asian Journal of Distance Education, 18(1), 1–17. https://doi.org/10.1016/j.futures.2026.103762 [Google Scholar] [Crossref]

10. Bravo, E. D. (2023). Trends and causes of student dropouts in a public higher education in Northern Philippines: A data visualization approach. [Google Scholar] [Crossref]

11. Cabrera, J., Dalagan, S. E., & Lugo, M. G. R. (2025). Dropout analysis in Davao Oriental State University. East Asian Journal of Multidisciplinary Research, 4(4), 1755 1764. [Google Scholar] [Crossref]

12. Chen, L., Chen, P., & Lin, Z. (2022). Artificial intelligence in education: A review. IEEE Access, 8, 75265–75278. doi: 10.1109/ACCESS.2020.2988510. [Google Scholar] [Crossref]

13. Chiu, T. K., Meng-Hsuan, H., & Cheng, J. W. (2021). The role of self-determination theory in explaining students’ motivation to use artificial intelligence in learning. Educational Technology Research and Development, 69(1), 1–22. https://doi.org/10.1007/s11423-021-09984-2 [Google Scholar] [Crossref]

14. Chukwu, C. O., Chukwu, J. C., & Odey, F. A. (2026). AI-powered learning tools on measurement of student engagement across academic disciplines: Implications of age and gender. Educational Point, 3(1), e144. https://doi.org/10.71176/edup/17782 [Google Scholar] [Crossref]

15. Costa, J. M. (2024). Descriptive statistics in educational research: Organizing and summarizing data patterns. Academic Press. [Google Scholar] [Crossref]

16. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. [Google Scholar] [Crossref]

17. Doyaoen, S. I., Cabote, G. D. D., Viernes, L. G., & Martinez, E. S. (2024). Analysis of education student retention rates: Basis for policy formation. International Journal of Social Science and Education Research Studies, 4(12). [Google Scholar] [Crossref]

18. Etikan, I. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.1 [Google Scholar] [Crossref]

19. Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Noroozi, A. (2024). Artificial intelligence in education: A systematic literature review on critical thinking, problem-solving, and decision-making. International Journal of Educational Technology in Higher Education, 21(1), 1–28. https://doi.org/10.1016/j.eswa.2024.124167 [Google Scholar] [Crossref]

20. Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. [Google Scholar] [Crossref]

21. Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press. [Google Scholar] [Crossref]

22. Higde, E., & Aktamis, H. (2022). Exploring the role of motivation in STEM education: A systematic review. Journal of Science Education and Technology. [Google Scholar] [Crossref]

23. Holmes, W., Persson, J., Chounta, I. A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. British Journal of Educational Technology, 53(3), 563–579. [Google Scholar] [Crossref]

24. Hwang, S., Horvát, E. Á., & Romero, D. M. (2023). Information retention in the multi platform sharing of science. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 17, pp. 375–386). AAAI Press. [Google Scholar] [Crossref]

25. Hwang, G.-J., & Tu, Y.-F. (2021). Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584 [Google Scholar] [Crossref]

26. Karam, J., Ibrahim, S. A., Mahmoud, L., Zeidan, N., Salameh, P., & Alami, N. H. (2022). Factors contributing to the improvement of university students’ academic performance and knowledge retention in an online learning environment. International Journal of Education Research, 10(9), 93106. [Google Scholar] [Crossref]

27. Kovanović, V., Joksimović, S., & Gašević, D. (2021). The role of motivation in student learning: A study of online educational environments. Computers & Education,160,104037. [Google Scholar] [Crossref]

28. Lærd Dissertation. (2022). Sampling: The basics. https://dissertation.laerd.com/sampling the-basics.php [Google Scholar] [Crossref]

29. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2021). Intelligence unleashed: An argument for AI in education. Pearson. [Google Scholar] [Crossref]

30. Matolić T, Jurakić D, Greblo Jurakić Z, Maršić T and Pedišić Ž (2023) Development and validation of the EDUcational Course Assessment TOOLkit (EDUCATOOL) – a 12 item questionnaire for evaluation of training and learning programmes. Front. Educ. 8:1314584. doi: 10.3389/feduc.2023.1314584 [Google Scholar] [Crossref]

31. Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 57(4), 632–645. https://doi.org/10.1111/ejed.12527 [Google Scholar] [Crossref]

32. Oranga, J., & Matere, A. (2025). Quantitative research: Types, advantages, generalizability & limitations. Open Access Library Journal, 12, 1–9. [Google Scholar] [Crossref]

33. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing andcomparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879 [Google Scholar] [Crossref]

34. Rakrak, M. (2025). Central tendency and data distribution: How to choose the right measure for accurate analysis. International Journal of Literacy and Education, 5(1), 71–75. https://doi.org/10.22271/27891607.2025.v5.i1b.253 [Google Scholar] [Crossref]

35. Rizk, N. (2023). Correlation analysis in the social sciences: Measuring relationships between continuous variables. Global Research Journal. [Google Scholar] [Crossref]

36. Royle, J., & Lincoln, N. B. (2008). The Everyday Memory Questionnaire–Revised: Development of a 13-item scale. Disability and Rehabilitation, 30(2), 114–121. https://doi.org/10.1080/09638280701223876 [Google Scholar] [Crossref]

37. Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. [Google Scholar] [Crossref]

38. Seli, P., Wammes, J. D., & Smilek, D. (2025). On the relation between motivation and retention in educational contexts: The role of intentional and unintentional mind wandering. Psychonomic Bulletin & Review. [Google Scholar] [Crossref]

39. Selwyn, N. (2022). Should robots replace teachers? AI and the future of education. Polity Press. [Google Scholar] [Crossref]

40. Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2023). Data mining for business analytics:Concepts, techniques, and applications in Python (2nd ed.). Wiley. [Google Scholar] [Crossref]

41. Sghir, N., Adadi, A., & Lahmer, M. (2022). Recent advances in predictive learning analytics: Adecade systematic review (2012–2022). Education and Information Technologies. [Google Scholar] [Crossref]

42. StatPearls. (2024). Standard deviation. In StatPearls [Internet]. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK574574/ [Google Scholar] [Crossref]

43. Sullivan, M., Kelly, A., & McLaughlan, P. (2024). ChatGPT in higher education: Student awareness and perceptions of ethical use. Journal of Applied Learning and Teaching, 7(1), 15–29. DOI:10.37074/jalt.2023.6.1.17 [Google Scholar] [Crossref]

44. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson. [Google Scholar] [Crossref]

45. Willingham, D. T. (2020). Why students forget and what you can do about it. Educational Psychology Review, 32(2), 475–488. https://doi.org/10.1007/s10648-020-09520-2 [Google Scholar] [Crossref]

46. Uyanık, G. K., & Güler, N. (2013). A study on multiple linear regression analysis. Procedia Social and Behavioral Sciences, 106, 234–240. https://doi.org/10.1016/j.sbspro.2013.12.027 [Google Scholar] [Crossref]

47. Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342–365. http://www.jstor.org/stable/23011042 [Google Scholar] [Crossref]

48. Vera, F. (2023). Cuestionario de Inteligencia Artificial en la Educación Superior (QUIA). Chile: Creative Commons BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/ [Google Scholar] [Crossref]

49. Yurt, E., & Kasarci, I. (2024). A questionnaire of artificial intelligence use motives: Contribution to investigating the connection between AI and motivation. International Journal of Technology in Education, 7(2), 308–325. https://doi.org/10.46328/ijte.725 [Google Scholar] [Crossref]

50. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2020). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 17(1), 1–27. [Google Scholar] [Crossref]

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