The Nexus Between AI Self-Efficacy and Attitude Towards AI of University Students in Davao City as Moderated by Sex

Authors

  • Jerlan Anthony D. Guipitacio University of Mindanao, Davao City, Philippines
  • Angelo Vincent B. Aleman University of Mindanao, Davao City, Philippines
  • Cleofe Margarette Bonsubre University of Mindanao, Davao City, Philippines
  • Jessie Mar T. Galleto University of Mindanao, Davao City, Philippines
  • Bruce Nolan B. Tapere University of Mindanao, Davao City, Philippines
  • John Harry Caballo University of Mindanao, Davao City, Philippines
  • Ria Bianca Caangay Ateneo De Davao University, Davao City, Philippines

DOI:

https://doi.org/10.54536/ajsts.v4i1.3788

Keywords:

AI Self-Efficacy, Artificial Intelligence in Education, Attitudes Toward AI, University Students

Abstract

This study quantitatively explores how sex moderates the relationship between AI self-efficacy and attitudes toward AI among university students in Davao City, Philippines. Data were obtained online via google forms using tailored questionnaires, with respondents chosen using stratified random sampling. The measurement model was tested for validity and reliability, and the constructs were defined using descriptive statistics. To evaluate the suggested moderation model, a moderation analysis was conducted using smartpls 4.0’s standard bootstrapping technique. The results showed that the constructs were valid and reliable, with university students exhibiting modest levels of ai self-efficacy and attitude toward ai. Furthermore, the study found that sex had a significant moderating role in the relationship between AI self-efficacy and attitude toward AI.

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References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020

Alzahrani, L. (2023). Analyzing Students’ Attitudes and Behavior Toward Artificial Intelligence Technologies in Higher Education. International Journal of Recent Technology and Engineering (IJRTE). https://doi.org/10.35940/ijrte.f7475.0311623.

Ayanwale, M. (2023). Evidence from Lesotho Secondary Schools on Students’ Intention to Engage in Artificial Intelligence Learning. 2023 IEEE AFRICON, 1-6. https://doi.org/10.1109/AFRICON55910.2023.10293644.

Bourne, J. (2019). How Squirrel AI is looking to provide ‘adaptive learning’ to revolutionise education through AI and big data. AI News. https://www.artificialintelligence-news.com/news/how-squirrel-ai-is-looking-to-provide-adaptive-learning-to-revolutionise-education-through-ai-and-big-data/

Chai, C., Chiu, T., Wang, X., Jiang, F., & Lin, X. (2022). Modeling Chinese Secondary School Students’ Behavioral Intentions to Learn Artificial Intelligence with the Theory of Planned Behavior and Self-Determination Theory. Sustainability. https://doi.org/10.3390/su15010605.

Chai, C., Wang, X., & Xu, C. (2020). An Extended Theory of Planned Behavior for the Modelling of Chinese Secondary School Students’ Intention to Learn Artificial Intelligence. Mathematics. https://doi.org/10.3390/math8112089.

Charness, N., & Boot, W. (2016). Handbook of the Psychology of Aging. ScienceDirect. https://www.sciencedirect.com/book/9780124114692/handbook-of-the-psychology-of-aging

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.

Chen, S., Su, Y., Ku, Y., Lai, C., & Hsiao, K. (2022). Exploring the factors of students’ intention to participate in AI software development. Library Hi Tech. https://doi.org/10.1108/lht-12-2021-0480.

Creswell, J. W. (2022). Research design: Qualitative, quantitative, and mixed methods approaches 3rd ed. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (6th Ed.).

Dogan, M., Dogan, T., & Bozkurt, A. (2023). The Use of Artificial Intelligence (AI) in Online Learning and Distance Education Processes: A Systematic Review of Empirical Studies. Applied Sciences. https://doi.org/10.3390/app13053056.

Dzin, N., & Lay, Y. (2021). Validity and Reliability of Adapted Self-Efficacy Scales in Malaysian Context Using PLS-SEM Approach. Education Sciences. https://doi.org/10.3390/educsci11110676.

Fietta, V., Zecchinato, F., Stasi, B., Polato, M., & Monaro, M. (2022). Dissociation Between Users’ Explicit and Implicit Attitudes Toward Artificial Intelligence: An Experimental Study. IEEE Transactions on Human-Machine Systems, 52, 481-489. https://doi.org/10.1109/thms.2021.3125280.

Fryer, L., Thompson, A., Nakao, K., Howarth, M., & Gallacher, A. (2020). Supporting self-efficacy beliefs and interest as educational inputs and outcomes: Framing AI and Human partnered task experiences. Learning and Individual Differences, 80, 101850. https://doi.org/10.1016/j.lindif.2020.101850.

Gallagher, M. W. (2012). Encyclopedia of Human Behavior. ScienceDirect. https://www.sciencedirect.com/referencework/9780080961804/encyclopedia-of-human-behavior

Gligorea, I., Cioca, M., Oancea, R., Gorski, A., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences. https://doi.org/10.3390/educsci13121216.

Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1191628.

Hamal, O., Faddouli, N., Harouni, M., & Lu, J. (2022). Artificial Intelligent in Education. Sustainability. https://doi.org/10.3390/su14052862.

Hamid, M. R. A., Sami, W., & Sidek, M. H. M. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890, 012163. https://doi.org/10.1088/1742-6596/890/1/012163

Harry, A. (2023). Role of AI in Education. Interdiciplinary Journal and Hummanity (INJURITY). https://doi.org/10.58631/injurity.v2i3.52.

Hashim, S., Omar, M., Jalil, H., & Sharef, N. (2022). Trends on Technologies and Artificial Intelligence in Education for Personalized Learning: Systematic Literature Review. International Journal of Academic Research in Progressive Education and Development. https://doi.org/10.6007/ijarped/v11-i1/12230.

Henseler, J., Ringle, C.M. and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the Academy of Marketing Science, 43(1), 1-21.

Ho, Y., Alam, S., Masukujjaman, M., Lin, C., Susmit, S., & Susmit, S. (2022). Intention to Adopt AI-Powered Online Service Among Tourism and Hospitality Companies. Int. J. Technol. Hum. Interact., 18, 1-19. https://doi.org/10.4018/ijthi.299357.

Hong, J. W. (2022). I Was Born to Love AI: The Influence of Social Status on AI Self-Efficacy and Intentions to Use AI. International Journal of Communication, 16, 172–191. https://doi.org/10.1146/annurev.psych.58.110405.085542

Kukul, V., & Karataş, S. (2019). Computational Thinking Self-Efficacy Scale: Development, Validity and Reliability. Informatics Educ., 18, 151-164. https://doi.org/10.15388/INFEDU.2019.07.

Livini, R., Gunnesch-Luca, G., & Iliescu, D. (2021). Research self-efficacy: A meta-analysis. Educational Psychologist, 56, 215 - 242. https://doi.org/10.1080/00461520.2021.1886103.

MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58(1), 593–614.

Montag, C., Kraus, J., Baumann, M., & Rozgonjuk, D. (2023). The propensity to trust in (automated) technology mediates the links between technology self-efficacy and fear and acceptance of artificial intelligence. Computers in Human Behavior Reports, 11, 1-7. https://doi.org/10.1016/j.chbr.2023.100315

Nazari, N., Shabbir, M., & Setiawan, R. (2021). Application of Artificial Intelligence powered digital writing assistant in higher education: randomized controlled trial. Heliyon, 7. https://doi.org/10.1016/j.heliyon.2021.e07014.

Obenza, B., Caballo, J. H., & Caangay, R. (2024). Analyzing university students’ attitude and behavior toward AI using the extended unified theory of acceptance and use of technology model. American Journal of Applied Statistics and Economics, 3(1), 99–108. https://doi.org/10.54536/ajase.v3i1.2510

Obenza, B., Salvahan, A., Rios, A. N., Solo, A., Alburo, R. A., & Gabila, R. J. (2023). University students’ perception and use of ChatGPT: Generative artificial intelligence (AI) in higher education. International Journal of Human Computing Studies, 5. https://doi.org/10.5281/zenodo.10360697

Obenza, B. N., Baguio, J. S. I. E., Bardago, K. M. W., Granado, L. B., Loreco, K. C. A., Matugas, L. P., Talaboc, D. J., Zayas, R. K. D. D., Caballo, J. H. S., & Caangay, R. B. R. (2023). The Mediating Effect of AI Trust on AI Self-Efficacy and Attitude Toward AI of College Students. International Journal of Metaverse, 2(1), 1–10. https://doi.org/10.54536/ijm.v2i1.2286

Obenza, B. N., Go, L. E., Francisco, J. A. M., Buit, E. E. T., Mariano, F. V. B., Cuizon Jr, H. L., Cagabhion, A. J. D., & Agbulos, K. A. J. L. (2024). The Nexus between Cognitive Absorption and AI Literacy of College Students as Moderated by Sex. American Journal of Smart Technology and Solutions, 3(1), 32–39. https://doi.org/10.54536/ajsts.v3i1.2603

Obenza-Tanudtanud, D. M. N., & Obenza, B. N. (2024). Assessment of Educational Digital Game-Based Learning and Academic Performance of Grade Six Pupils. American Journal of Interdisciplinary Research and Innovation, 3(1), 1–9. https://doi.org/10.54536/ajiri.v3i1.2338

Park, S. (2023). Analysis of AI using Education Competency according to AI Value Perception and AI Self-efficacy Cluster Types of Students in Graduate School of Education. Asia-pacific Journal of Convergent Research Interchange. https://doi.org/10.47116/apjcri.2023.06.47.

Ramayah, T., Hwa, C. J., Chuah, F., & Memon, M. A. (2017). PLS-SEM using SmartPLS 3.0: Chapter 13: Assessment of Moderation Analysis. ResearchGate. https://www.researchgate.net/publication/341357609_PLS-SEM_using_SmartPLS_30_Chapter_13_Assessment_of_%20Moderation_Analysis

Ringle, C., M., Wende, S., & Becker, J. (2024). SmartPLS 4. Bönningstedt: SmartPLS. Retrieved from https://www.smartpls.com

Suh, W., & Ahn, S. (2022). Development and validation of a scale measuring student attitudes toward artificial intelligence. SAGE Open, 12(2), 215824402211004. https://doi.org/10.1177/21582440221100463

Tang, K., Chang, C., & Hwang, G. (2021). Trends in artificial intelligence-supported e-learning: a systematic review and co-citation network analysis (1998–2019). Interactive Learning Environments, 31, 2134 - 2152. https://doi.org/10.1080/10494820.2021.1875001.

Wilson, A., Brega, A., Thomas, J., Henderson, W., Lind, K., Braun, P., Batliner, T., & Albino, J. (2018). Validity of Measures Assessing Oral Health Beliefs of American Indian Parents. Journal of Racial and Ethnic Health Disparities, 5, 1254-1263. https://doi.org/10.1007/s40615-018-0472-3.

Zawacki-Richter, O., Marín, V., 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. https://doi.org/10.1186/s41239-019-0171-0.

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Published

2025-01-21

How to Cite

Guipitacio, J. A. D., Aleman, A. V. B., Bonsubre, C. M., Galleto, J. M. T., Tapere, B. N. B., Caballo, J. H., & Caangay, R. B. (2025). The Nexus Between AI Self-Efficacy and Attitude Towards AI of University Students in Davao City as Moderated by Sex. American Journal of Smart Technology and Solutions, 4(1), 1–7. https://doi.org/10.54536/ajsts.v4i1.3788