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The Mediating Effect Of Learning Motivation On Attitudes Towards Using ChatGPT (ATUC) As Educational Resource And Academic Performance: Evidence From A State University In Iloilo, Philippines
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The use of AI chatbots, such as ChatGPT, in education has sparked significant interest, primarily due to their potential to enhance educational outcomes significantly. This study, employing a causal-predictive methodology, delved into the relationship between attitudes toward ChatGPT (ATUC), academic performance, and the mediating role of learning motivation among tertiary students at a state university in Iloilo, Philippines. Data were collected via an online survey that included personal demographics, adopted questionnaires for ATUC (Ajlouni et al., 2023) and learn- ing motivation (Caratiquit & Caratiquit, 2023), as well as an adapted questionnaire for academic performance (Maqableh et al., 2021). The study, which included 205 purposively selected respondents who met the inclusion criteria, utilised covariance-based structural equation modelling (CB-SEM) for data examination. The bootstrapping results demonstrated a positive relationship, with ATUC predicting learning motivation, which predicted academic performance, thereby encouraging the potential of AI chatbots in education. Future research should employ longitudinal and mixed meth- ods to investigate ChatGPT’s long-term impacts while minimising bias. It should be integrated into higher education institutions’ (HEIs) strategies, emphasising ethics, academic integrity, the advantages and disadvantages of AI, and its adaptability for faculty and students.
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Learning motivation strongly influences the association between GenAI attitudes and academic performance.
“Amorin and Gallaron (2025) found that learning motivation strongly influences the association between GenAI attitudes and academic performance, suggesting that positive AI attitudes only improve performance when accompanied with intrinsic motivation.”
From Beyond Access: Socioeconomic, Academic and Disciplinary Determinants of Generative AI Use in Higher Education (2026) · cites this paper as Amorin and Gallaron (2025)
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Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 1×
“It provides instructors with prepara - tion, resources, and feedback to increase engagement (Kasneci et al., 2023).”
Kyriazos, T. A. (2018) · Psychology · doi:10.4236/psych.2018.98126 · Cited by 1,882
Artificial intelligence in education: Addressing ethical challenges in K-12 settings
Akgun, S., & Greenhow, C. (2022) · AI and Ethics · doi:10.1007/s43681-021-00096-7 · Cited by 1,473 · cited 1×
“AI performs human-like activities such as thinking, understanding, and interacting to enable systems to function like humans (Akgun & Greenhow, 2021).”
Ethics of AI in Education: Towards a Community-Wide Framework
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., et al. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00239-1 · Cited by 1,437 · cited 1×
“While AI has made significant strides in education, issues of data security and privacy must be addressed to maintain academic integrity (Holmes et al., 2021).”
ChatGPT is fun, but not an author
Thorp, H. H. (2023) · Science · doi:10.1126/science.adg7879 · Cited by 1,437
ChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · Applied Sciences · doi:10.3390/app13095783 · Cited by 1,080 · cited 3×
“Rahman & Watanobe (2023) emphasised ChatGPT’s significance in encouraging users through materi - als, input, and distinctive features, leading to increased involvement and achievement.”
ChatGPT in higher education: considerations for academic integrity and student learning
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.17 · Cited by 853
What ChatGPT means for universities: Perceptions of scholars and students
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.22 · Cited by 431 · cited 2×
“…contradicting previous research suggesting a direct relationship between ATUC and performance (Babalola, Ahmad & Tafida, 2024; Jowarder, 2023 ; Kefalaki, Diamantidaki, & Rudolph, 2022; Lee & Yeo, 2022; Wardat et al., 2023), as well as knowledge acquisition and retention (Firat, 2023; Lee, 2023 ).”
Impact of ChatGPT on Learning Motivation: Teachers and Students' Voices
Ali, J. K. M., Shamsan, M. A. A., Hezam, T. A., & Mohammed, A. A. Q. (2023) · Journal of English Studies in Arabia Felix · doi:10.56540/jesaf.v2i1.51 · Cited by 335
Deng, X., & Yu, Z. (2023) · Sustainability · doi:10.3390/su15042940 · Cited by 298 · cited 2×
“Deng and Yu (2023) demonstrated increased motivation and participation when using ChatGPT as an instructional tool, espe - cially in virtual learning environments.”
Park, J., & Woo, S. E. (2022) · The Journal of Psychology · doi:10.1080/00223980.2021.2012109 · Cited by 243
Developing an AI-based chatbot for practicing responsive teaching in mathematics
Lee, D., & Yeo, S. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104646 · Cited by 210
Acosta-Enriquez, B. G., Arbulú Ballesteros, M. A., Huamaní Jordan, O., López Roca, C., & Saavedra Tirado, K. (2024) · BMC Psychology · doi:10.1186/s40359-024-01764-z · Cited by 164 · cited 1×
“Attitudes towards technological development stem from an individual’s ideas and influence their behaviour (Fishbein & Ajzen, 1977, in Acosta-Enriquez et al., 2024).”
Liu, Y., Hau, K., Liu, H., Wu, J., Wang, X., & Zheng, X. (2020) · Journal of Personality · doi:10.1111/jopy.12512 · Cited by 139
Students’ attitudes towards using ChatGPT as a learning tool: the case of the University of Jordan.
O. Ajlouni, A., Abd-Alkareem Wahba, F., & Salem Almahaireh, A. (2023) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v17i18.41753 · Cited by 116
Liang, Y., Meng, F., Zhang, Y., Chen, Y., Xu, J., & Zhou, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17575 · Cited by 63
Jowarder, M. I. (2023) · Indonesian Journal of Innovation and Applied Sciences (IJIAS) · doi:10.47540/ijias.v3i2.878 · Cited by 36
Flagged first, then by citations
Research works printed but not linked: 55
- Agapito, J. J. (2023). User perceptions and privacy concerns related to using ChatGPT in conversational AI systems. Social Science Research Network. https://doi.org/10.2139/ssrn.4440366 · DOI
- Ahmadi, A. (2023). ChatGPT: Exploring the threats and opportunities of artificial intelligence in the age of Chatbots. Asian Journal of Computer Science and Technology, 12(1), 25–30. https://doi.org/10.51983/ajcst-2023.12.1.3567 · DOI
- Aithal, S., & Aithal, P. S. (2023). Effects of AI-Based ChatGPT on higher education libraries. International Journal of Management, Technology, and Social Science, 8(2), 95–108. https://doi.org/10.47992/ijmts.2581.6012.0272 · DOI
- Ajlouni, A., & Rawadieh, S. (2022). Technophobia and Technophilia among undergraduates: Cross-national research in Jordan, Qatar, and Egypt. Journal of Social Studies Education Research, 13(4), 24–25. https://files.eric.ed.gov/fulltext/EJ1375664.pdf
- Ali, J. K. M., & Bin-Hady, W. R. A. (2019). A Study of EFL Students’ Attitudes, Motivation and Anxiety towards WhatsApp as a Language Learning Tool. Arab World English Journal, 5, 289–298. https://doi.org/10.24093/awej/call5.19 · DOI
- Aljanabi, M., Yaseen, M., Ali, A. H., & Abed, S. A. (2023). ChatGPT: Open possibilities. Iraqi Journal for computer Science and Mathematics. https://doi.org/10.52866/ijcsm.2023.01.01.0018 · DOI · Paper page
- Alotaibi, R., Al-Shehri, S., Al-Harbi, R., & Al-Mutairi, M. (2020). Enhancing learning outcomes through chatbot technology in computer science education. Education and Information Technologies, 25(6), 5167–5183. https://doi.org/10.1007/s10639-020-10182-8 · DOI
- Asare, M. (2015). Using the theory of planned behavior to determine the condom use behavior among college students. American Journal of Health Studies, 30(1), 43–50. https://doi.org/10.47779/ajhs.2015.168 · DOI
- Atiyah, A., Mohamad, A., & Zunaidah. (2021). The effect of e-learning and learning motivation on academic stress with self-efficacy as a mediation variable during the COVID-19 pandemic. Russian Journal of Agricultural and Socio-Economic Sciences, 10(118). https://rjoas.com/issue-2021-10/article_02.pdf · DOI
- Babalola, V. T., Ahmad, N. S. S., & Tafida, N. H. S. (2024). CHATGPT in Organic Chemistry Classrooms: Analyzing the impacts of social environment on students’ interest, critical thinking and academic achievement. International Journal of Education and Teaching Zone, 3(1), 60–72. https://doi.org/10.57092/ijetz.v3i1.155 · DOI
- Bhat, A. Z., Ahmed, I., Kameswari, L., & Khan, M. S. (2023). A Game Based Innovative teaching and learning environment to enhance progression and performance of students. SHS Web of Conferences. 156, 01001. https://doi.org/10.1051/shsconf/202315601001 · DOI
- Brenner, C. A. (2022). Self-regulated learning, self-determination theory and teacher candidates’ development of competency-based teaching practices. Smart Learning Environments, 9(1). https://doi.org/10.1186/s40561-021-00184-5 · DOI
- Caratiquit, K. D., & Caratiquit, L. J. C. (2023). ChatGPT as an academic support tool on the academic performance among students: The mediating role of learning motivation. Journal of Social, Humanity, and Education, 4(1), 21–33. https://doi.org/10.35912/jshe.v4i1.1558 · DOI
- Chin, W., Cheah, J., Liu, Y., Ting, H., Lim, X., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management & Data Systems, 120(12), 2161–2209. https://doi.org/10.1108/imds-10-2019-0529 · DOI
- Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with artificial intelligence (AI) based chatbot. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2023.2172044 · DOI · Paper page
- Dasborough, M. T. (2023). Awe-inspiring advancements in AI: The impact of ChatGPT on the field of organizational behavior. Journal of Organizational Behavior, 44(2), 177–179. https://doi.org/10.1002/ job.2695 · DOI
- Ebadi, S., & Amini, A. (2022). Examining the roles of social presence and human-likeness on Iranian EFL learners’ motivation using artificial intelligence technology: A case of CSIEC chatbot. Interactive Learning Environments, 32(2), 1–19. https://doi.org/10.1080/10494820.2022.2096638 · DOI
- Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. · DOI · Paper page
- Annual Review of Psychology, 53(1), 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153 · DOI · Paper page
- Estrellado, C. J. P., & Miranda, J. C. (2023). Artificial intelligence in the Philippine educational context: Circumspection and future inquiries. International Journal of Scientific and Research Publications, 13(5), 16–22. https://doi.org/10.29322/ijsrp.13.05.2023.p13704 · DOI
- Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312 · DOI · Paper page
- George, D., & Mallery, P. (2019). IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference (16th ed.). Routledge. https://doi.org/10.4324/9780429056765 · DOI · Paper page
- Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. Bench Council Transactions on Benchmarks, Standards and Evaluations, 2(4), 100089. https://doi.org/10.1016/j.tbench.2023.100089 · DOI · Paper page
- Jishnu, D., Srinivasan, M., Dhanunjay, G. S., & Shamala, R. (2023). Unveiling student motivations: A study of chatgpt usage in education. ShodhKosh: Journal of Visual and Performing Arts, 4(2), 65–73. https://doi.org/10.29121/shodhkosh.v4.i2.2023.503 · DOI
- Juhász, L., Mooney, P., Hochmair, H. H., & Guan, B. (2023). ChatGPT as a mapping assistant: A novel method to enrich maps with generative AI and content derived from street-level photographs. (pp. 1–12). UC Santa Barbara: Center for Spatial Studies. Retrieved from http://dx.doi.org/10.25436/E2ZW27 · DOI
- Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004 · DOI · Paper page
- Kapse, M., Bala, A., & Sharma, V. (2022). Gender and green IT practice— Implications from the ABC model of attitude. International Journal of Business Innovation and Research, 29(3), 308–325. https://doi.org/10.1504/IJBIR.2022.127178 · DOI
- Kefalaki, M., Diamantidaki, F., & Rudolph, J. (2022). Editorial 5(SI1): Technology and education: Innovation or hindrance? Journal of Applied Learning and Teaching, 5(Special Issue). 6–11. https://doi.org/10.37074/jalt.2022.5.s1.1 · DOI
- Khaneghahi, S., Nasripour, F., & MahmoudZehi, M. A. (2022). Investigating the relationship between e-learning and mobile learning on students’ academic self- handicapping during the outbreak of COVID-19. Journal of Social, Humanity and Education, 2(3), 269–281. https://doi.org/10.35912/jshe.v2i3.1051 · DOI
- King, T., & Dennis, C. (2006). Unethical consumers. Qualitative Market Research an International Journal, 9(3), 282–296. https://doi.org/10.1108/13522750610671699 · DOI
- Kitamura, F. C. (2023). ChatGPT is shaping the future of medical writing but still requires human judgment. Radiology, 307(2), e230171. https://doi.org/10.1148/RADIOL.230171 · DOI · Paper page
- Klassen, R. M., Bong, M., Usher, E. L., Chong, W. H., Huan, V. S., Wong, I. Y., & Georgiou, T. (2009). Exploring the validity of a teachers’ self-efficacy scale in five countries. Contemporary Educational Psychology, 34(1), 67–76. https://doi.org/10.1016/j.cedpsych.2008.08.001 · DOI
- Lahey, B. B., McNealy, K., Knodt, A., Zald, D. H., Sporns, O., Manuck, S. B., Flory, J. D., Applegate, B., Rathouz, P. J., & Hariri, A. R. (2012). Using confirmatory factor analysis to measure contemporaneous activation of defined neuronal networks in functional magnetic resonance imaging. NeuroImage, 60(4), 1982–1991. https://doi.org/10.1016/j.neuroimage.2012.02.002 · DOI
- Lee, H. (2023). The rise of ChatGPT: Exploring its potential in medical education. Anatomical Sciences Education, 17(9), 1779. https://doi.org/10.1002/ase.2270 · DOI
- Lu, C., & Cutumisu, M. (2022). Online engagement and performance on formative assessments mediate the relationship between attendance and course performance. International Journal of Educational Technology in Higher Education, 19(2). 1–23. https://doi.org/10.1186/s41239-021-00307-5 · DOI
- Maqableh, M., Jaradat, M., & Azzam, A. (2021). Exploring the determinants of students’ academic performance at university level: The mediating role of internet usage continuance intention. Education and Information Technologies, 26(4), 4003–4025. https://doi.org/10.1007/s10639-021-10453-y · DOI
- Menon, S., & Suresh, M. (2022). Enablers of technology agility in higher education. The International Journal of Information and Learning Technology, 39(2), 166–196. https://doi.org/10.1108/ijilt-07-2021-0107 · DOI
- Mhlanga, D. (2023). The value of open AI and Chat GPT for the current learning environments and the potential future uses. Social Science Research Network. https://doi.org/10.2139/ssrn.4439267 · DOI
- Moralista, R., & Oducado, R. (2020). Faculty perception toward online education in a state college in the Philippines during the coronavirus disease 19 (COVID-19) pandemic. Universal Journal of Educational Research, 8(10), 4736–4742. https://doi.org/10.13189/ujer.2020.081044 · DOI
- Nulty, D.D. (2008). The adequacy of response rates to online and paper surveys: What can be done? Assessment & Evaluation in Higher Education, 33(3), 301–314. https://doi.org/10.1080/02602930701293231 · DOI · Paper page
- Okuyama, K., & Suzuki, K. (2023). Correlators of double scaled SYK at one-loop. Journal of High Energy Physics, 2023(5). 1–27. https://doi.org/10.1007/jhep05(2023)117 · DOI
- Porayska-Pomsta, K., & Rajendran, G. (2019). Accountability in human and artificial intelligence Decision-Making as the basis for diversity and educational inclusion. In Perspectives on rethinking and reforming education (pp. 39–59). https://doi.org/10.1007/978-981-13-8161-43 · DOI
- Roumeliotis, K. I., & Tselikas, N. D. (2023). ChatGPT and Open-AI models: A preliminary review. Future Internet, 15(6), 192. https://doi.org/10.3390/fi15060192 · DOI
- 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. https://doi.org/10.1016/j.cedpsych.2020.101860 · DOI · Paper page
- Sanchez-Bolivar, L., & Martinez-Martinez, A. (2022). Factors related to university students’ motivation and instruments for its evaluation: A systematic review. Revista Electrónica Educare, 26(2), 1–22. https://doi.org/10.15359/ree.26-2.26 · DOI
- Sebastian, G. (2023). Privacy and Data protection in ChatGPT and other AI Chatbots: Strategies for Securing User information. International Journal of Security and Privacy in Pervasive Computing, 15(1), 1–14. https://doi.org/10.4018/IJSPPC.325475 · DOI
- Sengupta, S., & Chakraborty, T. (2020). Use of chatbots in higher education: A study of student engagement and satisfaction. Education and Information Technologies, 25(6), 514–5165. https://doi.org/10.1007/s10639-020-10171-x · DOI
- Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and non-experimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422–445. https://psycnet.apa.org/record/2002-11349-006 · DOI · Paper page
- Suharmawan, W. (2023). Pemanfaatan Chat GPT dalam Dunia Pendidikan. Education Journal: Journal Educational Research and Development, 7(2), 158–166. https://doi.org/10.31537/ej.v7i2.1248 · DOI
- Svenningsson, J., Höst, G., Hultén, M., & Hallström, J. (2022). Students’ attitudes toward technology: Exploring the relationship among affective, cognitive, and behavioral components of the attitude construct. International Journal of Technology and Design Education, 32, 1531–1551. https://doi.org/10.1007/s10798-021-09657-7 · DOI · Paper page
- Tanvir, K., Islam, M. S., Sezan, S. B. K., Sanad, Z. A., & Ataur, A. I. (2023). Impact of ChatGPT on Academic Performance among Bangladeshi Undergraduate Students. Deleted Journal, 35, 18–28. https://doi.org/10.55529/ijrise.35.18.28 · DOI
- Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics Science and Technology Education, 19(7), em2286. https://doi.org/10.29333/ejmste/13272 · DOI
- Woodhouse, D., & Charlesworth, A. (2023). Can ChatGPT predict future interest rate decisions? Social Science Research Network, 13. https://doi.org/10.2139/ssrn.4572831 · DOI
- Xiong, J., Hsiang, E., He, Z., Zhan, T., & Wu, S. (2021). Augmented reality and virtual reality displays: Emerging technologies and future perspectives. Light, Science & Applications/Light: Science & Applications, 10(1). https://doi.org/10.1038/s41377-021-00658-8 · DOI
- Zafrullah, Z., Hakim, M. L., & Angga, M. (2023). ChatGPT open AI: Analysis of mathematics education students learning interest. J. Technol. Global, 1(1), 1–10. https://penaeducentre.com/index.php/JTeG
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Metadata
- Authors
- Amorin, R. B., & Gallaron, G. M.
- Affiliations
- West Visayas State University (PH)
- Venue
- MIER Journal of Educational Studies Trends and Practices
- Published
- 2025-05-01
- Pages
- 214-239
- ISSN
- 0976-8203 (ISSN-L)
- 2319-1945
- OpenAlex
- W4410045866
- Cited by
- 3
- References
- 18
- Citations by year
- 2026: 3
- FWCI
- 1.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- MIER Journal of Educational Studies Trends & Practices (publishedVersion, cc-by)
- Funders
- Commission on Higher Education
- University of the Philippines
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- ChatGPT, student motivation, academic performance, mediating effect, artificial intelligence in higher education, university students
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