Psychometric Evidence and Associative Analysis of Dimensions of Familiarity, Frequency of Use and Satisfaction with AI Tools in University Research Training
Keywords:
AI; AI literacy; higher education; psychometric validation; research trainingAbstract
The study examined the psychometric properties of the instrument and the associations between the dimensions of familiarity, frequency of use, and perceived satisfaction regarding the use of artificial intelligence (AI) tools in university research training. A quantitative approach was adopted, with a non-experimental, cross-sectional, and descriptive-correlational design, involving a final sample of 105 students. Data were collected using a 30-item questionnaire, and the analysis included exploratory factor analysis to assess construct validity, internal consistency estimation (Cronbach’s ? and McDonald’s ?), and bivariate association analysis through Spearman correlations and chi-square tests. The results showed a predominance of low levels across the three dimensions, indicating a limited integration of these technological tools in university research training processes. The factor analysis confirmed a three-dimensional structure consistent with the theoretical model, explaining 72.8% of the variance, with high reliability (? = .974; ? = .974). Likewise, positive and statistically significant associations were found between the dimensions, with a particularly strong relationship between frequency of use and satisfaction (? = .925; p < .001). Overall, the findings suggest that the adoption of AI is still at an early stage, which highlights the need to strengthen AI literacy strategies that integrate technical, ethical, and methodological dimensions in higher education contexts.
https://doi.org/10.26803/ijlter.25.5.34
References
Almassaad, A., Alajlan, H., & Alebaikan, R. (2024). Student perceptions of generative artificial intelligence: Investigating utilization, benefits, and challenges in higher education. Systems, 12(10), Article 385. https://doi.org/10.3390/systems12100385
Barrett, A., & Pack, A. (2023). Not quite eye to A.I.: Student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education, 20, Article 59. https://doi.org/10.1186/s41239-023-00427-0
Bond, M., Khosravi, H., de Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, Article 4. https://doi.org/10.1186/s41239-023-00436-z
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. https://doi.org/10.1007/s11528-022-00715-y
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8
Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(1), Article 7. https://doi.org/10.7275/jyj1-4868
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272
Feng, J., Yu, B., Tan, W. H., Dai, Z., & Li, Z. (2025). Key factors influencing educational technology adoption in higher education: A systematic review. PLOS Digital Health, 4(4), Article e0000764. https://doi.org/10.1371/journal.pdig.0000764
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hernández-Sampieri, R., Fernández-Collado, C., & Baptista-Lucio, P. (2014). Metodología de la investigación [Methodology of investigation] (6th ed.). McGraw-Hill.
Inamorato dos Santos, A., Chinkes, E., Carvalho, M. A. G., Solórzano, C. M. V., & Marroni, L. S. (2023). The digital competence of academics in higher education: Is the glass half empty or half full? International Journal of Educational Technology in Higher Education, 20, Article 9. https://doi.org/10.1186/s41239-022-00376-0
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., Pfeffer, J., 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, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), Article 410. https://doi.org/10.3390/educsci13040410
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). https://doi.org/10.1145/3313831.3376727
Mamo, Y., Crompton, H., Burke, D., & Nickel, C. (2024). Higher education faculty perceptions of ChatGPT and the influencing factors: A sentiment analysis of X. TechTrends, 68(3), 520?534. https://doi.org/10.1007/s11528-024-00954-1
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers & Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
OpenAI. (2024). ChatGPT [Modelo de lenguaje]. https://chat.openai.com/
Redecker, C., & Punie, Y. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union. https://doi.org/10.2760/159770
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864
Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian Journal of Dermatology, 61(3), 261–264. https://doi.org/10.4103/0019-5154.182410
Sullivan, G. M., & Artino, A. R. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5(4), 541–542. https://doi.org/10.4300/JGME-5-4-18
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, Article 15. https://doi.org/10.1186/s40561-023-00237-x
UNESCO. (2023). Guidance for generative AI in education and research. https://unesdoc.unesco.org/ark:/48223/pf0000386693
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on AI applications in higher education. International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Walther Hernan Casimiro Urcos, Consuelo Nora Casimiro Urcos, Javier Francisco Casimiro Urcos, Roger Octavio Quinteros Osorio
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published by IJLTER are licensed under a Creative Commons Attribution Non-Commercial No-Derivatives 4.0 International License (CCBY-NC-ND4.0).
