Perception and uses of AI among students in the field of computing: a descriptive study
DOI:
https://doi.org/10.24310/ijtei.121.2026.22825Keywords:
Higher Education, Generative Artificial Intelligence, Computer Science Students, Academic Uses of AI, Student Perceptions, Spearman CorrelationAbstract
Generative Artificial Intelligence (GAI) tools have become widespread in various educational fields, particularly in higher education. However, further exploration is needed regarding the integration of these models into the academic activities of students in computer science programs. This study aims to conduct a descriptive exploration of the uses of GAI, the frequency of use, the output modalities employed (text, image, and video), and students' perceptions of the benefits and opportunities it offers. A quantitative and descriptive study was conducted using a questionnaire administered to a sample of 190 students from public institutions in the State of Mexico. The instrument covered generative tools in their three modalities and included items for evaluating perceptions. Subsequently, Spearman's rho correlations were performed to identify potential associations between output modalities and perceived benefits and opportunities. The findings show that text-based generative models are widely used for information retrieval, code explanation and generation, and error resolution, contrasting with the other models, which are used less frequently. Regarding perceptions of benefits and opportunities, students expressed an average rating for problem-solving and task automation, and a lower rating for skills development and collaboration. Furthermore, relevant correlations were identified with the opportunity for collaboration: while the association with its use for information retrieval was negative (ρ = -0.313), it was positive with diagram creation (ρ = 0.219). It is concluded that, although Genetic AI has been integrated into academic activities, it is essential to promote collaborative work in its educational practice, as well as ethics and responsibility, to avoid dependence on these tools.
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Agbo, F., Olivia, C., Oguibe, G., Sanusi, I., & Sani, G. (2025). Computing education using generative artificial intelligence tools: A systematic literature review. Computers and Education Open, (9), 100266. https://doi.org/10.1016/j.caeo.2025.100266
Alshamy, A., Al-Harthi, A., & Abdullah, S. (2025). Perceptions of generative AI tools in Higher Education: Insights from Students and Academics at Sultan Qaboos University. Education Sciences, 15(4), 501. https://doi.org/10.3390/educsci15040501
Alvarez, J., Benitez, M., & Hernandez, C. (2025). Generative AI in Engineering and Computing Education: A Scoping Review of Empirical Studies and Educational Practices. IEEE Access, 13, 30789–30810. https://doi.org/10.1109/ACCESS.2025.3541424
Aydin, Ö., & Karaarslan, E. (2023). Is ChatGPT Leading Generative AI? What is Beyond Expectations? APJESS, 11(3), 118-134 https://doi.org/10.21541/apjess.1293702
Baig, M., & Yadegaridehkordi, E. (2025). Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education. International Journal of Educational Technology in Higher Education, 22(1), 1-23. https://doi.org/10.1186/S41239-025-00506-4
Beale, R. (2025). Computer Science Education in the Age of Generative AI. Arxiv: 2507.02183. https://doi.org/10.48550/arXiv.2507.02183
Belkina, M., Daniel, S., Nikolic, S., Haque, R., Lyden, S., Neal, P., Grundy, S., & Hassan, G. (2025). Implementing generative AI (GenAI) in higher education: A systematic review of case studies. Computers and Education: Artificial Intelligence, 8, 100407. https://doi.org/10.1016/J.CAEAI.2025.100407
Cubillos, C., Mellado, R., Cabrera-Paniagua, D., & Urra, E. (2025). Generative Artificial Intelligence in Computer Programming: Does It Enhance Learning, Motivation, and the Learning Environment? IEEE Access, 13, 40438–40455. https://doi.org/10.1109/ACCESS.2025.3532883
Duran, V. (2024). Analyzing teacher candidates’ arguments on AI integration in education via different chatbots. Digital Education Review, 45, 68–83. https://doi.org/10.1344/der.2024.45.68-83
Groothuijsen, S., van den Beemt, A., Remmers, J., & van Meeuwen, L. (2024). AI chatbots in programming education: Students’ use in a scientific computing course and consequences for learning. Computers and Education: Artificial Intelligence, 7, 100290. https://doi.org/10.1016/J.CAEAI.2024.100290
Herman, B. (2015). The Influence of Global Warming Science Views and Sociocultural Factors on Willingness to Mitigate Global Warming. Science Education, 99(1), 1–38. https://doi.org/10.1002/SCE.21136
Jaccheri, L., Pereira, C., & Fast, S. (2020). Gender issues in computer science: Lessons learnt and reflections for the future. En Proceedings - 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 9–16. https://doi.org/10.1109/SYNASC51798.2020.00014
Koohang, A., Sargent, C., & Svanadze, S. (2024). Students’ perceptions of benefits and opportunities of artificial intelligence (AI). Issues in Information Systems, 25(2), 438–450. https://doi.org/10.48009/2_iis_2024_134
Kohen-Vacs, D., Usher, M., & Jansen, M. (2025). Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors. International Journal of Artificial Intelligence in Education, 35, 3166-3184. https://doi.org/10.1007/S40593-025-00496-4
Li, S., Liu, J., & Dong, Q. (2025). Generative artificial intelligence-supported programming education: Effects on learning performance, self-efficacy and processes. Australasian Journal of Educational Technology, 41(3), 1–25. https://doi.org/10.14742/AJET.9932
Lobo, E. (2024). El impacto de la alfabetización digital en la enseñanza y el aprendizaje. Complexus Contable, 1(1), 21-40.
Mar-I, F., Nerisafitra, P., Marianingsih, S., Hanjaya, S., Aisyah, Y., Primatama, D., & Sutaji, D. (2025). Exploring the Use of Generative AI in Software Development: A Preliminary Study. E3S Web of Conferences, 645. https://doi.org/10.1051/E3SCONF/202564504002
Matobobo, C., Ncube, P., Ngesimani, N., Dzvapatsva, G., & Chinhamo, E. (2025). Enhancing Computational Thinking and Problemsolving in Programming Education Through Generative AI: A Scoped Review. IEEE Global Engineering Education Conference (EDUCON). https://doi.org/10.1109/EDUCON62633.2025.11016317
Ng, J., Tong, M., Tsang, E., Chu, K., & Tsang, W. (2025). Exploring Students’ Perceptions and Satisfaction of Using GenAI-ChatGPT Tools for Learning in Higher Education: A Mixed Methods Study. SN Computer Science, 6(5), 1-17. https://doi.org/10.1007/S42979-025-04010-4
Niño-Carrasco, S., Castellanos-Ramírez, J., Perezchica, J., & Sepúlveda Rodríguez, J. A. (2025). Percepciones de estudiantes universitarios sobre los usos de inteligencia artificial en educación. Revista Fuentes, 27(1), 94–106. https://doi.org/10.12795/revistafuentes.2025.26356
Parra, P. (2023). Prototyping with Generative AI. En P. Parra, Creative Prototyping with Generative AI. Design Thinking. (pp.109-143). Apress https://doi.org/10.1007/978-1-4842-9579-3_5
Rodríguez, O., (2013). La evaluación objetiva en ingeniería aportes en procesos de evaluación y mejora curricular. Encuentro Internacional de Educación En Ingeniería, Cartagena. https://acofipapers.org/index.php/eiei/article/view/1457
Scholl, A., Tech, N., Schiffner, D., & Kiesler, N. (2025). Students’ Use of ChatGPT in an Introductory Programming Course: A Deep Dive into Chat Protocols and the Student Perspective. Eleed - e-Learning and Education, 16, 1-31. https://doi.org/10.57813/eleed.v1i16.248
Shankar, S., Pothancheri, G., Sasi, D., & Mishra, S. (2024). Bringing teachers in the loop: Exploring perspectives on integrating generative AI in technology-enhanced learning. International Journal of Artificial Intelligence in Education, 3, 155-180. https://doi.org/10.1007/s40593-024-00428-8
Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial Intelligence: Definition and Background. En: Mission AI. Research for Policy. Springer, Cham. https://doi.org/10.1007/978-3-031-21448-6_2
Sijtsma, K. (2009). On the Use, the Misuse, and the Very Limited Usefulness of Cronbach’s Alpha. Psychometrika, 74(1), 107–120. https://doi.org/10.1007/S11336-008-9101-0
Subramonyam, H., Thakkar, D., Ku, A., Dieber, J., & Sinha, A. (2025). Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams. Conference on Human Factors in Computing Systems – Proceedings, 882, 1-22. https://doi.org/10.1145/3706598.3713166
Taber, K. (2017). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education 48(6), 1273–1296. https://doi.org/10.1007/S11165-016-9602-2
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
Tillmanns, T., Salomão, A., Rudra, S., Weber, P., Dawitz, J., Wiersma, E., Dudenaite, D., & Reynolds, S. (2025). Mapping Tomorrow’s Teaching and Learning Spaces: A Systematic Review on GenAI in Higher Education. Trends in Higher Education, 4(1), 2. https://doi.org/10.3390/HIGHEREDU4010002
Torres-Torres, Y., Román-González, M., & Perez-Gonzalez, J. (2024). Didactic strategies for the education of computational thinking from a gender perspective: A systematic review. European Journal of Education, 59(2), e12640. https://doi.org/10.1111/EJED.12640
Verma, U. (2025). Generative AI in Higher Education: The Students’ Perception (Dissertation). https://www.diva-portal.org/smash/record.jsf?dswid=-9917&pid=diva2%3A1926981
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