Exploring BSIT Students Experiences and Perceptions on CHATGPT as a Tool for Software Development

Authors

  • Marylyn E. Tizon Southern Leyte State University -Tomas Oppus , Southern Leyte, Philippines
  • Efren I. Balaba Southern Leyte State University -Tomas Oppus , Southern Leyte, Philippines

Keywords:

Software Development, Educational Technology, ChatGPT

Abstract

This study investigates the attitudes and experiences of Bachelor of Science in Information Technology (BSIT) students about the use of ChatGPT as an aid tool in software development. With the rapid advancement of artificial intelligence (AI) in learning, particularly programming and software subjects, the study aims to determine how students use ChatGPT in their learning processes. With a descriptive qualitative research design guided by the Input-Process-Output (IPO) model, researchers gathered the data from 30 purposively sampled BSIT students using structured questionnaires. Results indicate that students primarily utilize ChatGPT to learn about programming concepts, debugging, and generating or optimizing code. While most acknowledged its usefulness and constructive contribution to learning, issues such as providing incorrect answers, failing to offer sufficient detail in explanations, and the risk of overreliance were also cited. Most participants favored integrating ChatGPT into the curriculum with proper instructor guidance and ethical instruction. This study offers practical contributions for teachers, developers, and institutions to integrate AI responsibly into IT education to enhance students' learning without sacrificing their critical and independent problem-solving skills

References

Ahmad, M., Smith, J., & Khan, R. (2023). The impact of AI-powered coding assistants on software development productivity. Journal of AI and Software Engineering, 15(2), 45–62.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., ... & Zimmermann, T. (2019). Software engineering for machine learning: A case study. Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice, 291–300.

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165.

Clark, C., & Etzioni, O. (2016). My computer is an honor student–but how intelligent is it?

Standardized tests as a measure of AI. AI Magazine, 37(1), 5–12.

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

Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30, 681–694.

Hendrycks, D., & Mazeika, M. (2022). Measuring mathematical problem-solving with the MATH dataset. arXiv preprint arXiv:2103.03874. https://arxiv.org/abs/2103.03874

Johnson, L., & Lee, C. (2023). Artificial intelligence in computer science education:

Adouble-edged sword? Computers & Learning, 12(3), 112–129.

Luxton, D. D. (2016). Artificial intelligence in psychological practice: Current and future applications and implications. Professional Psychology: Research and Practice, 47(3), 147–153.

Mason, M. (2010). Sample size and saturation in PhD studies using qualitative interviews. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 11(3), 145-160

Nguyen, H., & Rose, C. P. (2021). An exploratory study of using AI-powered writing assistants in higher education. Journal of Educational Computing Research, 59(5), 867–891.

Rowley, J. (2014). Designing and using research questionnaires. Management Research Review, 37(3), 308–330.

Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson Education Limited.

Salomon, G. (1993). No distribution without individuals’ cognition: A dynamic interactional view. In Distributed Cognitions: Psychological and Educational Considerations Cambridge University Press.

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Van Esch, P., & Black, J. S. (2019). Factors that influence new generation candidates to engage with and complete digital, AI-enabled recruiting. Business Horizons, 62(6), 729–739. https://doi.org/10.1016/j.bushor.2019.07.003

Woolf, B. P. (2020). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.

Downloads

Published

2025-06-30