“The Impact of Artificial Intelligence Usage on Skill Retention of Third-Year Computer Engineering Students at Bulacan State University”

The researchers investigate the possible impact of Artificial Intelligence (AI) towards the skill retention of third-year students in Bulacan State University Main Campus. The researchers utilized online survey questionnaires application to ensure proper dissemination and a steadfast response from the students, Google Forms was used to efficiently collect data from third-year Computer Engineering students. The questionnaire was disseminated through an online messaging app where the respondents can easily open and access the survey form, the researchers gathered a total of ninety two (92) responses from the third-year computer engineering students. Five-point likert scale was adopted to weigh in the respondents’ response towards the questionnaire provided. Through the data gathered from the respondents, researchers were able to collect relevant information and analyze data in order to draw conclusions. The result of the survey exhibits a neutral result in their skill retention towards problem-solving and programming logic, suggesting that their AI usage has no significant effect in recalling basic programming logics and problem solving skills despite a large number of responses indicating that they do not have the capability of solving engineering problems without the help of AI tools during the process of problem-solving and having a difficulty in coding and debugging programming code without the help of AI tools. Above all, these results imply that students were able to maintain their problem solving abilities and programming logic skills amidst the growing popularity and a threatening advancements of AI, while those who depend most in AI tend to lose fundamental engineering skills.

Artificial Intelligence, Computer Engineering, skill retention

1. Copeland, B. J. (1998, July 20). Artificial intelligence (AI). Encyclopedia Britannica. https://www.britannica.com/technology/artificial-intelligence [Google Scholar] [Crossref]

2. Creswell, J. W., & Creswell, J. D. (2022). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE Publications. [Google Scholar] [Crossref]

3. Etikan, I., & Bala, K. (2021). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 10(1), 5–7. https://doi.org/10.15406/bbij.2021.10.00305 [Google Scholar] [Crossref]

4. Field, A. (2022). Discovering statistics using IBM SPSS statistics (6th ed.). SAGE Publications. [Google Scholar] [Crossref]

5. Foundational learning. (2025, May 16). World Bank. https://www.worldbank.org/en/topic/education/brief/foundational-learning [Google Scholar] [Crossref]

6. Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2023). How to design and evaluate research in education (11th ed.). McGraw-Hill Education. [Google Scholar] [Crossref]

7. Hernandez, H. E., Canlas, R. B., & Nacianceno, M. C., … & Miranda, J. P. P. (2025). Trust, Usefulness, and Dependency on AI in Programming: A Hierarchical Clustering Approach. International Journal of Computing and Digital Systems, 15(2), 45–58. [Google Scholar] [Crossref]

8. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. [Google Scholar] [Crossref]

9. Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. [Google Scholar] [Crossref]

10. Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2021). Likert scale: Explored and explained. British Journal of Applied Science & Technology, 7(4), 396–403. [Google Scholar] [Crossref]

11. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274 [Google Scholar] [Crossref]

12. Navarra, L. W. M., Marcelo, H. L., & Borromeo, J. R. (2025). Quantifying the influence of artificial intelligence dependency on computer engineering students in Bulacan State University. International Journal of Research and Innovation in Social Science, 9(10), 541–550. https://doi.org/10.47772/IJRISS.2025.910000541 [Google Scholar] [Crossref]

13. Ortega-Dela Cruz, R., & Dela Cruz, R. (2025). AI Integration in IT Education: Challenges, Opportunities, and Future Directions. Asia Pacific Journal of Education, Arts and Sciences, 10(2), 22–31. [Google Scholar] [Crossref]

14. Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151, 103862. https://doi.org/10.1016/j.compedu.2020.103862 [Google Scholar] [Crossref]

15. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. [Google Scholar] [Crossref]

• The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction
• ESPYREAL: A Mobile Based Multi-Currency Identifier for Visually Impaired Individuals Using Convolutional Neural Network
• Comparative Analysis of AI-Driven IoT-Based Smart Agriculture Platforms with Blockchain-Enabled Marketplaces
• AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment
• SEA-TALK: An AI-Powered Voice Translator and Southeast Asian Dialects Recognition