Critical Review of Utilisation of Artificial Intelligence Technology to Improve Programming Skills of Students of Computer Science and Information Systems Department
Introduction
In the field of higher education, the emergence of intelligent learning technologies due to Artificial Intelligence (AI) has brought significant changes. The application of AI technology in the domain of computing has received much focus lately due to AI technology used in tools like ChatGPT and GitHub Copilot. These tools offer personalised feedback, debugging and help in coding. Thus, there is AI-based Programming Learning.
The paper entitled “The Role of AI Technology in Programming Education of University Students: An Empirical Study” written by Othman in 2026 examines the effect of AI-based learning on the programming skills of university students in Libya. By adopting a mixed-method research design, the research assesses the effect of AI on debugging skill, efficiency of coding, problem solving as well as the danger of over-reliance on AI.
Summary of the article
The paper investigates the ways in which AI technology has improved the programming skills of students through AI Technology in Education. According to the authors, the conventional methods of programming education make it hard for learners to acquire logical thinking skills and debugging skills because of the use of AI tools like ChatGPT and GitHub Copilot.
The method of research used in this paper was a combination of different methodologies. Data were collected using 200 undergraduate Computer Science students from three Libyan universities and 10 programming lecturers. The statistical methods employed were Pearson correlation, regression analysis, and independent-samples t-test.
The findings reveal that the participants who frequently utilised AI Technology performed better in terms of debugging speed, efficiency of writing code, and problem-solving than the ones who infrequently utilised AI. From the instructor’s perspective, there was an improvement in programming performance as well as coding efficiency of the participants as a result of AI-assisted learning.
However, it has been revealed through this study that overdependence on AI has lowered the conceptual knowledge of the participants since they were not able to explain the reasoning behind the code written by them. The researchers believe that AI for Programming Improvement is successful only when AI supports programming instruction.
Critique
Significance and contribution of the field
Among the strengths of this article is the addition to AI Technology in Education, which is achieved through the evaluation of both the positive and negative aspects of using AI for Programming Skills Improvement. Unlike most other research studies, which concentrate only on technical performance, the authors include the perspectives of instructors as well.
The results of this research corroborate those by Vaithilingam et al. (2022), who claimed that AI-based coding assistants boost programming productivity and assist in learning at the same time. In a similar way, the study by Becker et al. (2023) showed that conversational AI increases students’ comprehension of programming.
Moreover, the current research is like the ideas proposed by Kasneci et al. (2023), who state that through generative AI, it is possible to personalise learning and engage students more effectively. The two sources support the contribution of the article to AI in Computer Science Education and demonstrate its educational significance.
Still, the section about the future impact of excessive dependency on artificial intelligence is not thorough enough. Although the researchers recognise the negative effect of reliance on technology, an extensive comparison with the findings of other empirical research would add to the quality of the paper.
Methodology and research design
An important strength of this research article is that it has used a mixed-method research methodology involving quantitative surveys along with qualitative interviews to ensure that it gives us an all-around view of how AI is impacting programming abilities. In this research, data have been collected from 200 undergraduates along with ten lecturers, and hence the results are more relevant than research based solely on surveys.
The study uses both Pearson correlation, regression analysis, and independent samples t-tests. Use of various statistical tests helps the researchers see many links between AI use and programming success. This combined approach to methodology, supported by Creswell & Creswell (2018), who have long recommended similar kinds of combined methods for use in educational research, produces research results of more substance.
But the study has some limitations. First, convenience sampling was utilised, which is restricted to three Libyan universities. Thus, results do not apply to other higher educational institutions. Second, programming performance was estimated primarily based on students’ self-perceptions, not actual programming proficiency tests. As stated by Shadiev and Wang (2022), “to more conclusively determine the effectiveness of the AI technology for Programming Education, the surveyed students’ actual performance on their coding should also be measured.”
Theoretical and Interdisciplinary Analysis
This research has effectively integrated topics from the fields of Artificial Intelligence, Computer Science Education, and Educational Technology to provide insight into how AI enables programming education. The integration is achieved through the discussion of technologies including ChatGPT, GitHub Copilot, and CodeT5, which illustrate how AI-Based Programming Learning can be used to solve problems.
The results are also consistent with the ideas expressed by Kasneci et al. (2023), who note that generative AI allows for the personalisation of the learning process and greater student engagement in higher education. In turn, Luckin and Cukurova (2019) believe that AI is to make students’ learning experience better but not replace their ability to independently solve problems. Both these approaches support the focus of the article on AI application.
However, it should be noted that the theoretical part of the paper is largely descriptive in nature. While the authors describe the advantages of AI applications, they do not discuss any well-established theoretical approaches such as Constructivism or the Technology Acceptance Model (TAM).
The article also focuses on the significance of AI in improving programming skills because of the link between theory and practical aspects of programming. It agrees with Zawacki-Richter et al. (2019), who claim that the use of AI technologies allows for personalised and adaptive learning processes. Nevertheless, the research pays little attention to the long-term effect of AI on computational thinking and algorithm design.
Ethical Considerations
The author has rightly raised the issue of ethics related to AI programming assistance, which includes the dependency of students on AI systems and loss of conceptual knowledge. Moreover, the role of school policies and teachers’ support is also mentioned to guide students about using AI properly.
The ethical aspect, on the other hand, could have been addressed in more detail. Such matters as academic integrity, algorithmic bias, data protection, and transparency are only briefly covered, but they become more important as AI is applied in Computer Science Education. As UNESCO states in the recommendations regarding the ethical application of AI in the education sector, “Educational AI systems should be designed to support fairness, transparency and responsibility, and to protect the privacy of learners.
Another aspect that could be covered in this article is the proper utilisation of AI coding in academic evaluations. With the rise of AI coding platforms, it is becoming increasingly difficult to differentiate between students’ genuine answers and their answers created with the help of AI. According to Cotton et al., there should be an established institutional policy regarding the proper use of AI technology and an assessment approach that assesses students’ conceptual understanding.
Writing Style and Structure
However, the article is logically structured, starting from the introduction and literature review to methodology, results, discussion, recommendation, and conclusion. The use of tables and charts makes the presentation of the results easy to comprehend for the readers. The language used is clear enough for researchers and teachers to understand the discussion presented in this article.
In addition to this, there were a few parts in the discussion that were only descriptive in nature and could have been compared to previous empirical works in a more critical manner. While the researchers have provided relevant findings in their paper, it would have been better had they gone into more details of conflicting views as well as alternatives to improve the academic value of their work.
Conclusion
Overall, the article is one of the useful contributions to the rapidly developing area of AI technology in education, as it provides an analysis of the impact of artificial intelligence assisted learning technologies on the programming skills of university students. The findings of the article are helpful to educators who want to incorporate AI into programming curricula. The article also emphasizes that the use of AI technology can be efficient in debugging, problem solving, and improving code efficiency.
However, there are some shortcomings associated with the study such as having a small sample, use of self-reporting technique and lack of theory/ethical discussions. Further comparisons with other empirical studies done internationally would have added more to the strength of the research findings. However, the research paper makes an important contribution towards supporting AI-Based Learning and serves as a good basis for further research on Programming Skills Development Using AI.
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Reference
- Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2023.2190148
- Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications.
- Becker, B.A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J. and Santos, E.A. (2023) Programming Is Hard-Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, Toronto, 15-18 March 2023, 500-506. https://doi.org/10.1145/3545945.3569759
- 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., Pfeiffer, F., 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, 102274.
- Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences perspective. British Journal of Educational Technology, 50(6), 2824–2838.
- Shadiev, R., & Wang, X. (2022). A review of research on technology-supported programming education. Education and Information Technologies, 27(8), 11249–11279.
- (2021). Recommendation on the Ethics of Artificial Intelligence. UNESCO.
- Vaithilingam, S., Zhang, T., & Yew, W. S. (2022). Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. CHI Conference on Human Factors in Computing Systems Extended Abstracts.
- 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.