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Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning
Open accessAbstract
With the diversification of generative artificial intelligence (AI) applications, the interest in their use in every segment and field of society in recent years has been increasing rapidly. One of these areas is programming learning and program writing processes. One of the generative AI tools used for this purpose is ChatGPT. The use of ChatGPT in program writing processes has become widespread, and this tool has a certain potential in the programming process. However, when the literature is examined, research results related to using ChatGPT for this purpose have yet to be found. The existing literature has a gap that requires exploration. This study aims to analyze the students' perspectives on using ChatGPT in the field of programming and programming learning. The study encompassed a cohort of 41 undergraduate students enrolled in a public university's Computer Technology and Information Systems department. The research was carried out within the scope of the Object-Oriented Programming II course for eight weeks. Throughout the research process, students were given project assignments related to the course every week, and they were asked to use ChatGPT while solving them. The research data was collected using a form consisting of open-ended questions and analyzed through content analysis. The research findings revealed both the advantages and disadvantages of ChatGPT usage, as perceived by the students. The students stated that the main benefits of using ChatGPT in programming learning are providing fast and mostly correct answers to questions, improving thinking skills, facilitating debugging, and increasing self-confidence. On the other hand, the main limitations of using ChatGPT in programming education were getting students used to laziness, being unable to answer some questions, or giving incomplete/incorrect answers, causing professional anxiety in students. Based on the results of the research, it can be said that it would be useful to integrate generative AI tools into programming courses considering the advantages they provide in programming teaching. However, appropriate measures should be taken regarding the limitations it brings. Based on the research findings, several recommendations were proposed regarding the integration of ChatGPT into lessons.
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“Even these studies provide contradictory evidence on whether ChatGPT is helpful or harmful for students.”
From Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students (2024) · cites this paper as Yilmaz & Yilmaz, 2023a
In the citing paper
“Issues related to identity also arise, as reported by Yilmaz and Karaoglan Yilmaz (2023): students lack confidence in their own academic merit when AI handles high-level cognitive processes.”
From Mapping the impact of generative AI in higher education: A scoping review of psychological and equity dimensions. (2026) · cites this paper as Yilmaz and Karaoglan Yilmaz (2023)
Citations
Cites
Qualitative Data Analysis: An Expanded Sourcebook
(1994) · Journal of Environmental Psychology · doi:10.1016/s0272-4944(05)80231-2 · Cited by 48,420
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003
ChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · Applied Sciences · doi:10.3390/app13095783 · Cited by 1,080
Wang, B., Rau, P.-L. P., & Yuan, T. (2023) · Behaviour & Information Technology · doi:10.1080/0144929x.2022.2072768 · Cited by 1,036
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
Reynolds, L., & McDonell, K. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451760 · Cited by 952
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100147 · Cited by 745
Artificial intelligence literacy in higher and adult education: A scoping literature review
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100101 · Cited by 658
Students’ Misconceptions and Other Difficulties in Introductory Programming: A Literature Review
Qian, Y., & Lehman, J. (2018) · ACM Transactions on Computing Education · doi:10.1145/3077618 · Cited by 529
A large scale study of programming languages and code quality in github
Ray, B., Posnett, D., Filkov, V., & Dévanbu, P. (2014) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/2635868.2635922 · Cited by 398
Use chat gpt to solve programming bugs
Surameery, N. M. S., & Shakor, M. Y. (2023) · International Journal of Information technology and Computer Engineering · doi:10.55529/ijitc.31.17.22 · Cited by 382
Kong, S.-C., Man-Yin Cheung, W., & Zhang, G. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100026 · Cited by 332
Computational thinking in programming with Scratch in primary schools: A systematic review
Fagerlund, J., Häkkinen, P., Vesisenaho, M., & Viiri, J. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22255 · Cited by 307
Nouri, J., Zhang, L., Mannila, L., & Norén, E. (2019) · Education Inquiry · doi:10.1080/20004508.2019.1627844 · Cited by 304
Tikva, C., & Tambouris, E. (2021) · Computers & Education · doi:10.1016/j.compedu.2020.104083 · Cited by 296
Supporting the understanding and comparison of low-code development platforms
Sahay, A., Indamutsa, A., Di Ruscio, D., & Pierantonio, A. (2020) · 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa51224.2020.00036 · Cited by 270
29th International Conference on Software Engineering (ICSE'07)
(2007) · doi:10.1109/icse10853.2007 · Cited by 259
Computational thinking development through creative programming in higher education
Romero, M., Lepage, A., & Lille, B. (2017) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-017-0080-z · Cited by 250
Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
Kitamura, Y., Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Japan, C. '. C. C. O. H. F. I. C. S. 2. Y., Association for Computing Machinery-Digital Library, ACM Special Interest Group on Computer-Human Interaction, & SIGs, A. (2021) · doi:10.1145/3411763 · Cited by 245
Topallı, D., & Çağıltay, N. E. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.01.011 · Cited by 244
Nückles, M., Roelle, J., Glogger-Frey, I., Waldeyer, J., & Renkl, A. (2020) · Educational Psychology Review · doi:10.1007/s10648-020-09541-1 · Cited by 213
Noh, J., & Lee, J. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-019-09708-w · Cited by 207
ChatGPT in Computer Science Curriculum Assessment: An analysis of Its Successes and Shortcomings
Qureshi, B. (2023) · International Conference e-Society, e-Learning and e-Technologies · doi:10.1145/3613944.3613946 · Cited by 195
Tsai, C. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.11.038 · Cited by 166
GPTutor: A ChatGPT-Powered Programming Tool for Code Explanation
Chen, E., Huang, R., Chen, H.-S., Tseng, Y., & Li, L. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-36336-8_50 · Cited by 142
Lindberg, R. S. N., Laine, T. H., & Haaranen, L. (2018) · British Journal of Educational Technology · doi:10.1111/bjet.12685 · Cited by 141
Is ChatGPT the Ultimate Programming Assistant -- How far is it?
Tian, H., Lu, W., Li, T. O., Tang, X., Cheung, S.-C., Klein, J., & Bissyandé, T. F. (2023) · arXiv · doi:10.48550/arxiv.2304.11938 · Cited by 114
Gomes, A., & Mendes, A. J. (2014) · 2022 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2014.7044086 · Cited by 95
2014 IEEE Frontiers in Education Conference (FIE) Proceedings
(2014) · doi:10.1109/fie21181.2014 · Cited by 89
Shi, B., Huang, L., & Lu, X. (2020) · Language Testing · doi:10.1177/0265532220911626 · Cited by 81
Sun, J. C., & Hsu, K. Y.-C. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.01.036 · Cited by 62
Proceedings of the 19th Koli Calling International Conference on Computing Education Research
Pears, A., & Schulte, C. (2019) · doi:10.1145/3364510 · Cited by 46
Bai, H.-Q., Wang, X., & Zhao, L. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.771221 · Cited by 41
Yusoff, K. M., Sahari, N., Siti, T., & Mohd, N. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110329 · Cited by 29
Dorotea, N. M. T. C., Piedade, J., & Pedro, A. (2021) · Education Sciences · doi:10.3390/educsci11080443 · Cited by 26
Examining computational thinking processes in modeling unstructured data
Jiang, S., Qian, Y., Tang, H., Yalcinkaya, R., Rosé, C. P., Chao, J., & Finzer, W. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11355-3 · Cited by 22
Oktavia, T., Prabowo, H., Meyliana, M., & Supangkat, S. H. (2018) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech.2018.8528178 · Cited by 22
Program learning for beginners: Survey and taxonomy of programming learning tools
Saito, D., Sasaki, A., Washizaki, H., Fukazawa, Y., & Muto, Y. (2017) · IEEE International Conference on Engineering Education (ICEED) · doi:10.1109/iceed.2017.8251181 · Cited by 16
Analysis of MOOC on Programming for IT Specialist Training
Шаров, С., Колмакова, В., Шарова, Т., & Pavlenko, A. (2021) · TEM Journal · doi:10.18421/tem104-52 · Cited by 14
Programming learners struggle as much in Python as in C++ or Java
Gordon, C. L., Lysecky, R., & Vahid, F. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--41410 · Cited by 6
Research works printed but not linked: 2
- Agbo, 2019, November). A systematic review of computational thinking approach for programming education in higher education institutions, 1
- Zinovieva, 2021, March). The use of online coding platforms as additional distance tools in programming education, Vol. 1840
Cited by
Sorted by citations
Yusuf, A., Pervin, N., & Román-González, M. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00453-6 · Cited by 554
“As proposed by Yilmaz and Yilmaz (2023b), one of the researchers coded and categorized the qualitative responses, and a second encoder reviewed the code categories to ensure accuracy.”
BackgroundMethodInfluentialAbbas, M., Jam, F. A., & Khan, T. I. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00444-7 · Cited by 531
“Continuous use of ChatGPT for academic tasks may develop laziness among the students and weaken their cognitive skills (Yilmaz & Yilmaz, 2023a) leading to a memory loss.”
BackgroundInfluentialWang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2383033 · Cited by 468
Liu, M., Zhang, L. J., & Biebricher, C. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104977 · Cited by 397
Generative artificial intelligence acceptance scale: A validity and reliability study
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Metadata
- Authors
- Yilmaz, R., & Karaoglan Yilmaz, F. G.
- ORCID
- Yilmaz, Ramazan
- Venue
- Computers in Human Behavior: Artificial Humans
- Publisher
- Elsevier BV
- Published
- 2023-08
- Volume
- 1
- Issue
- 2
- Pages
- 100005
- ISSN
- 2949-8821 (ISSN-L)
- OpenAlex
- W4384461076
- Cited by
- 344
- References
- 48
- Citations by year
- 2026: 94 · 2025: 130 · 2024: 103 · 2023: 16
- FWCI
- 78.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers in Human Behavior Artificial Humans (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- ChatGPT, generative AI, programming education, student perceptions, augmented intelligence, object-oriented programming, debugging, professional anxiety, AI-assisted programming, computer science undergraduates
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