Studerendes anvendelse af generativ kunstig intelligens i programmeringsundervisning
DOI:
https://doi.org/10.7146/lom.v18i31.148811Keywords:
Programming, Generative AI, AI, Higher Education, computational thinkingAbstract
More students are applying for admission to IT- and STEM-related higher education programmes and need to be introduced to programming. Many students experience the learning curve as steep and are particularly challenged in understanding how different programming concepts can be applied in practice. Programming courses often focus on programming exercises, making it very demanding for the teacher to assist all students in a timely manner. Here, chatbots based on generative artificial intelligence (GenAI) can be a potential source of support. In this study, we explore the programming activities that students use GenAI for as well as the technology’s potential impact on the students’ learning outcome. The empirical material includes observations from an introductory course in programming and a survey where (N=30) students participated. The results from the study indicate that AI-based chatbots such as ChatGPT can play a valuable role in the teaching of programming by scaffolding students in developing their computational practices and problem-solving skills when they are included as an element in a coactive person-environment system. At the same time, AI-based chatbots can contribute by aiding the teacher in answering student questions, enabling the teacher to contribute to other learning activities.
Downloads
References
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48-54. https://doi.org/10.1145/19298 87.1929905
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the develop-ment of computational thinking. In Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada
Becker, B. A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J., & Santos, E. A. (2023). Programming is hard or at least it used to be: Educational opportunities and challenges of AI code generation. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education, 500-506. https://doi.org/10.1145/3545945.3569759
Butler, M., & Morgan, M. (2007). Learning challenges faced by novice programming students studying high level and low feedback concepts. Proceedings ascilite Singapore, 99-107.
Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), pp. 77-101. https://doi.org/10.1191/1478088706qp063oa
Cuny, J., Snyder, L., & Wing, J. M. (2010). Demystifying computational thinking for non-computer scientists. [White paper] http://www.cs.cmu. edu/~CompThink/resources/TheLinkWing.pdf
Danmarks Statistik. (2024). Digitalisering - Temaer. https://www.dst.dk/da/Statistik/temaer/digitalisering
Daun, M., & Brings, J. (2023, June). How ChatGPT will change software engineering education. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education 1, 110-116. https://doi.org/10.1145/3587102.3588815
Fincher, S. (1999). What are we doing when we teach programming? FIE'99 Frontiers in Education. 29th Annual Frontiers in Education Conference. Designing the Future of Science and Engineering Education, IEEE. DOI: 10.1109/FIE.1999.839268
Gibson, J. J. (1977). The theory of affordances. Hilldale, USA, 1(2), 67-82.
Gómez Puente, S. M., Van Eijck, M., & Jochems, W. (2013). A sampled literature review of design-based learning approaches: A search for key characteristics. International Journal of Technology and Design Education, 23, 717-732. https://doi.org/10.1007/s10798-012-9212-x
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational researcher, 42(1), 38-43. https://doi.org/10.3102/0013189X12463051
Hellas, A., Leinonen, J., Sarsa, S., Koutcheme, C., Kujanpää, L., & Sorva, J. (2023). Exploring the responses of large language models to beginner programmers’ help requests. In Proceedings of the 2023 ACM Conference on International Computing Education Research 1, 93-105. https://doi.org/10.1145/3568813.3600139
Hu, C. (2004). Rethinking of teaching objects-first. Education and Information technologies, 9, 209–218. https://doi.org/10.1023/B:EAIT.0000042040.90232.88
Lahtinen, E., Ala-Mutka, K., & Järvinen, H. M. (2005). A study of the difficulties of novice programmers. Acm sigcse bulletin, 37(3), 14-18. https://doi.org/10.1145/1151954.106745
Lau, S., & Guo, P. (2023, August). From" Ban it till we understand it" to" Resistance is futile": How university programming instructors plan to adapt as more students use AI code generation and explanation tools such as ChatGPT and GitHub Copilot. In Proceedings of the 2023 ACM Conference on International Computing Education Research 1, 106-121. https://doi.org/10.1145/3568813.3600138
Leinonen, J., Denny, P., MacNeil, S., Sarsa, S., Bernstein, S., Kim, J., ... & Hellas, A. (2023, June). Comparing code explanations created by students and large language models. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education 1,124-130. https://doi.org/10.1145/3587102.3588785
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790
Lu, J. J., & Fletcher, G. H. (2009). Thinking about computational thinking. In Proceedings of the 40th ACM technical symposium on Computer science education, 260-264. https://doi.org/10.1145/1539024.1508959
Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in human behavior, 41, 51-61. https://doi.org/10.1016/j.chb.2014.09.012
Mascolo, M. F. (2005). Change processes in development: The concept of coactive scaffolding. New Ideas in Psychology, 23(3), 185-196. https://doi.org/10.1016/j.newideapsych.2006.05.002
Mascolo, M. F. (2009). Beyond student-centered and teacher-centered pedagogy: Teaching and learning as guided participation. Pedagogy and the Human Sciences, 1(1), 3–27.
Miao, F., & Shiohira, K. (2024). AI competency framework for students, UNESCO Publishing
Microsoft (2024) Makecode. Hentet fra: https://makecode.microbit.org/
Nazir, A., & Wang, Z. (2023). A comprehensive survey of ChatGPT: advancements, applications, prospects, and challenges. Meta-radiology, 100022. https://doi.org/10.1016/j.metrad.2023.100022
Papert, S. (1980). Computers for children. Mindstorms: Children, computers, and powerful ideas, 3-18.
Processing (2024) Welcome to Processing. Hentet fra: https://processing.org/
Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer science education, 13(2), 137-172. https://doi.org/10.1076/csed.13.2.137.14200
Romero, M., Lepage, A., & Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14, 1-15. https://doi.org/10.1186/s41239-017-0080-z
Sabzalieva, E., & Valentini, A. (2023). ChatGPT and artificial intelligence in higher education: Quick start guide, UNESCO
Selby, C. & Woollard, J. (2013). Computational Thinking: The Developing Definition. ePrints Soton, Southampton. https://eprints.soton.ac.uk/356481
Sharples, M. (2023). Towards social generative AI for education: theory, practices and ethics. Learning: Research and Practice, 9(2), 159-167. https://doi.org/10.1080/23735082.2023.2261131
Schulte, C., & Bennedsen, J. (2006). What do teachers teach in introductory programming? In Proceedings of the second international workshop on Computing education research, 17-28. https://doi.org/10.1145/1151588.1151593
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. (M. Cole, V. Jolm-Steiner, S. Scribner, & E. Souberman, Eds.) Harvard University Press. https://doi.org/10.2307/j.ctvjf9vz4
Weintrop, D., & Wilensky, U. (2017). Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education (TOCE), 18(1), 1-25. https://doi.org/10.1145/3089799
Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of child psychology and psychiatry, 17(2), 89-100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anders Kalsgaard Møller, Kristine Bundgaard

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Articles published in the Journal of Learning and Media are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unported Licens.
Authors retain copyright and grant the journal right of first publication; simultaneously articles are licensend under the Creative Commons Attribution license: Attribution-NonCommercial-NoDerviatives (by-nc-nd). Read about this license at https://creativecommons.org/licenses/by-nc-nd/3.0/
---
At LOM.dk, you will also find articles from the discontinued Journal for the Continuing and Further Education of the Danish Universities (UNEV). Note that special rules apply to UNEV articles:
It is the authors and any other copyright holder who have the copyright of articles published under the auspices of UNEV, and access to the articles is contingent on users acknowledging and complying with the associated legal guidelines:
- Users may download and print one copy of any UNEV publication for private studies or research.
- The redistribution of articles or the use of these for revenue-funded activities or commercial purposes are not allowed.
- It is not allowed to distribute the URLs of UNEV articles.