Studerendes anvendelse af generativ kunstig intelligens i programmeringsundervisning

Authors

DOI:

https://doi.org/10.7146/lom.v18i31.148811

Keywords:

Programming, Generative AI, AI, Higher Education, computational thinking

Abstract

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.

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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

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Published

2025-08-01

How to Cite

Møller, A. K., & Bundgaard, K. (2025). Studerendes anvendelse af generativ kunstig intelligens i programmeringsundervisning. Tidsskriftet Læring Og Medier (LOM), 18(31). https://doi.org/10.7146/lom.v18i31.148811

Issue

Section

LOM 31: Generative AI in Nordic higher education: cases and pedagogical implications