Using Generative AI to Support UX Design Students in Web Development Courses
Source abstract
Abstract
This work explores the integration of Generative AI (GenAI) tools into web development educational settings, with a focus on enhancing the user experience (UX) design process and supporting students with limited technical backgrounds. The democratization of GenAI has allowed non-technical users to engage in the creation of computing applications. However, its adoption among UX-focused learners remains limited. In this study, we propose an innovative instructional approach designed to facilitate the effective use of GenAI tools in web development courses for UX designers. This approach is based on incorporating interaction mechanisms that allow students to prompt GenAI tools using an incremental strategy. Moreover, this scaffolding process enables the definition of benchmarks that can be used as a reference for the development tasks proposed and their assessment by comparing the students’ outcomes with the benchmarks provided. The results obtained from applying the proposed approach in a web development course using GitHub Copilot environments show its potential to address the challenges UX design students face in these educational settings.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Model-Driven Software Engineering Techniques · Intelligent Tutoring Systems and Adaptive Learning · Teaching and Learning Programming
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, Universitat Politècnica de Valencia, 46022 Valencia, Spain
Department of Computer Systems and Computation, Universitat Politècnica de Valencia, 46022 Valencia, Spain
References (54)
Show source-provided references
1. Generative AI for software practitioners10.1109/MS.2023.3265877 ↗
2. Nguyen-Duc, A., Abrahamsson, P., and Khomh, F. (2024). Generative AI for Effective Software Development, Springer Nature.10.1007/978-3-031-55642-5 ↗
3. Coutinho, M., Marques, L., Santos, A., Dahia, M., França, C., and de Souza Santos, R. (2024). The role of generative ai in software development productivity: A pilot case study. AIware 2024, Proceedings of the 1st ACM International Conference on AI-Powered Software, Porto de Galinhas, Brazil, 15–16 July 2024, Association for Computing Machinery.10.1145/3664646.3664773 ↗
4. An approach for rapid source code development based on ChatGPT and prompt engineering10.1109/ACCESS.2024.3385682 ↗
5. Rajbhoj, A., Somase, A., Kulkarni, P., and Kulkarni, V. (2024). Accelerating software development using generative ai: Chatgpt case study. ISEC ’24, Proceedings of the 17th Innovations in Software Engineering Conference, Bangalore, India, 22–24 February 2024, Association for Computing Machinery.10.1145/3641399.3641403 ↗
6. Petrovska, O., Clift, L., Moller, F., and Pearsall, R. (2024). Incorporating generative ai into software development education. CEP ’24, Proceedings of the 8th Conference on Computing Education Practice, Durham, UK, 5 January 2024, Association for Computing Machinery.10.1145/3633053.3633057 ↗
7. Navigating the pitfalls: Analyzing the behavior of LLMs as a coding assistant for computer science students-a systematic review of the literature10.1109/ACCESS.2024.3443621 ↗
8. Prather, J., Denny, P., Leinonen, J., Becker, B.A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T., and Luxton-Reilly, A. (2023). The robots are here: Navigating the generative ai revolution in computing education. ITiCSE-WGR ’23, Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education, Turku, Finland, 7–12 July 2023, Association for Computing Machinery.10.1145/3623762.3633499 ↗
9. Computing education in the era of generative AI10.1145/3624720 ↗
10. Instructor Perceptions of AI Code Generation Tools—A Multi-Institutional Interview Study
11. Large language models in computer science education: A systematic literature review
12. Becker, B.A., Craig, M., Denny, P., Keuning, H., Kiesler, N., Leinonen, J., Luxton-Reilly, A., Prather, J., and Quille, K. (2024). Generative AI in introductory programming. Computer Science Curricula 2023, Association for Computing Machinery.
13. Software engineering education must adapt and evolve for an llm environment
14. López-Fernández, D., and Vergaz, R. (2025). ChatGPT in Computer Science Education: A Case Study on a Database Administration Course. Appl. Sci., 15.10.3390/app15020985 ↗
15. Kazemitabaar, M., Hou, X., Henley, A., Ericson, B.J., Weintrop, D., and Grossman, T. (2024). How novices use LLM-based code generators to solve CS1 coding tasks in a self-paced learning environment. Koli Calling ’23, Proceedings of the 23rd Koli Calling International Conference on Computing Education Research, Koli, Finland, 13–18 November 2023, Association for Computing Machinery.10.1145/3631802.3631806 ↗
16. The widening gap: The benefits and harms of generative ai for novice programmers
17. Hsiao, H.L., and Tang, H.H. (2024). A Study on the Application of Generative AI Tools in Assisting the User Experience Design Process. Artificial Intelligence in HCI, Proceedings of the International Conference on Human-Computer Interaction, Washington, DC, USA, 29 June–4 July 2024, Springer Nature.10.1007/978-3-031-60611-3_13 ↗
18. Sahay, A., Indamutsa, A., Di Ruscio, D., and Pierantonio, A. Supporting the understanding and comparison of low-code development platforms. Proceedings of the 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Portoroz, Slovenia, 26–28 August 2020.10.1109/SEAA51224.2020.00036 ↗
19. User experience design and agile development: Managing cooperation through articulation work10.1002/spe.1012 ↗
20. Pacheco, J., Garbatov, S., and Goulão, M. Improving collaboration efficiency between UX/UI designers and developers in a low-code platform. Proceedings of the 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Fukuoka, Japan, 10–15 October 2021.10.1109/MODELS-C53483.2021.00025 ↗
21. Rode, J., Rosson, M.B., and Quinones, M.A.P. (2006). End user development of web applications. End User Development, Springer.10.1007/1-4020-5386-X_8 ↗
22. End-user development, end-user programming and end-user software engineering: A systematic mapping study10.1016/j.jss.2018.11.041 ↗
23. Paternò, F. (2023). End-user development. Handbook of Human Computer Interaction, Springer International Publishing.10.1007/978-3-319-27648-9_37-1 ↗
24. Calò, T., and De Russis, L. (2023). Leveraging large language models for end-user website generation. End-User Development, Proceedings of the International Symposium on End User Development, Cagliari, Italy, 6–8 June 2023, Springer Nature.10.1007/978-3-031-34433-6_4 ↗
25. Rise of Generative AI: Impacts on Frontend Development
26. Avdić, A. (2024). Generative AI Tools in Web Design. Proceedings of the Sinteza 2024-International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Serbia, 16 May 2024, Singidunum University.
27. AlDahoul, N., Hong, J., Varvello, M., and Zaki, Y. (2025). Towards a World Wide Web powered by generative AI. Sci. Rep., 15.10.1038/s41598-024-77301-0 ↗
28. Web Sculptor-Generative AI Based Comprehensive Web Development Framework
29. Lu, Y., Zhang, C., Zhang, I., and Li, T.J.J. (2022). Bridging the Gap between UX Practitioners’ work practices and AI-enabled design support tools. CHI EA ’22, Proceedings of the CHI Conference on Human Factors in Computing Systems Extended Abstracts, New Orleans, LA, USA, 29 April–5 May 2022, Association for Computing Machinery.10.1145/3491101.3519809 ↗
30. Designing with AI: A Systematic Literature Review on the Use, Development, and Perception of AI-Enabled UX Design Tools10.1155/ahci/3869207 ↗
31. Voronin, D.N. (2024). Development and Evaluation of an LLM-Based Tool for Automatically Building Web Applications. [Doctoral Dissertation, Massachusetts Institute of Technology].
32. York, E. (2023). Evaluating chatgpt: Generative ai in ux design and web development pedagogy. SIGDOC ’23, Proceedings of the 41st ACM International Conference on Design of Communication, Orlando, FL, USA, 26–28 October 2023, Association for Computing Machinery.10.1145/3615335.3623035 ↗
33. Integrating AI-generative tools in web design education: Enhancing student aesthetic and creative copy capabilities using image and text-based AI generators
34. Naeem, U., Styve, A., and Virkki, O.T. Stimulating Critical Thinking in a Web Programming Module with Generative AI Tools. Proceedings of the 2025 IEEE Global Engineering Education Conference (EDUCON), London, UK, 22–25 April 2025.10.1109/EDUCON62633.2025.11016298 ↗
35. Alpizar-Chacon, I., and Keuning, H. (2025). Student’s Use of Generative AI as a Support Tool in an Advanced Web Development Course. arXiv.10.1145/3724363.3729106 ↗
36. Greenberg, I. (2007). Processing: Creative Coding and Computational Art, Apress.
37. Patel, S.K., Rathod, V.R., and Parikh, S. Joomla, Drupal and WordPress—A statistical comparison of open source CMS. Proceedings of the 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011), Chennai, India, 8–9 December 2011.10.1109/TISC.2011.6169111 ↗
38. Buendía-García, F. Using UX Methods and AI Tools to Enhance Collaborative Scenarios in Creative Technology Courses. Proceedings of the 2024 International Symposium on Computers in Education (SIIE), A Coruna, Spain, 19–21 June 2024.10.1109/SIIE63180.2024.10604490 ↗
39. Voila (2025, May 28). AI Assistant for Your Team. Available online: https://www.getvoila.ai/chatgpt-for-teams.
40. Using GitHub Copilot to solve simple programming problems
41. Merchant, A. (2025, May 28). I Got Access to GitHub Copilot Chat. Available online: https://www.amitmerchant.com/github-copilot-chat/.
42. Pezoa, F., Reutter, J.L., Suarez, F., Ugarte, M., and Vrgoč, D. (2016). Foundations of JSON schema. WWW’16, Proceedings of the 25th International Conference on World Wide Web, Montreal, QC, Canada, 11–15 April 2016, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva.10.1145/2872427.2883029 ↗
43. Chapman, C., Wang, P., and Stolee, K.T. Exploring regular expression comprehension. Proceedings of the 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Urbana, IL, USA, 30 October–3 November 2017.10.1109/ASE.2017.8115653 ↗
44. Straka, M., Hajic, J., and Straková, J. (2016, January 23–28). UDPipe: Trainable pipeline for processing CoNLL-U files performing tokenization, morphological analysis, pos tagging and parsing. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), Portorož, Slovenia.
45. Anandarajan, M., Hill, C., Nolan, T., Anandarajan, M., Hill, C., and Nolan, T. (2019). Term-document representation. Practical Text Analytics: Maximizing the Value of Text Data, Springer.10.1007/978-3-319-95663-3 ↗
46. An introduction to latent semantic analysis10.1080/01638539809545028 ↗
47. Latent semantic analysis for text-based research10.3758/BF03204765 ↗
48. Gururangan, S. (2025, May 28). Latent Semantic Analysis Project. Available online: https://github.com/kernelmachine/pyLSA.
49. Brinck, T., Gergle, D., and Wood, S.D. (2001). Usability for the Web: Designing Web Sites That Work, Elsevier.10.1016/B978-155860658-6/50010-9 ↗
50. Zhang, S., Zhang, T., Cheng, J., and Zhou, S. (2025). Who is to Blame: A Comprehensive Review of Challenges and Opportunities in Designer-Developer Collaboration. arXiv.10.1145/3711105 ↗
51. Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B., and Myers, B. (2024). Using an llm to help with code understanding. ICSE ’24, Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, Lisbon, Portugal, 14–20 April 2024, Association for Computing Machinery.10.1145/3597503.3639187 ↗
52. Oppenlaender, J., Linder, R., and Silvennoinen, J. (2024). Prompting AI art: An investigation into the creative skill of prompt engineering. Int. J. Hum.–Comput. Interact., 1–23.10.1080/10447318.2024.2431761 ↗
53. Mihalcea, R., Corley, C., and Strapparava, C. (2006, January 16–20). Corpus-based and knowledge-based measures of text semantic similarity. Proceedings of the Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, Boston, MA, USA.
54. Gatto, J., Sharif, O., Seegmiller, P., Bohlman, P., and Preum, S.M. (2023). Text encoders lack knowledge: Leveraging generative llms for domain-specific semantic textual similarity. arXiv.