Feasibility of AI-Assisted Programming for End-User Development
Irene Weber
TL;DR
This work addresses whether AI-assisted end-user coding (EUCod) can enable non-programmers to build basic applications, potentially replacing LCNC in some contexts. It conducts a case study where participants use an LLM assistant to create a minimal full-stack web form connected to a Google Sheets backend, assessing feasibility, effort, and acceptance. The findings show substantial success and positive willingness to adopt EUCod, suggesting it is a viable EUD approach with implications for practice and education, while highlighting a need for training and dedicated development platforms. Overall, EUCod emerges as a promising complement to LCNC, warranting further research across diverse user groups and organizational settings.
Abstract
End-user development,where non-programmers create or adapt their own digital tools, can play a key role in driving digital transformation within organizations. Currently, low-code/no-code platforms are widely used to enable end-user development through visual programming, minimizing the need for manual coding. Recent advancements in generative AI, particularly large language model-based assistants and "copilots", open new possibilities, as they may enable end users to generate and refine programming code and build apps directly from natural language prompts. This approach, here referred to as AI-assisted end-user coding, promises greater flexibility, broader applicability, faster development, improved reusability, and reduced vendor lock-in compared to the established visual LCNC platforms. This paper investigates whether AI-assisted end-user coding is a feasible paradigm for end-user development, which may complement or even replace the LCNC model in the future. To explore this, we conducted a case study in which non-programmers were asked to develop a basic web app through interaction with AI assistants.The majority of study participants successfully completed the task in reasonable time and also expressed support for AI-assisted end-user coding as a viable approach for end-user development. The paper presents the study design, analyzes the outcomes, and discusses potential implications for practice, future research, and academic teaching.