Feasibility of AI-Assisted Programming for End-User Development

TL;DR

This paper explores the feasibility of AI-assisted programming for non-programmers creating digital tools, showing promising results through a case study with positive participant feedback and successful task completion.

Contribution

It introduces AI-assisted end-user coding as a potential alternative or complement to visual low-code/no-code platforms, demonstrating its practicality through empirical case study evidence.

Findings

Most participants completed tasks successfully within reasonable time.

Participants supported AI-assisted coding as a viable development approach.

The approach shows potential to enhance end-user development practices.

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.