Feasibility of AI-Assisted Programming for End-User Development

2025-12-05

AI Summary

Non-programmer end users face limitations in development capability and vendor lock-in when using low-code/no-code (LCNC) platforms. Method: This study investigates generative AI–assisted end-user development (AI-TUD) as an alternative or complementary paradigm, proposing a large language model (LLM)–centric, natural language–driven conversational programming framework that enables users to generate executable code directly from natural language instructions. Contribution/Results: Experiments demonstrate that most non-professional users can successfully build basic web applications within reasonable timeframes. Compared to conventional LCNC platforms, AI-TUD significantly enhances development flexibility, task coverage breadth, and technological neutrality while mitigating platform dependency risks. The study empirically validates AI-TUD’s practical efficacy and scalability in end-user development, providing theoretical foundations and empirical evidence for next-generation human–AI collaborative software engineering.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: User Experience and Usability
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Investigates AI-assisted coding feasibility for non-programmers
• Compares AI approach with low-code/no-code platforms for flexibility
• Assesses if AI can enable app development from natural language

Innovation

• AI assistants enable coding from natural language prompts
• Generative AI replaces visual programming in end-user development
• Non-programmers successfully build apps with AI assistance