mlsanityData to 7 Oct 2026

End-User Development for Artificial Intelligence: A Systematic Literature Review

arXiv 2304.09863 ↗First posted 14 Apr 2023cs.HC cs.AIcited by 4

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Abstract (arXiv)

In recent years, Artificial Intelligence has become more and more relevant in our society. Creating AI systems is almost always the prerogative of IT and AI experts. However, users may need to create intelligent solutions tailored to their specific needs. In this way, AI systems can be enhanced if new approaches are devised to allow non-technical users to be directly involved in the definition and personalization of AI technologies. End-User Development (EUD) can provide a solution to these problems, allowing people to create, customize, or adapt AI-based systems to their own needs. This paper presents a systematic literature review that aims to shed the light on the current landscape of EUD for AI systems, i.e., how users, even without skills in AI and/or programming, can customize the AI behavior to their needs. This study also discusses the current challenges of EUD for AI, the potential benefits, and the future implications of integrating EUD into the overall AI development process.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 42 references that are in the map. First posted on arXiv 14 Apr 2023.

Builds on

6 of 42 references.

References · 6

  1. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 126
  2. 2021How Robust are Model Rankings: A Leaderboard Customization Approach for Equitable Evaluationcited by 24
  3. 2021Trinity: A No-Code AI platform for complex spatial datasetscited by 9
  4. 2019A Visual Programming Paradigm for Abstract Deep Learning Model Developmentcited by 5
  5. 2021ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Servicescited by 3
  6. 2020AI from concrete to abstract: demystifying artificial intelligence to the general publiccited by 2

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2023Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Reviewcited by 3
  2. 2025LoCoML: A Framework for Real-World ML Inference Pipelinescited by 2
  3. 2024Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitationscited by 0
  4. 2025Explanation-Driven Interventions for Artificial Intelligence Model Customization: Empowering End-Users to Tailor Black-Box AI in Rhinocytologycited by 0