Paper Number
ECIS2026-1246
Paper Type
CRP
Abstract
Artificial Intelligence (AI) is transforming business development, but has required high levels of IT expertise and costly infrastructure so far. With the advent of low-code/no-code (LCNC) platforms for AI development, the barriers are reduced as domain experts with limited IT expertise are empowered. This democratization of AI, however, comes not without challenges, and one major barrier for domain experts is assessing the quality of AI models created with LCNC. In this structured literature review, 50 papers are analyzed to explore the upcoming challenges in evaluating the AI model quality from the perspective of domain experts, focusing on the software quality criteria of performance, interpretability, and adaptability. Based on the analysis, a research agenda is proposed as a framework visualizing relationships between AI challenges, key requirements on individual, organizational, and technical levels. We finally discuss how to empower domain experts in AI model quality assessment using LCNC AI platforms.
Recommended Citation
Gigerl, Benjamin and Thalmann, Stefan, "Challenges In Assessing AI Model Quality In Low-Code / No-Code Platforms: A Literature Review" (2026). ECIS 2026 Proceedings. 1.
https://aisel.aisnet.org/ecis2026/litrev/litrev/1
Challenges In Assessing AI Model Quality In Low-Code / No-Code Platforms: A Literature Review
Artificial Intelligence (AI) is transforming business development, but has required high levels of IT expertise and costly infrastructure so far. With the advent of low-code/no-code (LCNC) platforms for AI development, the barriers are reduced as domain experts with limited IT expertise are empowered. This democratization of AI, however, comes not without challenges, and one major barrier for domain experts is assessing the quality of AI models created with LCNC. In this structured literature review, 50 papers are analyzed to explore the upcoming challenges in evaluating the AI model quality from the perspective of domain experts, focusing on the software quality criteria of performance, interpretability, and adaptability. Based on the analysis, a research agenda is proposed as a framework visualizing relationships between AI challenges, key requirements on individual, organizational, and technical levels. We finally discuss how to empower domain experts in AI model quality assessment using LCNC AI platforms.
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