# A Low-Code Methodology for Developing AI Kiosks: a Case Study with the DIZEST Platform

arXiv: https://arxiv.org/abs/2511.17853 · HTML: https://mlsanity.com/paper/2511.17853/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets](https://mlsanity.com/paper/2407.08872/) (2024)
- [ComfyUI-Copilot: An Intelligent Assistant for Automated Workflow Development](https://mlsanity.com/paper/2506.05010/) (2025)
- [Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering](https://mlsanity.com/paper/2211.16041/) (2022)
- [A Systematic Literature Review of Software Engineering Research on Jupyter Notebook](https://mlsanity.com/paper/2504.16180/) (2025)

## Abstract (arXiv)

This paper presents a comprehensive study on enhancing kiosk systems through a low-code architecture, with a focus on AI-based implementations. Modern kiosk systems are confronted with significant challenges, including a lack of integration, structural rigidity, performance bottlenecks, and the absence of collaborative frameworks. To overcome these limitations, we propose a DIZEST-based approach methodology, a specialized low-code platform that enables intuitive workflow design and seamless AI integration. Through a comparative analysis with existing platforms, including Jupyter Notebook, ComfyUI, and Orange3, we demonstrate that DIZEST delivers superior performance across key evaluation criteria. Our photo kiosk case study further validates the effectiveness of this approach in improving interoperability, enhancing user experience, and increasing deployment flexibility.