A Low-Code Methodology for Developing AI Kiosks: a Case Study with the DIZEST Platform
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915632662970368 |
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| author | Moon, SunMin Gim, Jangwon Kim, Chaerin Kim, Yeeun Kim, YoungJoo Choi, Kang |
| author_facet | Moon, SunMin Gim, Jangwon Kim, Chaerin Kim, Yeeun Kim, YoungJoo Choi, Kang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17853 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Low-Code Methodology for Developing AI Kiosks: a Case Study with the DIZEST Platform Moon, SunMin Gim, Jangwon Kim, Chaerin Kim, Yeeun Kim, YoungJoo Choi, Kang Software Engineering Artificial Intelligence 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. |
| title | A Low-Code Methodology for Developing AI Kiosks: a Case Study with the DIZEST Platform |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2511.17853 |