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How Can No/Low Code Platforms Help End-Users Develop ML Applications? - A Systematic Review

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HCI International 2022 – Late Breaking Papers: Interacting with eXtended Reality and Artificial Intelligence (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13518))

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Abstract

With the increasing popularity of machine learning, the demand for users to develop machine learning applications has grown rapidly, which brings about a rising growth for end-user development (EUD) method research. Based on previous works, EUD can be roughly divided into two categories: methods with coding and without coding (i.e. no-code). In recent years, no-code and low-code methods have become the mainstream EUD methods that have been widely concerned by the education and business community, due to their low technical barriers. However, there lacks a comprehensive summary of existing research to answer some fundamental questions, such as: How can no/low-code platform help end-users develop ML applications? what are their effects, design methods, and user experience? This paper answers the above questions through a systematic literature review. Two experienced researchers carefully read, coded, analyzed, and checked the literature by using MAXQDA, the results showed:

1. No-code or low-code tools can already support the development pipeline of ML applications that traditionally requires coding. 2. No-code or low-code methods are preferred by users. 3. For design purposes, the visual development method is the most commonly used form, especially in the field of education. 4. In terms of interactive experience, a few design principles were recognized from the reviewed pieces of literature, including the interactive process should provide a low threshold, high ceiling, and wide walls; the information architecture should meet the mental model of the novice user's cognitive process; the platform functions should support rapid prototyping, iterations, and timely feedback.

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Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    LuYun Li & ZhanWei Wu

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  1. LuYun Li
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  2. ZhanWei Wu
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Correspondence to ZhanWei Wu .

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Editors and Affiliations

  1. U.S. Army Research Laboratory, Adelphi, MD, USA

    Jessie Y. C. Chen

  2. U.S. Army Combat Capabilities Development Command Soldier Center, Orlando, FL, USA

    Gino Fragomeni

  3. Siemens (United States), Princeton, NJ, USA

    Helmut Degen

  4. Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

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Li, L., Wu, Z. (2022). How Can No/Low Code Platforms Help End-Users Develop ML Applications? - A Systematic Review. In: Chen, J.Y.C., Fragomeni, G., Degen, H., Ntoa, S. (eds) HCI International 2022 – Late Breaking Papers: Interacting with eXtended Reality and Artificial Intelligence. HCII 2022. Lecture Notes in Computer Science, vol 13518. Springer, Cham. https://doi.org/10.1007/978-3-031-21707-4_25

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  • DOI: https://doi.org/10.1007/978-3-031-21707-4_25

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