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A future‐oriented approach to the selection of artificial intelligence technologies for knowledge platforms

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  • Andrzej M. J. Skulimowski
  • Thomas Köhler

Abstract

This article presents approaches used to solve the problem of selecting AI technologies and tools to obtain the creativity fostering functionalities of an innovative knowledge platform. The aforementioned selection problem has been lagging behind other software‐specific aspects of online knowledge platform and learning platform development so far. We linked technological recommendations from group decision support exercises to the platform design aims and constraints using an expert Delphi survey and multicriteria analysis methods. The links between expected advantages of using selected AI building tools, AI‐related system functionalities, and their ongoing relevance until 2030 were assessed and used to optimize the learning scenarios and in planning the future development of the platform. The selected technologies allowed the platform management to implement the desired functionalities, thus harnessing the potential of open innovation platforms more effectively and delivering a model for the development of a relevant class of advanced open‐access knowledge provision systems. Additionally, our approach is an essential part of digital sustainability and AI‐alignment strategy for the aforementioned class of systems. The knowledge platform, which serves as a case study for our methodology has been developed within an EU Horizon 2020 research project.

Suggested Citation

  • Andrzej M. J. Skulimowski & Thomas Köhler, 2023. "A future‐oriented approach to the selection of artificial intelligence technologies for knowledge platforms," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(8), pages 905-922, August.
    • Handle: RePEc:bla:jinfst:v:74:y:2023:i:8:p:905-922
    DOI: 10.1002/asi.24763
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    1. Arciszewski, Tomasz, 2018. "Morphological Analysis in Inventive Engineering," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 92-101.
    2. Isto Huvila & Heidi Enwald & Kristina Eriksson‐Backa & Ying‐Hsang Liu & Noora Hirvonen, 2022. "Information behavior and practices research informing information systems design," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 1043-1057, July.
    3. Daniel R. Georgiadis & Thomas A. Mazzuchi & Shahram Sarkani, 2013. "Using multi criteria decision making in analysis of alternatives for selection of enabling technology," Systems Engineering, John Wiley & Sons, vol. 16(3), pages 287-303, September.
    4. Thomas Köhler & Christoph Lattemann & Jörg Neumann, 2021. "Organising Academia Online," Progress in IS, in: Claudia Koschtial & Thomas Köhler & Carsten Felden (ed.), e-Science, edition 1, pages 11-28, Springer.
    5. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
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