Skip to main content

ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. AI in Business
Volume 8 - 2025 | doi: 10.3389/frai.2025.1538840

Augmented Intelligence with Voice Assistance and Automated Machine Learning in Industry 5.0

Provisionally accepted
Alexandros Bousdekis Alexandros Bousdekis 1*Mina Foosherian Mina Foosherian 2Mattheos Fikardos Mattheos Fikardos 1Stefan Wellsandt Stefan Wellsandt 2Katerina Lepenioti Katerina Lepenioti 1Enrica Bosani Enrica Bosani 3Gregoris Mentzas Gregoris Mentzas 1Klaus-Dieter Thoben Klaus-Dieter Thoben 2
  • 1 National Technical University of Athens, Athens, Greece
  • 2 Bremen Institute for Production and Logistics (BIBA), University of Bremen, Bremen, Bremen, Germany
  • 3 Beko Europe, Varese, Italy

The final, formatted version of the article will be published soon.

    Augmented intelligence puts together human and artificial agents to create a socio-technological system, so that they co-evolve by learning and optimizing decisions through intuitive interfaces, such as conversational, voice-enabled interfaces. However, existing research works on voice assistants relies on knowledge management and simulation methods instead of data-driven algorithms. In addition, practical application and evaluation in real-life scenarios are scarce and limited in scope. In this paper, we propose the integration of voice assistance technology with Automated Machine Learning (AutoML) in order to enable the realization of the augmented intelligence paradigm in the context of Industry 5.0. In this way, the user is able to interact with the assistant through Speech-To-Text (STT) and Text-To-Speech (TTS) technologies, and consequently with the Machine Learning (ML) pipelines that are automatically created with AutoML, through voice in order to receive immediate insights while performing their task. The proposed approach was evaluated in a real manufacturing environment. We followed a structured evaluation methodology, and we analyzed the results, which demonstrates the effectiveness of our proposed approach.

    Keywords: Digital Intelligent Assistant, Automated machine learning, Voice assistance, human-AI collaboration, artificial intelligence, Smart manufacturing

    Received: 03 Dec 2024; Accepted: 07 Feb 2025.

    Copyright: © 2025 Bousdekis, Foosherian, Fikardos, Wellsandt, Lepenioti, Bosani, Mentzas and Thoben. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Alexandros Bousdekis, National Technical University of Athens, Athens, Greece

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.