{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T20:46:59Z","timestamp":1780778819860,"version":"3.54.1"},"reference-count":79,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/frai.2025.1538840","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T07:10:15Z","timestamp":1741072215000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Augmented intelligence with voice assistance and automated machine learning in Industry 5.0"],"prefix":"10.3389","volume":"8","author":[{"given":"Alexandros","family":"Bousdekis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mina","family":"Foosherian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mattheos","family":"Fikardos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Wellsandt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katerina","family":"Lepenioti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrica","family":"Bosani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregoris","family":"Mentzas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus-Dieter","family":"Thoben","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/978-3-030-57997-5_3","article-title":"Production management as-a-service: a softbot approach","volume-title":"Advances in production management systems towards smart and digital manufacturing: Part II","author":"Abner","year":"2020"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.1109\/INDIN41052.2019.8972015","article-title":"A concept for integration of voice assistant and modular cyber-physical production system","author":"Afanasev","year":"2019"},{"key":"ref3","first-page":"268","article-title":"Two to trust: automl for safe modelling and interpretable deep learning for robustness","author":"Amirian","year":"2021"},{"key":"ref4","year":"2019"},{"key":"ref5","volume-title":"A multivocal literature review on the benefits and limitations of automated machine learning tools","author":"Azevedo","year":"2024"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"e88616","DOI":"10.1371\/journal.pone.0088616","article-title":"Towards a more nuanced view of vocal attractiveness","volume":"9","author":"Babel","year":"2014","journal-title":"PLoS One"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"100975","DOI":"10.1016\/j.aei.2019.100975","article-title":"Estimation of the degree of hydration of concrete through automated machine learning based microstructure analysis\u2013a study on effect of image magnification","volume":"42","author":"Bangaru","year":"2019","journal-title":"Adv. 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