Unpacking human–AI collaboration: conceptualisations and emerging research streams

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dc.contributor.authorWang, Yu
dc.contributor.authorHeavin, Ciara
dc.contributor.authorde Paula, Danielly
dc.date.accessioned2026-05-22T13:50:04Z
dc.date.available2026-05-22T13:50:04Z
dc.date.issued2026-04-16
dc.description.abstractDespite the growing adoption of AI across organisations, fragmented knowledge of key concepts and measurement approaches constrains systematic evaluation and cumulative progress. This review aims to harmonise the conceptual vocabulary of HAIC, map how conceptual framings align with evaluation emphases, and surface an stream structure from co-occurrence patterns, thereby supporting more comparable HAIC assessment designs. We reviewed 149 peer-reviewed studies (2018–2025) from AISeL, ACM, and IEEE using a concept-centric approach. We identified 16 recurring themes for coding and mapping, 10 of which we further defined by synthesising consistent definitional passages to specify boundary conditions that distinguish collaboration from tool use and automation. To identify concept–evaluation link patterns, we computed a co-occurrence matrix and a Jaccard-normalised overlap indicator. Our findings reveal four preliminary streams that organise these linkages. Overall, the study proposes a harmonised cross-disciplinary vocabulary and an empirically grounded mapping that strengthens interpretability and comparability in HAIC assessment.en
dc.description.versionPublished Version
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid2653697
dc.identifier.authororcidWang, Yu
dc.identifier.authororcidHeavin, Ciara§0000-0001-8237-3350
dc.identifier.authororcidde Paula, Danielly§0000-0002-4837-7347
dc.identifier.citationWang, Y, Heavin, C & de Paula, D 2026, 'Unpacking human–AI collaboration: conceptualisations and emerging research streams', Journal of Decision Systems, vol. 35, no. 1, 2653697. https://doi.org/10.1080/12460125.2026.2653697
dc.identifier.doi10.1080/12460125.2026.2653697
dc.identifier.issn1246-0125
dc.identifier.issued1
dc.identifier.journaltitleJournal of Decision Systems
dc.identifier.otherRIS: urn:C1E7D8199CFF1A542E0D3A1571CDF721
dc.identifier.otherORCID: /0000-0001-8237-3350/work/215517455
dc.identifier.urihttps://hdl.handle.net/10468/18842
dc.identifier.volume35
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.relation.urihttps://www.scopus.com/pages/publications/105036028797
dc.relation.urihttps://www.tandfonline.com/doi/pdf/10.1080/12460125.2026.2653697
dc.rights© 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is notaltered, transformed, or built upon in any way.
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.statusPeer reviewed
dc.subjectHuman-AI decision making
dc.subjectHuman–AI collaboration
dc.subjectHuman–AI teaming
dc.subjectHybrid intelligence
dc.subjectIntelligence augmentation
dc.subject[CUBS]
dc.titleUnpacking human–AI collaboration: conceptualisations and emerging research streamsen
dc.typeArticle (peer-reviewed)
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