Sensemaking with AI: how trust influences human–AI collaboration in health and creative industries
Open accessAbstract
This study investigates how trust influences human-AI collaboration within the health and creative sectors, where attitudes toward AI vary widely due to field-specific challenges and expectations. By comparing perspectives from 285 professionals, including artists and doctors, and through persona narratives , we explore how trust in AI can enhance human potential and encourage adoption. Findings reveal that trust hinges on transparency, contextual understanding, human agency during collaboration, and the user's initial perception of AI. This study contributes to understanding trust as a cornerstone of successful human-AI collaboration, proposing ways to foster trusted AI adoption across diverse professional landscapes.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In studies on human-AI co-creation or comparisons between two co-creation types, the need to belong has rarely been directly investigated or has been treated only as a mediating variable.
“However, in studies on human–AI co-creation or comparisons between two co-creation types, while many have examined social motivational factors such as trust (Daly et al., 2025), empathy (Zhu et al., 2025; Qian and Wan, 2025), reliance (Siemon et al., 2025) and commitment feeling (Wang et al., 2025), the need to belong, although a fundamental social motivation, has rarely been directly investigated”
From From humans to AI: understanding why AI is perceived as the preferred co-creation partner (2025) · cites this paper as Daly et al., 2025
Cites
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921 · see citing works
Automation and New Tasks: How Technology Displaces and Reinstates Labor
Acemoglu, D., & Restrepo, P. (2019) · Journal of Economic Perspectives · doi:10.1257/jep.33.2.3 · Cited by 2,612 · see citing works
About Face 2.0: The Essentials of Interaction Design
Cooper, A., Reimann, R., Cronin, D., & Noessel, C. (1995) · Cited by 1,339 · see citing works
Artificial intelligence and innovation management: A review, framework, and research agenda✰
Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120392 · Cited by 1,171 · see citing works
Brynjolfsson, E., Li, D., & Raymond, L. (2023) · National Bureau of Economic Research · doi:10.3386/w31161 · Cited by 995 · see citing works
Machines as teammates: A research agenda on AI in team collaboration
Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G.-J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., et al. (2020) · Information & Management · doi:10.1016/j.im.2019.103174 · Cited by 835 · see citing works
Nadarzynski, T., Miles, O., Cowie, A., & Ridge, D. (2019) · DIGITAL HEALTH · doi:10.1177/2055207619871808 · Cited by 810 · see citing works
Hengstler, M., Enkel, E., & Duelli, S. (2016) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2015.12.014 · Cited by 661 · see citing works
Boden, M. A. (2009) · AI Magazine · doi:10.1609/aimag.v30i3.2254 · Cited by 527 · see citing works
Generative artificial intelligence, human creativity, and art
Zhou, E., & Lee, D. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae052 · Cited by 459 · see citing works
Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., et al. (2020) · Computer · doi:10.1109/mc.2020.2996587 · Cited by 415 · see citing works
Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326 · see citing works
Ayaz, M., Pasha, M. F., Alzahrani, M. Y., Budiarto, R., & Stiawan, D. (2021) · JMIR Medical Informatics · doi:10.2196/21929 · Cited by 284 · see citing works
Li, F., Ruijs, N., & Lü, Y. (2022) · AI · doi:10.3390/ai4010003 · Cited by 261 · see citing works
On the Utility of Learning about Humans for Human-AI Coordination
Carroll, M., Shah, R., Ho, M. K., Griffiths, T. L., Seshia, S. A., Abbeel, P., & Dragan, A. (2019) · arXiv · doi:10.48550/arxiv.1910.05789 · Cited by 168 · see citing works
Let's Think Together! Assessing Shared Mental Models, Performance, and Trust in Human-Agent Teams
Schelble, B. G., Flathmann, C., McNeese, N. J., Freeman, G., & Mallick, R. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3492832 · Cited by 160 · see citing works
On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?
Dziri, N., Milton, S., Yu, M., Zaiane, O., & Reddy, S. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.387 · Cited by 121 · see citing works
Kim, E. A., Shoenberger, H., Kim, D.-G., Thorson, E., & Zihang, E. (2024) · International Journal of Advertising · doi:10.1080/02650487.2024.2386916 · Cited by 103 · see citing works
Bao, Y., Gong, W., & Yang, K. (2023) · Systems · doi:10.3390/systems11090442 · Cited by 96 · see citing works
An Affordance-Based Framework for Human Computation and Human-Computer Collaboration
Crouser, R. J., & Chang, R. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.195 · Cited by 59 · see citing works
Improving Teamwork Competencies in Human-Machine Teams: Perspectives From Team Science
Stowers, K. L., Brady, L. L., MacLellan, C. J., Wohleber, R. W., & Eduardo Salas, A. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.590290 · Cited by 57 · see citing works
Understandable and trustworthy explainable robots: A sensemaking perspective
Papagni, G., & Koeszegi, S. T. (2020) · Paladyn Journal of Behavioral Robotics · doi:10.1515/pjbr-2021-0002 · Cited by 39 · see citing works
Factors that Influence the Adoption of Human-AI Collaboration in Clinical Decision-MakingPreprint
Hemmer, P., Schemmer, M., Riefle, L., Rosellen, N., Vössing, M., & Kühl, N. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.09082 · Cited by 26 · see citing works
Managing the creative frontier of Generative AI: The novelty-usefulness tradeoff
MUKHERJEE, A., & CHANG, H. H. (2023) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 17 · see citing works
I Am Not Your Typical Chatbot: Hedonic and Utilitarian Evaluation of Open-Domain Chatbots
Hong, J.-W., Fischer, K., Kim, D.-G., Cho, J. H., & Sun, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2416016 · Cited by 11 · see citing works
Collaborative Human-AI Sensemaking for Intelligence Analysis
Dorton, S. L., & Hall, R. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77772-2_12 · Cited by 9 · see citing works
Shared Model of Sense-making for Human-Machine CollaborationPreprint
Tecuci, G. D., Marcu, D., Kaiser, L., & Boicu, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03728 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Lyu, W., Zhang, S., Chung, T., Sun, Y., & Zhang, Y. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100383 · Cited by 68 · see citing works
From humans to AI: understanding why AI is perceived as the preferred co-creation partner
Liu, Y., Yang, Y., & Xu, H. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1695532 · Cited by 10 · see citing works
Sposato, M., Dittmar, E. C., & Portillo, J. P. V. (2025) · EuroMed Journal of Business · doi:10.1108/emjb-05-2025-0187 · Cited by 7 · see citing works
Understanding the Video Content Creation Journey of Creators with Sensory Impairment in Kenya
Xiao, L., Bandukda, M., Li, F. M., Colley, M., & Holloway, C. (2025) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663547.3746356 · Cited by 4 · see citing works
Agentic AI as a service innovation: A mixed-methods study of satisfaction, trust, and continued use
Al-Sharafi, M. A., Alzaeemi, S. A., Dwivedi, Y. K., Abdulkareem, Z. O., Ali, A., & Chae, I. (2026) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2026.101039 · Cited by 7 · see citing works
Human–AI Collaboration in Creativity, Design, and Innovation
Putri, A. A., & Sekti, B. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7153-5.ch009 · Cited by 3 · see citing works
Shuo, W., Mei, Y.-Q., Yue, J.-C., & Xia, H.-M. (2026) · Information Technology & Tourism · doi:10.1007/s40558-026-00384-0 · Cited by 4 · see citing works
Interactive Co-Creation with StyleGAN for Enhancing Visual Design Using Generative AI
Wibowo, M. C., Manongga, D., Hendry, H., & Bayu, T. I. (2025) · International Conference on Creative Communication and Innovative Technology (ICCIT) · doi:10.1109/iccit65724.2025.11167676 · Cited by 2 · see citing works
Health information communication and advocacy in the haemophilia community: an X-based analysis
Ahmed, W., Hardey, M., & Yavetz, G. (2025) · Journal of Documentation · doi:10.1108/jd-09-2024-0224 · Cited by 1 · see citing works
Bhattacharya, A., Svedas, G., Lyskov, A., Strasser, M., & Canonico, L. B. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251372102 · Cited by 1 · see citing works
Collaborating with generative AI: a review of models, applications and challenges
Verma, K., & Yadav, S. (2025) · AI & Society · doi:10.1007/s00146-025-02805-w · Cited by 1 · see citing works
Does brand anthropomorphism matter? Impact on brand loyalty and consumer decision-making
Ye, J., Zhou, X. Y., Zhao, S., & Yua, B. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102664 · Cited by 1 · see citing works
The AI workforce and firm maturity: old firms, new tech
Chatjuthamard, P., Jiraporn, P., Chintrakarn, P., & Wattanatorn, W. (2026) · Journal of Business Economics · doi:10.1007/s11573-026-01271-0 · Cited by 1 · see citing works
Abdulwahab, L. O. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9119-9.ch008 · Cited by 1 · see citing works
Trust-Based Adaptive Consensus Formation Towards Human-Machine Hybrid IntelligencePreprint
Chen, G., Wang, G., Ding, W., Yan, Y., Chen, W., & Shang, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5338274
A Conceptual Framework of Human-AI Synergy for Journalist
Sukpolsawat, C., Piriyasurawong, P., & Nilsook, P. (2025) · Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C) · doi:10.1109/ri2c67120.2025.11282569
Making sense of safety: transforming the corporate safety culture
Lanawaang, Y., & Zulfiningrum, R. (2025) · Jurnal Studi Komunikasi (Indonesian Journal of Communications Studies) · doi:10.25139/jsk.v9i2.10020
Augmented Creativity Uniting Human Intuition and Artificial Intelligence in Design
Shome, S., Bhaumik, P., & Das, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7153-5.ch003
Yang, Z., Chen, G. P., Chotia, V., Malik, P., Quacquarelli, B., & Cillo, V. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-03-2026-0471
Kumar, P., Mastana, A. S., Khanijou, S., & Luanrattana, R. (2026) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v7.i10s.2026.7927
Joseph, J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5067-7.ch005
Preparing future creators: how media students navigate the role of AI in creative expression
Lan, D. H. (2026) · Psychological Science and Education · doi:10.17759/pse.2026310313
Verma, A., Tyagi, V., Khosla, R., & Chandra, A. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0116-5.ch008
How AI induces professional identity threats: A survey study with public relations professionals
Qu, Y., Morehouse, J., Lu, S., & Ahmad, T. (2026) · Public Relations Review · doi:10.1016/j.pubrev.2026.102745
Setyanti, S. W. L. H., Khusna, K., Seminari, N. K., & Jamillah, K. (2026) · Cogent Business & Management · doi:10.1080/23311975.2026.2727723
Development of a theoretical model for calibrating trust in AI to augment highways safety
Ivvala, P., Bayramova, A., Roberts, C. J., Edwards, D. J., & Rillie, I. (2026) · Smart and Sustainable Built Environment · doi:10.1108/sasbe-05-2026-0485
AI-Driven Personalization and Transparency Effects on Customer Experience and Engagement
Afzaal, K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1508-7.ch003
- Authors
- Daly, S. J., Hearn, G., & Papageorgiou, K.
- Affiliations
- Queensland University of Technology (AU)
- Norwegian University of Science and Technology (NO)
- Corresponding author
- Daly, Sarah J.
- Venue
- Social Sciences & Humanities Open
- Publisher
- Elsevier BV
- Published
- 2025
- Volume
- 11
- Pages
- 101346
- ISSN
- 2590-2911 (ISSN-L)
- OpenAlex
- W4407732943
- Semantic Scholar
- a956f3166975b967f20153f0834798f91a2f8031
- Cited by
- 27
- References
- 27
- Citations by year
- 2026: 16 · 2025: 11
- FWCI
- 29.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Social Sciences & Humanities Open (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- trust in AI, human-AI collaboration, sensemaking, healthcare workers, creative industries, artists, artificial intelligence adoption, transparency, human agency, perceptions of AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review