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What’s Informing Trust and Adoption? Can Current Cognitive Models Support Expert Adoption of AI?

Shaun C. Lamb et al.

Journal of Cognitive Engineering and Decision Making2026https://doi.org/10.1177/15553434261430550article
AJG 2
Weight
0.50

What the paper says

Defence often uses rationalistic approaches for the adoption of AI, but we question whether this is appropriate. By studying adoption using naturalistic approaches in the work environment where AI is actually employed, we better can capture situated issues around trust and adoption. We conducted a longitudinal study of expert intelligence analysts adopting a machine-vision Object Detection and Recognition Overlay (ODRO) within their mission teams and tooling, premised on improving analyst functions. We employ and adapt naturalistic decision-making models to identify key situational challenges, showing that naturalistic approaches can better inform Defence’s AI adoption. Specifically, we find that organisational rewards for accuracy influence analysts’ willingness to adopt and rely on imperfect tools, and that analysts develop a non-uniform relationship with ODRO over time, which we translate into design implications. Crucially, our results support a model of interdependence over prevalent task-allocation paradigms that assume human and AI as functionally divided, rational entities.

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https://doi.org/https://doi.org/10.1177/15553434261430550

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@article{shaun2026,
  title        = {{What’s Informing Trust and Adoption? Can Current Cognitive Models Support Expert Adoption of AI?}},
  author       = {Shaun C. Lamb et al.},
  journal      = {Journal of Cognitive Engineering and Decision Making},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/15553434261430550},
}

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What’s Informing Trust and Adoption? Can Current Cognitive Models Support Expert Adoption of AI?

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.