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TRUVACE RECORD VERSION
record: TRV-2026-2796
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:25:10.669276Z
status: published
lens: trace
sector: science
headline: When combinations of humans and AI are useful: A systematic review and meta-analysis
dek: Inspired by the increasing use of artificial intelligence (AI) to augment humans, researchers have studied human-AI systems involving different tasks, systems and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here we addressed this question by conducting a preregistered systematic review and meta-analysis of 106 experimental studies reporting 370 effect sizes. We searched an interdisciplinary set of dat…
gain_title: A meta-analysis of 106 experiments (370 effect sizes) found human-AI teams outperformed humans working alone (Hedges' g = 0.64), and content-creation tasks showed gains over the best solo performer (g = 0.19, not statistically significant).
problem_title: A meta-analysis of 106 experiments (370 effect sizes) found human-AI combinations did worse than the better of humans or AI alone (g = -0.23), with decision tasks losing ground (g = -0.27) and AI-outperforming-human tasks worse still (g = -0.54).
trace_subject: Performance of human-AI combinations versus humans or AI alone
gain_reading: A meta-analysis of 106 experiments (370 effect sizes) found human-AI teams outperformed humans working alone (Hedges' g = 0.64), and content-creation tasks showed gains over the best solo performer (g = 0.19, not statistically significant).
gain_evidence: The human–AI systems performed significantly better than humans alone, and this pooled effect size was positive ( g  = 0.64
problem_reading: A meta-analysis of 106 experiments (370 effect sizes) found human-AI combinations did worse than the better of humans or AI alone (g = -0.23), with decision tasks losing ground (g = -0.27) and AI-outperforming-human tasks worse still (g = -0.54).
problem_evidence: human-AI combinations performed significantly worse than the best of humans or AI alone (Hedges' g = -0.23; 95% confidence interval, -0.39 to -0.07) | the pooled effect size for human–AI synergy was significantly negative ( g  = −0.27
quick_read: A preregistered systematic review and meta-analysis in Nature Human Behaviour pooled 106 experiments (370 effect sizes) published between January 2020 and June 2023. On average, human-AI combinations performed worse than the better of humans alone or AI alone (Hedges' g = -0.23), a small negative effect.

The same pooled data show human-AI teams did beat humans working alone (g = 0.64, medium to large). The gap between the two results means the AI helped people, but often the AI by itself would have done as well or better.

Task type and relative skill mattered. Decision tasks, about 85% of effect sizes, showed losses (g = -0.27), while creation tasks, about 10%, showed a positive but non-significant gain (g = 0.19). When humans outperformed AI alone, teams gained (g = 0.46); when AI outperformed humans, teams lost (g = -0.54).
limitation: The results apply only to laboratory-style experiments that reported humans alone, AI alone and the team, which may not reflect real-world deployments; heterogeneity was very high, and tests suggested possible publication bias for the augmentation outcome.
tag: Dual reading
key_points: The search found 5,126 papers; 74 met the inclusion criteria, covering 106 experiments and 370 effect sizes from 2020 to mid-2023. | Each study had to report performance for humans alone, AI alone and the human-AI team, and the three-level model accounted for dependent effect sizes. | In a fake hotel review task cited from Cabrera and colleagues, AI alone scored 73% accuracy, humans 55% and the team 69%; for bird images, AI scored 73%, humans 81% and the team 90%. | Explanations and AI confidence displays did not significantly change team performance, nor did participant type or division of labour. | Only 3 of the 100+ experiments assigned subtasks to humans and AI in advance; their pooled synergy (g = 0.22) was not statistically significant. | Heterogeneity was very high (I2 = 97.7% for synergy), and results held in sensitivity checks, including excluding 11 outliers (g = -0.25). | Egger's test suggested publication bias favouring studies where teams beat humans alone, but not for the synergy outcome.
rundown: The authors defined two outcomes: synergy, where the team beats both humans alone and AI alone, and augmentation, where the team beats humans alone. Effect sizes were computed as Hedges' g across accuracy, error and quality measures, drawing on databases from computing, information systems and Web of Science.

Task type split the results: decision tasks, where participants chose among set options, lost performance when combined (g = -0.27), while open-ended creation tasks leaned positive (g = 0.19, 95% CI -0.09 to 0.48) with only 34 effect sizes. The authors suggest generative AI creation tasks are an under-studied area.

Relative skill was a strong factor. When the human beat the AI alone, the team outperformed both (g = 0.46); when AI beat the human, teams lost to the AI (g = -0.54), though they still improved on humans alone (g = 0.74). Over 95% of systems had humans making the final decision.

The authors recommend designing processes that give AI only the subtasks it does better, and reporting AI-alone performance, cost, time and error severity. They say the findings do not mean combining humans and AI is a bad idea.
sources:
- peer_reviewed | Nature Human Behaviour | https://doi.org/10.1038/s41562-024-02024-1 | 2024-10-28
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