W4392308557 Working papers AI × management and organizations

When Advanced AI Isn't Enough: Human Factors as Drivers of Success in Generative AI-Human Collaborations

Ning Li · Huaikang Zhou · Wenming Deng · Junyuan Liu · Fengxian Liu · Kris Mikel-Hong

Also recorded as: doi:10.2139/ssrn.4738829 · ssrn:4738829

ssrn abstract page · 2024-02-29

Recorded claims
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coded from the inspected source
Evidence classes
1
ssrn-abstract-page
Recorded limitations
2
stated, never hidden

From the hash-bound evidence releases

Recorded claims

Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.

Across two randomized experiments of unreported sample size and period, the abstract reports that generative-AI access improved performance without compressing individual variation, that GPT-4 produced only a small gain over a less advanced model, and that collaboration training improved performance by changing human-AI interaction patterns.

confidence: moderate ssrn-abstract-page apw-e1-ssrn-backfill

Scope: Two randomized experiments and conversation-log analysis; participant universe, sample denominator, task, study period, effect sizes, and inferential statistics are not reported on the abstract page

Structured relationships

In-corpus citations

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Boundaries

Limitations & independence

Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.

Recorded limitations

  • Abstract-level evidence only; full text and PDFs were not inspected.
  • The abstract gives no participant count, condition denominator, participant population, task, experimental period, effect size, confidence interval, test statistic, or p-value; comparative findings are retained only at the qualitative level stated on the page.

Source independence

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