W4409884049 Working papers AI × management and organizations

Unraveling Human-AI Teaming: A Review and Outlook

Bowen Lou · Tian Lu · T. S. Raghu · Yingjie Zhang

Also recorded as: doi:10.2139/ssrn.5211067 · ssrn:5211067

ssrn abstract page · 2025-04-28

Recorded claims
1
coded from the inspected source
Evidence classes
1
ssrn-abstract-page
Recorded limitations
3
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.

Using team situation-awareness theory, the review frames AI agents as active collaborators and organizes a research agenda around team formation, coordination, maintenance, and training, with shared mental models, trust, conflict resolution, delegation, responsibility, and skill adaptation as central mechanisms.

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

Scope: Conceptual review and research outlook on human-AI teams; the abstract reports no corpus size, search period, study denominator, or empirical effect estimate

Structured relationships

In-corpus citations

Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.

Cites (0)

No in-corpus outgoing citation is available in the current enrichment coverage.

Cited by (0)

No in-corpus incoming citation is available in the current enrichment coverage.

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 does not describe the review search strategy, inclusion criteria, corpus size, or time window.
  • The proposed four-part framework and gaps are an agenda rather than empirically validated organizational effects.

Source independence

ssrn-only