Human-AI Cognition & Performance / Human–AI Collaboration
SUB-0031Escalation and Handover
Definition
Escalation and Handover examines the mechanisms, conditions and outcomes through which this factor shapes human cognition, learning, collaboration or sustainable performance in AI-supported settings.
Why this matters
Escalation and Handover may materially affect human capability, independence, confidence, safety and productivity. Poorly designed assistance can create hidden costs even where short-term output appears to improve.
Research questions
What information is required for safe transfer from AI to a person and back again?
Hypotheses
Structured handover packets containing goal, state, evidence and unresolved risks will reduce recovery time and errors.
Proposed methods
workflow experiments; task-allocation trials; simulation; incident analysis; ethnography; decision audits; controlled handover tests; pre-registered analysis; active comparison; subgroup and accessibility analysis; delayed retention or longitudinal follow-up; adverse-effect capture; reproducibility testing; participant debrief
Stakeholders and beneficiaries
workers; team leaders; boards; risk officers; AI vendors; unions; customers; regulators; auditors; operations controllers; incident-response teams