AI-Assisted Evaluation
AI supports the evaluation. Humans make the decision.
TAP uses AI to analyse responses, surface behavioural evidence, and suggest scores — while assessors review, edit, and approve every result.
Evaluating open-ended responses at scale is slow and inconsistent. TAP applies BARS and BOS methodologies so AI can extract evidence and propose scores against clear behavioural anchors, giving assessors a faster, more consistent starting point that they remain fully in control of.
How AI-assisted evaluation works
01
Evidence extraction
AI reads each response and highlights behavioural evidence linked to the relevant competency.
02
Score suggestions
Using BARS-based rubrics, AI proposes a score with the reasoning behind it.
03
Assessor review
Assessors inspect the evidence, adjust or override scores, and add their own notes.
04
Human approval
No report is finalised until an assessor approves the evaluation.
Key capabilities
- Evidence extraction mapped to competencies
- BARS-based score suggestions with rationale
- Full assessor review and override
- Human approval before final reporting
- Consistent rubrics across all participants
- Audit trail of AI suggestions and changes