Tools · Management & HR
Kirkpatrick Evaluation
Prove training changed behavior and results — not just that people enjoyed it.
How it works
Grounded in the Kirkpatrick/Phillips corpus (people-analytics): designs measurement at all four levels (Reaction → Learning → Behavior → Results) with instruments, timing, and success bars, the chain-of-evidence between levels, an optional Phillips Level-5 ROI extension (per-stream monetization + isolation plans with credibility rules; ROI% and BCR computed deterministically in code when financials are supplied — never by the model), and honest caveats about attribution. Reuses the reliability stats engine for Level-2 assessment.
You bring
{ program, context?, include_roi?, financials? (program_cost · benefit_streams · isolation_adjustment), cluster? }
You get
{ program_summary, levels[1..4] (measures · instruments · timing · success_indicator), chain_of_evidence, level5? (benefit_streams · computed ROI%/BCR · honesty_notes), roi_level5?, caveats[], grounded_in, provenance }
Use it for
- →L&D budget defense: a program → a four-level evaluation that reaches behavior + results, not smile sheets
- →Program design review: surface where the chain-of-evidence is weakest before launch
- →ROI case: a Phillips Level-5 frame with an isolation method and cost/benefit sketch
Run it on your data
Call it on your own inputs — over the API, or hand it to your AI agent via MCP. Discovery is open; running it is metered.