K
EU AI Act: Not High Risk Q3

Training Effectiveness Agent

Measure L&D impact - beyond satisfaction scores.

Measures training impact across multiple levels: satisfaction, knowledge transfer, behavioural change, and business outcomes.

Score Dashboard

Agent Readiness 54-61%
Governance Complexity 31-38%
Economic Impact 44-51%
Lighthouse Effect 48-55%
Implementation Complexity 38-45%
Transaction Volume Quarterly

What This Agent Does

Most organisations measure training effectiveness at the first level only: participant satisfaction ('How did you like the course?'). The Training Effectiveness Agent goes deeper, implementing a multi-level evaluation framework that measures reaction (satisfaction), learning (knowledge gained), behaviour (application on the job), and results (business impact). The agent collects evaluation data at each level: post-training surveys for reaction, assessments for learning, follow-up surveys and manager observations for behaviour change, and performance or business metrics for results. It correlates this data across programmes to identify which training investments deliver measurable value and which do not. This analysis enables a fundamental shift in L&D strategy: from spending based on perceived need or popularity to investing based on demonstrated effectiveness. Programmes that consistently fail to produce behaviour change can be redesigned or discontinued. Programmes that correlate with performance improvement can be scaled.

Micro-Decision Table

Human
Rules Engine
AI Agent
Each row is a decision. Expand to see the decision record and whether it can be challenged.
Collect reaction data Distribute and aggregate post-training satisfaction surveys AI Agent

Automated survey distribution and response collection

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Collect learning data Aggregate assessment results and certification outcomes AI Agent

Automated data collection from LMS and assessment systems

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Collect behaviour data Gather follow-up observations and manager feedback AI Agent

Automated survey and feedback collection at defined intervals

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Correlate with performance metrics Analyse relationship between training completion and outcomes AI Agent

Statistical correlation analysis controlling for confounding factors

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Generate effectiveness report Produce multi-level evaluation per programme AI Agent

Automated report generation with statistical summaries

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Decision Record and Right to Challenge

Every decision this agent makes or prepares is documented in a complete decision record. Affected employees can review, understand, and challenge every individual decision.

Which rule in which version was applied?
What data was the decision based on?
Who (human, rules engine, or AI) decided - and why?
How can the affected person file an objection?
How the Decision Layer enforces this architecturally →

Prerequisites

  • Learning management system with completion and assessment data
  • Post-training survey infrastructure
  • Follow-up observation or feedback collection capability
  • Performance metrics accessible for correlation analysis
  • Multi-level evaluation framework definition
  • Statistical analysis capability for correlation and significance testing

Governance Notes

EU AI Act: Not High Risk
Not classified as high-risk under the EU AI Act - the agent evaluates programmes, not individuals. GDPR applies to individual-level training and performance data used in the analysis. Aggregation should be applied when programme-level rather than individual-level insight is the goal. Works council information rights may apply to the collection of behaviour change and performance data linked to training attendance.

Infrastructure Contribution

The Training Effectiveness Agent closes the L&D investment loop: needs analysis identifies gaps, learning path recommendations guide individual development, and effectiveness measurement validates that the investment produced results. This creates the evidence base for L&D budget decisions. Builds Decision Logging and Audit Trail used by the Decision Layer for traceability and challengeability of every decision.

Related Pages

Decision Layer
Governance layer between AI agent and target system. Every decision documented and challengeable.
Co-Determination & AI
Works council, works agreements, and co-determination as architecture constraint.
HR AI Agents
All HR agent services at a glance.

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Back to Catalog

Frequently Asked Questions

How does the agent measure 'behaviour change' after training?

Through a combination of follow-up surveys (asking participants and managers about application on the job), observable metric changes (where applicable), and longitudinal tracking. Behaviour measurement is imperfect - but even imperfect measurement is better than no measurement.

Can the agent prove causation between training and performance improvement?

The agent measures correlation, not causation. However, by controlling for confounding factors and comparing trained vs. untrained groups where possible, it provides the closest approximation to causal inference that is feasible in a workplace context.

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