AI Evaluation
Using artificial intelligence models to objectively assess trainee performance during simulated exercises.
What is AI evaluation?
AI evaluation in corporate training refers to the use of artificial intelligence to analyze, score, and provide feedback on a trainee's performance during simulated exercises. Instead of relying on a human observer who may be biased, distracted, or inconsistent, AI models assess each interaction against predefined criteria and rubrics.
The concept emerged as natural language processing and large language models became sophisticated enough to understand nuance, context, and intent in human conversation. Some implementations use a single AI model to generate scores. Others run multiple models independently and combine them into a consensus, which can reduce single-model bias but adds cost and latency. There is no universally correct architecture: the right choice depends on the accuracy gain measured against those tradeoffs.
AI evaluation can assess a wide range of competencies: product knowledge accuracy, empathy and active listening, objection handling technique, regulatory compliance adherence, communication clarity, and more. Each criterion receives a score, and the trainee gets actionable feedback they can use to improve.
Single vs multi-model evaluation
The architecture behind the score matters as much as the score itself.
Single Model
One AI evaluator
- Faster and less expensive to implement
- Susceptible to single-model bias and hallucination
- No cross-validation of scoring decisions
- Difficult to audit or explain scoring rationale
Multi-Model
Consensus evaluation
- Multiple models evaluate independently, reducing bias
- Consensus scoring flags outlier evaluations automatically
- Higher confidence scores that stakeholders trust
- Higher cost and latency, and the gain over a single specialized model is often marginal
Why it matters
Evaluation is the bottleneck in most training programs. Organizations can create unlimited content, but meaningful, personalized feedback requires expert time that does not scale. A senior manager can observe perhaps five role-play sessions per week. AI evaluation removes that constraint entirely.
Objectivity is equally important. Human evaluators are influenced by recency bias, halo effects, personal relationships, and fatigue. Two observers watching the same role-play often produce significantly different scores. AI evaluation delivers consistency: the same performance receives the same score whether it happens at 9 AM on Monday or 11 PM on Friday.
For regulated industries, AI evaluation also provides compliance evidence. Every session generates a documented, timestamped record of the trainee's performance against specific competency criteria, exactly what auditors require during inspections.
How Roleplays approaches AI evaluation
Roleplays scores every session with a specialized AI model per surface, evaluating the trainee against your criteria one at a time, from 0 to 100, with a quote from the transcript as evidence for each score. We tested consensus across multiple models and found the accuracy gain did not justify the cost and latency, so we put that budget into sharper criteria and faster feedback instead.
Organizations define their own evaluation criteria, from product knowledge accuracy to empathy, from regulatory compliance to communication clarity. Each criterion carries a weight, and any criterion marked critical can fail the session on its own. The system returns strengths, gaps, and specific recommendations, not just a number.
Evaluation happens in both chat and real-time voice. For voice, the system analyzes not just the words but the flow of the conversation. Results feed straight into analytics dashboards so managers can spot skill gaps and track improvement over time. Video evaluation is on the roadmap.
0-100
Score per criterion
100%
Sessions evaluated
0
Human bias
See AI evaluation in action
Watch how Roleplays scores a single session against your criteria, with transcript evidence and actionable feedback.