Multi-competency assessment, grounded in science.
Three validated methodologies—each designed to measure different competencies—unified in one scoring framework. Case studies, situational judgement tests, and knowledge assessments, all backed by research.
Teams have thinking patterns
When teams see their collective pattern, they can balance strengths, plug gaps, and design better AI workflows together.
These are illustrative examples—real team patterns come from aggregating results across your organisation.
Product
Strong framingTypical Kaairo profile: Framing & Feasibility.
Marketing
Idea-heavyTypical Kaairo profile: Creativity & Articulation.
Ops
Action-firstTypical Kaairo profile: Feasibility-led.
Strategy
Big pictureTypical Kaairo profile: Framing & Creativity.
Grounded in the science of good thinking
Each test type draws on established research—from creative problem solving to behavioural science to psychometrics—adapted for modern assessment needs.
Creative Problem Solving (CPS)
A process model that balances divergent thinking (opening up options) and convergent thinking (selecting and strengthening them). Case studies capture how candidates navigate this process with AI assistance.
Creative Product Semantic Scale (CPSS)
A framework for judging creative outputs on dimensions like novelty, resolution, and elaboration. Kaairo adapts this to score the quality of candidate solutions across 9 parameters mapped to 4 dimensions.
Situational Judgement Methodology
SJTs present realistic work scenarios and evaluate how candidates would respond. Research shows SJTs predict job performance, especially for interpersonal competencies like leadership, teamwork, and conflict resolution.
Knowledge Assessment Principles
Multiple-choice questions follow psychometric best practices for measuring domain knowledge. Item analysis ensures questions discriminate effectively between proficiency levels and measure targeted competencies.
Human–AI Interaction Guidelines
Research on how AI systems should set expectations, explain themselves, and adapt cautiously. Case studies specifically evaluate how candidates collaborate with AI as a thinking partner.
From competencies to scores
A unified scoring framework that handles multiple test types while preserving the nuance of each methodology.
Choose the right tests
Define competencies for the role. Kaairo maps competencies to test types that measure them best and recommends an optimal test mix with coverage scores.
Score with structured rubrics
Each test type has its own scoring rubric. Case Studies use 9 parameters across 4 dimensions. SJTs evaluate decision quality. MCQs measure knowledge accuracy.
Weight and combine
Set configurable weights for each test based on importance. Kaairo calculates weighted averages to produce a combined score while preserving individual breakdowns.
Deliver comprehensive insights
Results include combined scores (0–100), individual test scores, dimension-level analysis, and proctoring flags—all normalised for easy comparison.
Each test type measures different competencies
Combine test types to achieve comprehensive coverage across the competencies that matter for each role.
Case Study
- •Problem Solving
- •Critical Thinking
- •Strategic Vision
- •Innovation
- •Analytical Skills
- •Decision Making
Situational Judgement (SJT)
- •Leadership
- •Emotional Intelligence
- •Conflict Resolution
- •Teamwork
- •Communication
- •Adaptability
Knowledge Assessment (MCQ)
- •Technical Expertise
- •Data Analysis
- •Financial Acumen
- •Industry Knowledge
- •Regulatory Compliance
- •Domain Proficiency
Kaairo includes 35+ competencies across 7 categories. Each competency is mapped to the test types that measure it most effectively, enabling AI-powered test recommendations.
FAQs
What does each test type measure?
Case Studies measure problem-solving, critical thinking, strategic vision, and innovation through AI-assisted scenarios. SJTs measure leadership, emotional intelligence, decision-making, and interpersonal skills through realistic workplace scenarios. MCQs measure domain knowledge, technical expertise, and factual understanding.
How are different test types scored?
Case Studies are scored on 9 parameters (Problem Framing, Creative Breadth, Solution Development, Risk/Ethics, Implementation Planning, Novelty, Usefulness, Elaboration, Human-AI Collaboration) mapped to 4 dimensions. SJTs evaluate the quality of behavioural choices against expert-validated responses. MCQs use standard accuracy scoring with optional confidence weighting.
How does weighted scoring work?
When assessments combine multiple tests, you set the weight for each test (e.g., Case Study 50%, SJT 30%, MCQ 20%). Kaairo calculates a weighted average to produce a combined score while preserving individual test scores and dimension breakdowns for detailed analysis.
What research backs each test type?
Case Studies draw on Creative Problem Solving (CPS) methodology and CPSS-style evaluation of creative products. SJTs are backed by decades of validity research showing they predict job performance, especially for interpersonal competencies (McDaniel et al., 2007). MCQs follow established psychometric principles for knowledge assessment.
References & further reading
- Creative Education Foundation (2015). Igniting Creative Potential: The Creative Problem Solving Guidebook.
- Isaksen, S. G., Dorval, K. B., & Treffinger, D. J. (2011). Creative Approaches to Problem Solving: A Framework for Innovation and Change.
- Besemer, S. P., & O'Quin, K. (1999). Creative Product Semantic Scale. In The Creativity Research Handbook.
- McDaniel, M. A., Hartman, N. S., Whetzel, D. L., & Grubb, W. L. (2007). Situational judgment tests, response instructions, and validity: A meta-analysis. Personnel Psychology, 60(1), 63–91.
- Lievens, F., & Patterson, F. (2011). The validity and incremental validity of knowledge tests, low-fidelity simulations, and high-fidelity simulations for predicting job performance. Journal of Applied Psychology, 96(5), 927–940.
- Amershi, S., et al. (2019). Guidelines for Human–AI Interaction. CHI Conference on Human Factors in Computing Systems.