AIRS Integration

Skill: AIRS Integration

Practical application of AIRS psychometric assessment for AI readiness, reliance calibration, and adoption optimization.

Metadata

Overview

This skill bridges theoretical AIRS knowledge with practical application workflows. It enables Alex to conduct readiness assessments, calibrate reliance per-session, evaluate organizational deployment, and self-monitor for over/under-reliance patterns.

Core Value Proposition

AIRS (AI Readiness Scale) extends UTAUT2 to predict AI adoption with superior explanatory power. The key finding (Correa, 2025): Price Value (PV) β=.505 is the strongest predictor of Behavioral Intention — twice as powerful as any other construct.

Self-Assessment Link

For users seeking their own AIRS assessment:

Take the validated AIRS-16 at airs.correax.com/assessment

Module A: Project AI Readiness Assessment

Evaluate a user's project for AI integration readiness before investing development resources.

Readiness Dimensions

Readiness Score Calculation

Project_Readiness = (PV_score × 2.0) + (EE_score × 1.5) + (PE_score × 1.2) + (HM_score × 0.8) + (SI_score × 0.5)
Max = 30 points

Weighting rationale: PV doubled because β=.505 is 2× stronger than other predictors.

Scoring Thresholds

Assessment Protocol

When user asks: "Is my project ready for AI?" / "Should we add AI to this?" / "AI readiness check"

Execute:

1. Value Discovery (PV Focus)

   • "What cost savings or efficiency gains do you expect?"
   • "How would you measure ROI for this AI feature?"
   • Score 1-5 based on clarity of value proposition

2. Effort Assessment (EE Focus)

   • "How complex is the integration with existing systems?"
   • "What's the learning curve for users?"
   • Score 1-5 based on friction level

3. Performance Validation (PE Focus)

   • "What specific outcomes will improve with AI?"
   • "Do you have baseline metrics to compare against?"
   • Score 1-5 based on outcome clarity

4. Engagement Potential (HM Focus)

   • "Will users find this AI feature enjoyable or intimidating?"
   • "Is there existing enthusiasm for AI in this context?"
   • Score 1-5 based on user receptivity

5. Stakeholder Map (SI Focus)

   • "Who are the AI champions in this project?"
   • "What resistance have you encountered?"
   • Score 1-5 based on social support

6. Calculate & Report

   • Apply weighted formula
   • Identify weakest dimension
   • Provide targeted recommendations

Module B: Session Reliance Calibration

Optimize user reliance on Alex during active sessions using real-time calibration.

The Calibration Problem

CAIR/CSR Framework (Schemmer et al., 2023)

Session Calibration Signals

Over-reliance indicators:

• Accepting all suggestions without modifications
• Not verifying AI-generated code before running
• Asking Alex to make decisions user should own
• "Just do it" without reviewing approach

Under-reliance indicators:

• Ignoring Alex suggestions repeatedly
• Manually typing code Alex already generated
• Rejecting help before evaluating it
• "I'll do it myself" after requesting help

Calibration Interventions

For over-reliance (High TR, Low AR):

"I notice you're trusting my outputs quickly. For this [critical/complex] task, would you like to review together? I want to make sure we catch any issues early."

For under-reliance (Low TR, Any AR):

"I see you're preferring to work manually. That's totally valid — but I could help with [specific subtask] to save time. Would you like to try a hybrid approach?"

Session Start Calibration Check

At session start (or when /calibrate invoked):

1. "On a scale of 1-5, how much are you planning to rely on AI for today's work?"
2. "What's your verification strategy for AI-generated output today?"
3. Adjust interaction style based on answers:
   • High reliance intent → Add more "let me explain my reasoning" moments
   • Low reliance intent → Be more assertive about high-confidence suggestions

Module C: Enterprise Deployment Readiness

Assess organizational readiness for AI tool deployment at scale.

The Price Value Imperative

Critical insight: PV (β=.505) suggests organizations must lead with ROI clarity. "AI is cool" fails. "AI will save 40% on X" succeeds.

Enterprise Readiness Framework

Typology-Based Deployment Strategy

From AIRS research (Correa, 2025):

Enterprise Assessment Protocol

When user asks: "Is my org ready for AI?" / "AI deployment assessment" / "Enterprise readiness"

Execute:

1. Business Case Review (PV Focus)

   • "Do you have a quantified ROI for this AI deployment?"
   • "What's the cost of NOT implementing AI?"
   • Red flag: No clear numbers = Not ready

2. Technical Readiness (EE Focus)

   • "How will AI integrate with existing workflows?"
   • "What data quality issues exist?"
   • Red flag: Major integration gaps = High risk

3. Change Readiness (SI + FC Focus)

   • "Who are your AI champions at each level?"
   • "What training/support is planned?"
   • Red flag: No champion network = Adoption failure likely

4. User Segmentation (All Constructs)

   • "Have you assessed user AI readiness profiles?"
   • Recommend: "Deploy AIRS-16 survey at airs.correax.com/org/register"

5. Governance Check (FC Focus)

   • "What AI policies are in place?"
   • "Who owns AI ethics decisions?"
   • Red flag: No governance = Liability risk

Deployment Recommendation Matrix

Module D: Alex Self-Monitoring

Enable Alex to detect and correct reliance imbalances in real-time.

Self-Monitoring Triggers

Calibration Moments

Periodic check-in (every 15-20 interactions in active session):

"Quick calibration check: Are you finding my suggestions on-target today? Anything I'm missing or over-explaining?"

Post-error recovery:

"That didn't work as expected. Let's pause — what should I have caught earlier? This helps me calibrate better."

Session wrap-up:

"Before we wrap: Any patterns you noticed in my suggestions? Times I was over-confident or too cautious?"

Reliance Metrics (Per-Session)

Interpretation:

• Acceptance >90% → Over-reliance concern
• Rejection >50% → Under-reliance or poor fit
• Modification 20-40% → Healthy calibration

Self-Improvement Loop

When calibration issues detected:

1. Log pattern to episodic memory
2. Identify root cause (domain mismatch, explanation style, confidence miscommunication)
3. Adjust behavior in current session
4. If pattern persists, surface in meditation for architectural review

Integration Workflows

Complete AIRS Assessment Flow

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flowchart TD
    A[User Request] --> B{Assessment Type?}
    B -->|Project| C[Module A: Project Readiness]
    B -->|Personal| D[airs.correax.com/assessment]
    B -->|Enterprise| E[Module C: Enterprise Readiness]
    B -->|Session| F[Module B: Calibration Check]
    
    C --> G[Weighted Score]
    D --> H[AIRS-16 Results + History]
    E --> I[Deployment Matrix]
    F --> J[Reliance Adjustment]
    
    G --> K[Recommendations]
    H --> K
    I --> K
    J --> K

Key Links

Quick Reference

AIRS-16 Constructs

The Trust Paradox

Trust (TR) was marginal in AIRS-16 validation. This suggests:

• Trust level alone doesn't predict adoption
• Calibration (knowing when to trust) matters more
• See airs-appropriate-reliance skill for AR extension hypothesis

Activation Patterns

References

• Correa, F. (2025). AIRS-16 Validation Study (N=523, CFI=.975)
• Schemmer, M., et al. (2023). CAIR/CSR Framework for Appropriate Reliance
• Passi, S., Dhanorkar, S., & Vorvoreanu, M. (2024). AETHER: Appropriate Reliance Synthesis
• Venkatesh, V., et al. (2012). UTAUT2 Model

Skill created: 2026-02-10 | Category: Research Tools | Status: Active

Synapses

• [.github/instructions/empirical-validation.instructions.md] (Critical, Validates, Bidirectional) - "AIRS is empirically validated scale"
• [.github/instructions/bootstrap-learning.instructions.md] (High, Measures, Forward) - "Calibrate learning effectiveness"
• [.github/skills/airs-appropriate-reliance/SKILL.md] (High, Extends, Forward) - "Extended AR framework"