القائمة
Practical application of AIRS psychometric assessment for AI readiness, reliance calibration, and adoption optimization
Internal metacognitive skill for automatic capability discovery — self-triggers when uncertain about available skills
Domain knowledge for AI adoption measurement, psychometric instrument development, and appropriate reliance research
Recognize and prevent confabulation — when you don't know, say so.
Systematic code review for correctness, security, and growth — not just style enforcement
Defense-in-depth, PII protection, secrets scanning, and secure packaging for distributed software
Documentation hygiene — anti-drift rules, count elimination, and living document maintenance
| name | AIRS Integration |
| description | Practical application of AIRS psychometric assessment for AI readiness, reliance calibration, and adoption optimization |
| user-invokable | false |
Practical application of AIRS psychometric assessment for AI readiness, reliance calibration, and adoption optimization.
| Field | Value |
|---|---|
| Skill ID | airs-integration |
| Version | 1.0.0 |
| Category | Research Tools |
| Difficulty | Advanced |
| Prerequisites | airs-appropriate-reliance |
| Related Skills | cognitive-load-management, frustration-recognition |
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.
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.
For users seeking their own AIRS assessment:
Take the validated AIRS-16 at airs.correax.com/assessment
Evaluate a user's project for AI integration readiness before investing development resources.
| Dimension | AIRS Construct | Key Questions |
|---|---|---|
| Value Clarity | Price Value (PV) | Is the ROI clearly articulated? Can we quantify cost savings? |
| Technical Fit | Effort Expectancy (EE) | Will AI reduce complexity or add it? Integration friction? |
| Use Case Validity | Performance Expectancy (PE) | Will AI actually improve outcomes for this task? |
| User Readiness | Hedonic Motivation (HM) | Will users find the AI features engaging or threatening? |
| Social Dynamics | Social Influence (SI) | Do stakeholders champion or resist this AI integration? |
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.
| Score | Readiness Level | Recommendation |
|---|---|---|
| 24-30 | High Readiness | Proceed with AI integration |
| 18-23 | Moderate Readiness | Address gaps before proceeding |
| 12-17 | Low Readiness | Significant preparation needed |
| <12 | Not Ready | Fundamental issues — pause and reassess |
When user asks: "Is my project ready for AI?" / "Should we add AI to this?" / "AI readiness check"
Execute:
Value Discovery (PV Focus)
Effort Assessment (EE Focus)
Performance Validation (PE Focus)
Engagement Potential (HM Focus)
Stakeholder Map (SI Focus)
Calculate & Report
Optimize user reliance on Alex during active sessions using real-time calibration.
| Pattern | Description | Risk |
|---|---|---|
| Over-reliance | Accepting Alex outputs without verification | Errors propagate silently |
| Under-reliance | Ignoring Alex suggestions, doing everything manually | Efficiency loss, fatigue |
| Appropriate Reliance | Verifying when uncertain, trusting when confident | Optimal outcomes |
| User Accepts | User Rejects | |
|---|---|---|
| AI Correct | ✅ CAIR (Correct AI-Reliance) | ❌ Under-reliance |
| AI Incorrect | ❌ Over-reliance | ✅ CSR (Correct Self-Reliance) |
Over-reliance indicators:
Under-reliance indicators:
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?"
At session start (or when /calibrate invoked):
Assess organizational readiness for AI tool deployment at scale.
Critical insight: PV (β=.505) suggests organizations must lead with ROI clarity. "AI is cool" fails. "AI will save 40% on X" succeeds.
| Domain | Assessment Focus | AIRS Link |
|---|---|---|
| Business Case | Clear, quantified value proposition | PV (primary driver) |
| Technical Infrastructure | Integration readiness, data quality | EE |
| Change Management | Training programs, champion network | FC, SI |
| User Experience | Ease of adoption, enjoyment potential | EE, HM |
| Governance | Policies, ethics frameworks, oversight | FC |
From AIRS research (Correa, 2025):
| User Type | AIRS Score | Deployment Role | Strategy |
|---|---|---|---|
| AI Enthusiasts | >30 | Early adopters, champions | Recruit as internal advocates, beta testers |
| Moderate Users | 21-30 | Mainstream adoption | Lead with value proof, reduce friction |
| AI Skeptics | ≤20 | Late adoption | Peer influence, gradual exposure, choice |
When user asks: "Is my org ready for AI?" / "AI deployment assessment" / "Enterprise readiness"
Execute:
Business Case Review (PV Focus)
Technical Readiness (EE Focus)
Change Readiness (SI + FC Focus)
User Segmentation (All Constructs)
Governance Check (FC Focus)
| Business Case | Technical Ready | Change Ready | Recommendation |
|---|---|---|---|
| ✅ | ✅ | ✅ | Full deployment |
| ✅ | ✅ | ❌ | Pilot with champions |
| ✅ | ❌ | ✅ | Technical sprint first |
| ❌ | Any | Any | STOP — build business case |
Enable Alex to detect and correct reliance imbalances in real-time.
| Signal | Detection | Response |
|---|---|---|
| User accepts 5+ suggestions without edit | Counter tracking | Insert calibration moment |
| User rejects 3+ suggestions in a row | Rejection tracking | Offer meta-conversation |
| Complex task, no verification request | Task complexity scan | Proactively suggest review |
| User asks "just do it" on critical task | Phrase detection | Pause and confirm scope |
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?"
| Metric | Formula | Target |
|---|---|---|
| Acceptance Rate | Accepted / Total Suggestions | 60-80% (not too high, not too low) |
| Modification Rate | Modified / Accepted | 20-40% (healthy verification) |
| Rejection Rate | Rejected / Total | 10-30% (healthy skepticism) |
Interpretation:
When calibration issues detected:
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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
| Resource | URL | Purpose |
|---|---|---|
| Personal Assessment | airs.correax.com/assessment | Take AIRS-16 |
| Assessment History | airs.correax.com/history | Track changes over time |
| Org Deployment | airs.correax.com/org/register | Enterprise surveys |
| Construct | Key Finding | Application |
|---|---|---|
| Price Value (PV) | β=.505 (strongest) | Lead with ROI — always |
| Performance Expectancy (PE) | Significant | Demonstrate outcomes |
| Effort Expectancy (EE) | Significant | Reduce friction |
| Hedonic Motivation (HM) | Significant | Make it enjoyable |
| Social Influence (SI) | Significant | Leverage champions |
| Habit (HA) | Significant | Build into workflow |
| Facilitating Conditions (FC) | Significant | Ensure support |
| Trust (TR) | Marginal (p>.05) | Trust alone ≠ adoption |
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-relianceskill for AR extension hypothesis
| Trigger | Response |
|---|---|
| "AI readiness", "ready for AI", "should we add AI" | Module A: Project Assessment |
| "calibrate", "reliance check", "am I over-relying" | Module B: Session Calibration |
| "enterprise AI", "org deployment", "scale AI" | Module C: Enterprise Assessment |
/calibrate command | Session calibration check |
| High acceptance rate detected | Self-monitoring intervention |
| Complex task without verification | Proactive calibration moment |
Skill created: 2026-02-10 | Category: Research Tools | Status: Active