Join the PAICE Research Community: How You Can Contribute

PAICE.work is building something unprecedented: the first objective, behavioral measure of People+AI collaboration effectiveness.

But we can't do it alone.

Validation requires data, feedback, and insights from real practitioners across diverse contexts. Whether you're a casual user interested in AI collaboration or a researcher studying People-AI interaction, there are meaningful ways you can contribute to this work.

This post explains the different levels of participation, what each involves, and how your contribution helps establish industry standards for AI collaboration measurement.

Why Validation Matters

The Current State

Right now, organizations are deploying AI at scale with no reliable way to measure whether people can actually use it effectively. The tools available are:

Self-assessments (biased and unreliable)
Knowledge tests (don't predict actual performance)
Usage metrics (show adoption, not effectiveness)
Maturity models (measure process, not capability)

None of these measure what actually matters: behavioral patterns when AI is uncertain, incomplete, or wrong.

What PAICE Is Building

PAICE.work provides behavioral observation of real collaboration patterns across five dimensions:

Performance (communication efficiency)
Accountability (failure detection and recovery)
Integrity (logical consistency and factual grounding)
Collaboration (iterative refinement)
Evolution (meta-awareness and adaptation)

But for this framework to become an industry standard, it needs rigorous validation.

Your Role

Every person who takes the assessment, provides feedback, or participates in research helps:

Validate the framework - Does it measure what it claims to measure?
Refine the methodology - How can we improve precision and reliability?
Establish norms - What does "good" collaboration look like across contexts?
Build evidence - What patterns predict real-world effectiveness?

Levels of Participation

Level 1: Take the Assessment (Everyone)

Time commitment: 25 minutes What it involves: Complete a PAICE assessment with a real work task
How it helps: Provides behavioral data for validation studies

You're already contributing by taking the assessment. Every session helps us:

Identify patterns across different task types
Understand score distributions
Refine dimensional measurements
Test framework reliability

No additional action required - but your participation matters.

Level 2: Provide Feedback (5 minutes)

Time commitment: 5-10 minutes after assessment
What it involves: Share your experience and impressions
How it helps: Identifies areas for improvement and validates user experience

After your assessment, tell us:

Did the score feel accurate?
Were the recommendations helpful?
Did anything surprise you?
What would make the assessment more valuable?

How to provide feedback:

Use the feedback form at the end of your assessment
Contact us via our dedicated webpage
Share your experience on social media (tag #PAICEwork)

Level 3: Retake Periodically (Ongoing)

Time commitment: 25 minutes every 30-90 days What it involves: Retake the assessment to track changes over time
How it helps: Validates score stability and sensitivity to improvement

Longitudinal data is crucial for understanding:

How stable are collaboration patterns over time?
Can the assessment detect genuine improvement?
What factors influence score changes?

Recommended schedule:

Initial assessment (establish baseline)
30 days later (test short-term stability)
60 days later (measure improvement sensitivity)
Quarterly thereafter (track long-term development)

Track your progress and share insights about what changed between assessments.

Level 4: Detailed Participation (30-60 minutes)

Time commitment: 30-60 minutes (one-time or periodic)
What it involves: Extended feedback session or structured interview
How it helps: Provides deep qualitative insights for framework refinement

What this might include:

Detailed discussion of your assessment experience
Comparison with your self-perception of collaboration skills
Feedback on specific dimensional measurements
Suggestions for framework improvements
Discussion of real-world collaboration challenges

Who this is for:

Practitioners with significant AI collaboration experience
People with insights about specific industries or use cases
Users who had particularly interesting assessment experiences
Anyone passionate about improving the framework

How to participate: Contact us via our webform

Level 5: Research Partnership (Ongoing collaboration)

Time commitment: Varies by project (typically 2-10 hours)
What it involves: Formal research collaboration or validation study
How it helps: Enables rigorous academic validation and peer review

Types of research partnerships:

Academic Researchers:

Validation studies comparing PAICE to other measures
Predictive validity research (do scores predict real-world outcomes?)
Construct validity studies (does PAICE measure what it claims?)
Cross-cultural or domain-specific validation

Industry Partners:

Team or organizational assessment pilots
Longitudinal effectiveness tracking
Training program evaluation
ROI measurement studies

Domain Experts:

Industry-specific validation (healthcare, finance, education, etc.)
Task-specific framework refinement
Specialized use case development

How to explore partnerships: Contact us with:

Your area of expertise or research interest
Potential collaboration ideas
Your institutional affiliation (if applicable)
Timeline and resource availability

What Research Participation Involves

Data Collection

What we collect:

Assessment scores and dimensional breakdowns
Anonymized behavioral patterns (not conversation content)
Feedback and survey responses
Demographic information (optional, for research only)

What we don't collect:

Personally identifiable information (unless you explicitly provide it)
Conversation content (processed in real-time, then discarded)
Employer information (unless you choose to share it)

Your rights:

All participation is voluntary
You can withdraw at any time
You can request data deletion
Research data is anonymized and aggregated

Privacy and Ethics

Our commitments:

Transparency: Clear communication about data use
Consent: Explicit opt-in for research participation
Privacy: Anonymization and secure data handling
Ethics: IRB-equivalent review for formal studies
Benefit: Research findings shared with community

Your data is protected through:

Cryptographic hashing of identifiers
Aggregate-only reporting
Secure data storage
No data selling or marketing use

Time and Effort

Minimal participation (Levels 1-2):

20-35 minutes total
No ongoing commitment
Immediate value (your assessment results)

Moderate participation (Levels 3-4):

1-2 hours over several months
Flexible scheduling
Additional insights into your development

Research partnership (Level 5):

Varies by project
Negotiated timeline and scope
Potential for co-authorship or acknowledgment

Impact of Your Contribution

Individual Impact

Your participation helps:

Validate that PAICE measures what it claims to measure
Refine dimensional definitions and scoring
Improve assessment accuracy and reliability
Develop better recommendations and insights

Example: Early user feedback revealed that Accountability scores were consistently lower than other dimensions. This led to deeper investigation and the discovery that failure detection is genuinely harder than other collaboration skills—a key insight now central to the framework.

Community Impact

Collective contributions enable:

Industry-wide collaboration standards
Evidence-based training programs
Organizational readiness assessment
Academic research on people+AI interaction

Example: Aggregate data from 1,000+ assessments helped establish tier boundaries and score distributions, making individual results more meaningful through comparison to broader patterns.

Research Impact

Validation studies support:

Peer-reviewed publication
Academic credibility
Industry adoption
Policy and governance frameworks

Example: Research partnerships with universities are enabling formal validation studies that will be published in academic journals, establishing PAICE as a scientifically validated measurement tool.

Current Research Priorities

What We're Studying Now

1. Predictive Validity

Does PAICE score™ predict real-world collaboration effectiveness?
What outcomes correlate with different score levels?
How do scores relate to productivity and quality metrics?

2. Construct Validity

Does PAICE measure collaboration effectiveness vs. other constructs?
How do dimensions relate to each other?
What patterns distinguish high vs. low performers?

3. Reliability and Stability

How stable are scores over time?
What factors influence score variability?
How sensitive is the assessment to genuine improvement?

4. Domain Specificity

Do collaboration patterns vary by industry or role?
Are different task types more or less revealing?
Should scoring be adjusted for context?

Where We Need Help

Diverse participants:

Different industries and roles
Varying levels of AI experience
International and cross-cultural perspectives
Different task types and use cases

Longitudinal data:

Repeated assessments over time
Before/after training or development programs
Tracking improvement efforts

Qualitative insights:

Detailed feedback on assessment experience
Real-world collaboration challenges
Framework refinement suggestions

How to Get Started

For Casual Contributors

Take the assessment with a real work task
Provide feedback on your experience
Share your results (if comfortable) to help others understand the framework
Retake periodically to contribute longitudinal data

For Active Participants

Reach out via our Contact page
Describe your interest in contributing
Share your background (industry, role, AI experience)
Indicate your availability for extended participation

For Research Partners

Prepare a brief proposal outlining:
- Research question or validation goal
- Methodology and timeline
- Resources and institutional support
- Expected outcomes
Email us via our Contact page
Schedule a discussion to explore collaboration

Frequently Asked Questions

"Do I need research experience to participate?"

No. Levels 1-4 require no research background. You just need to be someone who uses AI tools and is willing to share your experience.

"Will I be compensated?"

Currently, no. PAICE.work is in Research Preview and free to use. Your "compensation" is the assessment itself and the insights it provides. For extensive research partnerships, we're open to discussing arrangements.

"Can I participate anonymously?"

Yes. You can contribute data and feedback without providing identifying information. For research partnerships, some identification may be necessary for collaboration logistics.

"How will my data be used?"

Your data contributes to:

Aggregate validation studies
Framework refinement
Academic research (anonymized)
Public reporting of findings

Your data will never be:

Sold to third parties
Used for marketing
Shared with employers without consent
Published in identifiable form

"Can I see the research findings?"

Yes. We're committed to transparency. Research findings will be:

Published on our blog
Shared in academic journals
Presented at conferences
Made available to participants

The Bigger Picture

PAICE.work is building the foundation for how organizations will measure, develop, and govern AI collaboration capability for years to come.

Your participation helps establish:

Industry standards for collaboration measurement
Evidence-based training and development programs
Organizational readiness frameworks
Policy and governance guidelines

This is bigger than one assessment tool. It's about creating a shared understanding of what effective AI collaboration looks like and how to measure it objectively.

Every assessment you take, every piece of feedback you provide, every research conversation you participate in, all of it contributes to building something that doesn't exist yet but desperately needs to.

Join Us

Ready to contribute to the future of AI collaboration measurement?

Start here:

Take the PAICE assessment if you haven't already
Provide feedback on your experience
Contact us to explore deeper participation

Together, we're building the first objective standard for AI collaboration effectiveness.

Your contribution matters.

Questions about research participation? Contact us.

Join the PAICE Research Community