Research Preview 2025.11

We're excited to announce that Research Preview 2025.11 is now open for sharing with the broader community. This milestone represents more than just a product release, it's an invitation to join us in shaping how we measure and improve human+AI collaboration.

What Is Research Preview 2025.11?

Research Preview 2025.11 is the latest iteration of the PAICE (People + Artificial Intelligence Collaboration Effectiveness) assessment platform. It provides a standardized, measurable way to evaluate the quality of human+AI collaboration through natural conversation.

Key Features:

• Natural Assessment: Evaluate your AI collaboration skills through authentic conversation, not artificial tests
• Five-Dimensional Framework: Comprehensive scoring across Performance, Accountability, Integrity, Collaboration, and Evolution
• Privacy-First Design: Single-session architecture with no persistent data storage
• Actionable Insights: Detailed feedback with specific recommendations for improvement
• Immediate Results: Complete assessment in 25 minutes with instant scoring

Why We're Opening This to the Community

The PAICE vision has always been about more than just tool-building, it's about establishing a shared understanding of what effective human+AI collaboration looks like. We believe this requires community participation.

The Challenge We're Addressing

Organizations and individuals currently lack trusted indicators of AI collaboration effectiveness. Without standardized measurement, critical decisions about productivity, training, and readiness remain speculative rather than data-driven. This creates uncertainty in:

• Evaluating team readiness for AI-assisted workflows
• Identifying training needs for effective collaboration
• Measuring productivity improvements from AI integration
• Establishing benchmarks for collaboration quality

Why Community Input Matters

You are the experts in AI collaboration. Every day, you're navigating the challenges of working effectively with AI systems. Your experiences, insights, and feedback are essential to:

1. Validating the Assessment Framework: Does the PAICE scoring model accurately reflect real-world collaboration quality?
2. Refining the Methodology: Are we measuring the right dimensions? Are there gaps in our approach?
3. Building Shared Standards: What should "good" AI collaboration look like across different contexts and industries?
4. Identifying Blind Spots: What are we missing? What assumptions need challenging?

What Makes This a Research Preview

This is explicitly a research preview, not yet a finished product. We're transparent about what that means:

What's Working Well

• Core assessment engine with adaptive testing
• Five-dimensional scoring framework
• Natural conversation flow
• Privacy-first architecture
• Immediate results and insights

What We're Actively Improving

• Scoring Transparency: Making the "why" behind scores more visible
• Assessment Accuracy: Ensuring scores align with user perception
• Contextual Relevance: Adapting assessments to different use cases
• Feedback Granularity: Providing more detailed, actionable insights

What We Need From You

• Take the Assessment: Experience it firsthand
• Share Your Feedback: Does your score feel accurate? What surprised you?
• Question the Framework: Challenge our assumptions about what matters
• Share Your Use Cases: How would you use this in your work?

How to Participate

1. Take the Assessment

Visit paice.work and complete an assessment. Approach it naturally, and have a real conversation about something you're working on. The assessment works best when you're authentic.

Time Investment: 25 minutes What You'll Get: Detailed scoring across five dimensions, key moments analysis, and personalized recommendations

2. Sign up

Once you chose to share your email with us, then we can notify you about:

• Major changes to PAICE and significant project milestones
• New findings from our research preview, and other related AI collaboration research
• Other opportunities for you, your team, and your org to benefit from our work

3. Subscribe to our Blog

Our RSS/Atom feed will bring you new content every weekday, so you can keep up on things like:

• How to improve your PAICE score™ and your general AI collaboration skills
• What we've learned lately from the data/project that surprised us
• Weekly updates on our progress and partnerships

4. Share Your Experience

We're building in the open and value transparency. Share your thoughts:

• What felt accurate? Which insights resonated with your self-perception?
• What felt off? Where did the assessment miss the mark?
• What surprised you? Did you discover strengths or gaps you weren't aware of?
• What's missing? What dimensions or aspects of collaboration aren't captured?

5. Spread the Word

If you find value in PAICE, share it with others who are navigating AI collaboration:

• Teams exploring AI adoption: Help them assess readiness
• Training professionals: Provide a measurement tool for program effectiveness
• Fellow practitioners: Build a community of practice around effective collaboration
• Researchers and academics: Contribute to the emerging field of human+AI collaboration

The Broader Vision

PAICE exists to address a fundamental gap: we lack shared language and standards for human+AI collaboration quality.

Where We're Headed

Short-term (this month & next month):

• Improve scoring transparency and accuracy
• Add user-selectable assessment profiles
• Enhance feedback granularity
• Build community feedback loops
• Introduce optional progress tracking
• Final Research Preview release (2025.12 in December)

Medium-term (2026):

• Full release of PAICE.work for individuals (free & paid versions)
• Release team assessment product
• Release org-ready "People+AI" assessment solution
• Create industry-specific benchmarks
• Establish validation partnerships
• Foster a global community of practice

Long-term (2027 and beyond):

• Build comprehensive People+AI collaboration standards
• Inform AI regulatory policy decisions
• Help people, teams, organizations, and states better collaborate with ASI (Artificial Super Intelligence)

Why This Matters

As AI becomes increasingly integrated into knowledge work, the quality of human+AI collaboration will be a critical differentiator. Organizations and individuals who develop strong collaboration skills will have significant advantages in:

• Productivity: Accomplishing more with AI assistance
• Quality: Producing better outcomes through effective partnership
• Innovation: Leveraging AI for creative problem-solving
• Risk Management: Avoiding AI-related errors and failures
• Adaptability: Evolving practices as AI capabilities advance

Our Commitment to the Community

As we open Research Preview 2025.11, we commit to:

Transparency

• Sharing our methodology openly
• Documenting our assumptions and limitations
• Publishing our findings and learnings
• Being honest about what we don't know

Privacy

• Never storing your AI conversations
• Giving users full control over their results
• Building trust through privacy-first design

Iteration

• Actively incorporating community feedback
• Continuously improving based on real-world use
• Adapting to emerging best practices
• Evolving as the field develops

Accessibility

• Keeping the core assessment free and open to people
• Removing barriers to participation
• Building for diverse users and contexts

Join Us in Building the Future

Research Preview 2025.11 represents our current best understanding of how to measure human+AI collaboration effectiveness. But it's just the beginning.

We need your participation to:

• Validate and refine the assessment framework
• Establish meaningful benchmarks and standards
• Identify gaps and blind spots in our approach
• Build a shared understanding of collaboration quality
• Create resources that benefit the entire community

Getting Started

1. Visit paice.work and take the assessment
2. Reflect on your results and what they reveal about your collaboration approach
3. Share your experience with colleagues, teams, or on social media
4. Provide feedback through our contact form or community channels
5. Join the conversation about what effective AI collaboration looks like

A Note on Research Ethics

As a research preview, we're committed to ethical practices:

• Informed Participation: You know this is research and what we're studying
• Voluntary Engagement: Participation is entirely optional
• Privacy Protection: No data persistence beyond your active session
• Transparent Methodology: Our approach is documented and open
• Community Benefit: Findings will be shared to benefit all participants

What Success Looks Like

We'll know Research Preview 2025.11 is successful when:

• Users find value: The assessment provides genuine insights that improve collaboration
• Scores feel accurate: Results align with users' self-perception and experience
• Community engages: Active participation in refining the framework
• Standards emerge: Shared understanding of collaboration quality develops
• Impact is measurable: Organizations and individuals improve their AI collaboration

Thank You

To everyone who has participated in earlier previews, provided feedback, challenged our assumptions, and helped shape PAICE, thank you! Your contributions have been invaluable.

To those joining us now with Research Preview 2025.11, welcome. Your perspective and experience will help us build something meaningful for the entire community.

Let's build the future of human+AI collaboration together.

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Take Action Today

1. Take the PAICE Assessment - Experience Research Preview 2025.11 firsthand
2. Share Your Feedback - Help us improve by sharing your experience
3. Spread the Word - Share PAICE with others navigating AI collaboration
4. Join the Community - Connect with others building effective AI partnerships

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Research Preview 2025.11 is now live at paice.work. We're building in the open and learning together. Join us.

Recommended Reading

📖 Understanding Research Preview:

• What "Research Preview" Really Means for PAICE Users - What to expect and how to interpret results
• Join the PAICE Research Community: How You Can Contribute - Participation opportunities

📖 Get Started:

• The PAICE Framework: Five Dimensions of AI Readiness - The framework we're validating
• How to Prepare for Your PAICE Assessment (Spoiler: You Don't) - Taking your first assessment