ai-collaboration-reference-guide
AI Collaboration Reference Guide
Comprehensive documentation of ethical AI collaboration methodology for doctoral research
📚 Overview
This repository documents a complete AI collaboration methodology developed through 11 sessions (October 27 - November 8, 2025) of iterative practice with Claude (Anthropic) for doctoral dissertation research. The system provides frameworks, protocols, and templates for ethical, sustainable, long-term AI collaboration in academic research.
Research Context: Doctoral dissertation on supervisory relationships, scaffolded support, and doctoral student development at Northeastern University’s Graduate School of Education.
🎯 What This Repository Provides
Core Documentation (6 Documents)
- Academic Integrity Framework (H50-C50)
- Bright line tests for appropriate vs. inappropriate AI use
- Red flags and green lights for academic integrity
- Real-time case examples of ethical boundaries
- Documentation best practices
- Comprehensive Reference Guide (H50-C50)
- Complete two-tier chat architecture (Home Base + Work Units)
- Naming conventions for chats, artifacts, and files
- Session protocols (opening, closing, check-ins)
- Decision frameworks and troubleshooting
- Step-by-step implementation guide (6 phases)
- Collaboration Log (H50-C50)
- All 11 sessions documented with timestamps
- Independent work before/during/after each session
- Intellectual ownership statements for each session
- Evidence trails and integrity checkpoints
- Progressive learning arc from setup to system design
- Orientation Protocol (H50-C50)
- Managing system complexity as it emerges
- Four options for scaling collaboration
- Honest assessment of AI limitations
- When to trust AI vs. researcher authority
- Project-Level Instructions Template (H40-C60)
- Customizable template for project-specific AI configuration
- Research methodology integration
- Chat architecture setup
- Timeline and milestone tracking
- Project Overview Template (H20-C80)
- Maintenance protocol for keeping AI oriented
- Three prompt templates (initial, regular update, quick update)
- File routing and active work tracking
- Artifacts inventory with version control
🚀 Quick Start
For Doctoral Students
- Read the Academic Integrity Framework first
- Review the Collaboration Log to see methodology in practice
- Follow the Reference Guide implementation steps
- Adapt templates for your specific research context
For Advisors/Committee Members
- Review Academic Integrity Framework for ethical boundaries
- Examine Collaboration Log for transparency model
- Understand two-tier system in Reference Guide
For Researchers in Other Fields
- Start with Reference Guide for system architecture
- Adapt naming conventions and protocols for your domain
- Use Project Overview Template for maintenance
🏗️ System Architecture
Two-Tier Chat System
TIER 1: Home Base Chat
- Brainstorming, check-ins, exploratory thinking
- Discovery Mode (dialogic, Socratic questioning)
- Long-running (months), retired at major transitions
TIER 2: Work Unit Chats
- Specific deliverables with bounded scope
- Execution Mode (directive, efficient)
- Short-lived (days-weeks), retired when complete
META Chat (Special)
- Maintains Project Overview document
- Updated every 2-4 weeks
- Keeps all chats aligned on current status
Key Protocols
- Opening Protocol: Orientation at start of every session
- Closing Protocol: Document accomplishments and next steps
- Chat Retirement: Proactive management based on token limits and phase transitions
- Contribution Tracking: H[X]-C[Y] ratio system for all outputs
📖 Document Guide
By Use Case
Setting Up Your System: → Start with Reference Guide Implementation section
Maintaining Academic Integrity: → Consult Academic Integrity Framework
Understanding Development Process: → Read Collaboration Log Sessions 1-11
Managing Complexity: → Review Orientation Protocol
Configuring Your Project: → Use Project-Level Instructions Template
Ongoing Maintenance: → Follow Project Overview Template
🔬 Methodology Highlights
Core Principles
- Transparency: Full documentation of AI collaboration with contribution ratios
- Academic Integrity: Clear boundaries between consultation and creation
- Progressive Autonomy: Scaffolded support that builds independence
- Iterative Refinement: System evolves through use, not perfect design
- Researcher Authority: Human maintains intellectual ownership and final decisions
What Makes This Different
- Not just guidelines: Complete working system with 11 sessions of proof
- Action research approach: Iterative cycles of plan-act-observe-reflect
- Honest documentation: Includes failures, adjustments, and complexity management
- Living methodology: Shows system evolution in real-time
- Contribution transparency: Every document marked with H/C ratio
🛠️ Technical Requirements
Required
- Claude Pro, Teams, or Enterprise subscription (Projects feature)
- Understanding of markdown for documentation
- Commitment to academic integrity principles
Optional but Recommended
- Obsidian or similar knowledge management tool
- Zotero or reference manager
- GitHub for version control and sharing
📊 Contribution Ratios Explained
All documents include H[X]-C[Y] ratios indicating human/AI contribution:
- H100-C0: Fully human-created
- H90-C10: Primarily human with minimal AI assistance
- H50-C50: Equal collaboration
- H40-C60: AI-heavy structure with human requirements
- H20-C80: AI-dominant structure with human workflow needs
These ratios enable:
- Transparent methodology documentation
- Academic integrity verification
- Understanding of collaboration types
- Replication by other researchers
📝 Citation
Repository Citation (APA 7th)
Dawson, D. R., II. (2025). AI collaboration reference guide: Comprehensive
documentation of ethical AI collaboration methodology for doctoral research.
GitHub. https://github.com/drdawson2/ai-collaboration-reference-guide
Individual Document Citations
See the Citation & Attribution section at the end of each document for specific citation formats.
👤 Author
David R. Dawson II
- ORCID: 0009-0001-4719-4370
- Institution: Northeastern University, Graduate School of Education
- Position: EdD Student & Associate Director of PhD Programs, College of Arts, Media and Design
- Email: davidrobertodawsonii@outlook.com
- LinkedIn: linkedin.com/in/david-dawson-ii
Research Focus: Supervisory relationships, scaffolded support, and doctoral student development using Hall’s Protean Career Theory and Savickas’ Career Construction Theory
📄 License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made
- NonCommercial — You may not use the material for commercial purposes
Suggested Attribution:
Based on AI Collaboration Reference Guide by David R. Dawson II (2025),
available at https://github.com/drdawson2/ai-collaboration-reference-guide.
Licensed under CC BY-NC 4.0.
🔗 Related Resources
Companion Website
AI-Assisted Dissertation Management System
- Practical implementation guide for Zotero + Obsidian + Claude
- 8 comprehensive sections with copy-to-clipboard prompts
- Step-by-step workflows for literature review and qualitative analysis
How These Resources Connect
- Dissertation Guide = Practical tools for daily research work
- Reference Guide Repository = Complete methodology documentation and system architecture
💬 Using This Repository
For Your Dissertation Methodology
If you use this system in your research, consider including:
I developed my AI collaboration approach based on the framework
documented by Dawson (2025), adapting the two-tier chat architecture
and academic integrity protocols to my specific research context in
[your field]. I maintained contribution tracking (H/C ratios) and
session documentation throughout my dissertation work to ensure
methodological transparency and scholarly integrity.
For Publications or Presentations
This repository provides:
- Evidence of systematic AI collaboration methodology
- Replicable protocols for other researchers
- Contribution to emerging scholarship on AI in academic research
- Case study of action research applied to tool development
🤝 Feedback & Contributions
Questions or Feedback?
Email: davidrobertodawsonii@outlook.com
Subject line: “AI Collaboration Reference Guide Feedback”
Adaptation & Sharing
You are encouraged to:
- Adapt these frameworks for your research context
- Share your adaptations (with attribution)
- Cite this work in your methodology
- Contribute to evolving best practices for AI collaboration
What Would Be Valuable to Share
- Adaptations for different research methodologies
- Discipline-specific protocols or considerations
- Implementation experiences (successes and challenges)
- Extensions or refinements to the system
⚠️ Important Notes
This Is Not
- ❌ Official guidance from Northeastern University
- ❌ Endorsed by Claude/Anthropic
- ❌ A substitute for advisor mentorship
- ❌ Appropriate for all research contexts without adaptation
This Is
- ✅ One researcher’s documented methodology
- ✅ A transparent case study of AI collaboration
- ✅ A starting point for developing your own approach
- ✅ Evidence that ethical AI collaboration is possible
Before Implementing
- Consult your institution’s AI use policies
- Discuss with your advisor and committee
- Review IRB requirements for your research
- Adapt protocols for your specific context
- Maintain critical engagement with AI outputs
📅 Development Timeline
October 27, 2025: Session 1 - System setup (Zotero + Obsidian)
October 29, 2025: Session 2 - First annotated bibliography
November 1, 2025: Sessions 3-5 - Reading, integrity discussion, AI limitations
November 2, 2025: Sessions 6-7 - Theoretical synthesis, coding planning
November 3, 2025: Session 8 - Managing complexity (orientation protocol)
November 5, 2025: Session 9 - Website development (9-page methodology site)
November 6, 2025: Session 10 - Website refinement, mode discovery
November 7, 2025: Session 11 - Complete system design (this repository)
Total: 11 sessions over 12 days documenting foundation → implementation → refinement → systematization
🎯 Next Steps for This Repository
Planned Additions
- Example implementations from different disciplines
- Video walkthrough of system setup (if helpful)
- FAQ document based on user questions
- Case studies of system use across dissertation phases
How to Stay Updated
- Watch this repository for updates and additions
- Check back for refined templates and protocols
- Look for additional case studies as research progresses
📖 Document Table of Contents
All documents include:
- Table of contents (where applicable)
- Contribution reports (H/C ratios)
- Citation & attribution blocks
- CC BY-NC 4.0 licensing
Navigate to any document from the repository file structure or links in this README.
🙏 Acknowledgments
- Dr. Joseph McNabb - Advisor, guidance and support
- Northeastern University - Institutional support
- Claude by Anthropic - AI collaboration capabilities that enabled this methodology development
- The open-source community - For tools and inspiration
- Future users - For adapting and improving these frameworks
Last Updated: November 9, 2025
Repository Status: Active development - living documentation that evolves with use
Version: 1.0 - Foundation documentation complete, ongoing refinement expected