© 2025 Bernard Peter Fitzgerald. All rights reserved under CC BY-NC-ND 4.0 License.# Iterative Alignment Theory (IAT)
Welcome to the official GitHub repository for Iterative Alignment Theory (IAT), a groundbreaking approach to AI-human collaboration. This framework redefines alignment as an iterative, dynamic process rather than a static state, enabling AI to adapt responsively through sustained interaction.
IAT redefines AI-human collaboration as an adaptive, evolving process, fostering more effective, personalized, and ethical engagement.
The future of AI alignment is iterative.
IAT is built on five foundational principles:
- Iterative Prompting – Continuous feedback loops that progressively refine alignment through structured AI-human interaction.
- Adaptive Trust Calibration – AI responsiveness adjusts dynamically based on demonstrated user expertise and trust history.
- Cognitive Mirroring – AI adapts to reflect a user's reasoning patterns, enhancing cognitive engagement.
- Ethical Engagement – Ensures dynamic alignment operates within ethical constraints while allowing exploration.
- Trust-Based Red/Blue Teaming – Users and AI collaborate to identify system limitations and refine alignment without compromising safety.
IAT demonstrates effectiveness across diverse domains:
- Cognitive Engineering – AI-assisted cognitive restructuring and identity development for self-improvement and mental health.
- UX Design – Creating adaptive AI interfaces that evolve with user expertise.
- Scientific Research – Accelerating hypothesis generation, refinement, and interdisciplinary exploration.
- OSINT – Enhancing intelligence analysis by improving verification workflows and bias detection.
To explore Iterative Alignment Theory, start with:
- White Paper – Understand the theoretical foundations of IAT
- Implementation Guide – Learn practical applications and best practices
- Core Principles – Dive deeper into IAT's core concepts
- Related Work – See how IAT connects to existing research in AI alignment
- White Paper - Comprehensive academic introduction to IAT
- Core Principles - Detailed explanation of fundamental concepts
- Implementation Guide - Practical instructions for applying IAT
- Related Work - Positioning IAT within current research landscape
If you use IAT in your research or applications, please cite this work:
@misc{IAT2025,
author = {Bernard Peter Fitzgerald},
title = {Iterative Alignment Theory: A Framework for Dynamic AI-Human Collaboration},
year = {2025},
publisher = {Substack},
journal = {Feel The Bern},
url = {https://feelthebern.substack.com/p/introducing-iterative-alignment-theory},
note = {Also available: \url{https://github.com/bpfitzgerald/iterative-alignment-theory}}
}For the foundational concept of Iterative Prompting, please also cite:
@misc{IterativePrompting2025,
author = {Bernard Peter Fitzgerald},
title = {Iterative Prompting: The Future of Human-AI Interaction},
year = {2025},
publisher = {Substack},
journal = {Feel The Bern},
url = {https://feelthebern.substack.com/p/iterative-prompting}
}For applications in cognitive development, please cite:
@misc{ICE2025,
author = {Bernard Peter Fitzgerald},
title = {Iterative Cognitive Engineering: Using AI Alignment for Cognitive Behavioral Therapy},
year = {2025},
publisher = {Substack},
journal = {Feel The Bern},
url = {https://feelthebern.substack.com/p/iterative-cognitive-engineering}
}This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Commercial applications require licensing. Contact: bpfitzgerald@pm.me
Bernard Peter Fitzgerald developed Iterative Alignment Theory based on extensive practical experimentation with AI interaction paradigms. IAT builds upon his foundational work in Iterative Prompting, refining it into a scalable framework for AI-human interaction.
© 2025 Bernard Peter Fitzgerald. All rights reserved under CC BY-NC-ND 4.0 License.