A Methodological Framework for Embedded Rationale Tracking in AI-Assisted Research: Proof-of-Concept and Call for Community Validation

The integration of artificial intelligence into research workflows presents un-precedented opportunities for accelerating scientific discovery while simultaneouslycreating critical challenges for transparency, reproducibility, and knowledge preser-vation. This paper proposes a comprehensive methodological framework for embed-ding explainable rationale tracking directly into AI-assisted research processes. Ipresent initial proof-of-concept evidence through the development of a complex cog-nitive modeling framework, where systematic documentation enabled rapid progresswhile maintaining intellectual coherence. To protect potentially sensitive imple-mentation details while establishing methodological prior art, I present the ratio-nale tracking methodology with authentic development timeline data but substitutedummy technical specifications for proprietary content. This work representsa methodological framework proposal with single-case implementationevidence, requiring community validation across diverse research con-texts before broader adoption. I call for systematic empirical validation of thisapproach across multiple research domains, teams, and project types to establishits general effectiveness and identify optimal implementation strategies.Keywords: AI-assisted research, reproducibility, scientific methodology, rationale tracking, research acceleration, knowledge preservation, methodological frame-work