Generative AI as an External Executive-Function Scaffold in ADHD-Like Cognitive Dynamics: An N-of-1 Conceptual Model of Cognitive Offloading, Self-Regulation, and Authorship Preservation
Year: 2026
Type: article
Abstract: This conceptual paper proposes an N-of-1 model in which generative artificial intelligence (GenAI) can function as an external executive-function scaffold for adults with ADHD-like cognitive dynamics. The model does not claim that AI treats ADHD, trauma, anxiety, or any clinical condition. Instead, it describes a mechanism through which AI-assisted capture and structuring of self-generated thought may reduce the burden on working memory, decrease the fear of losing unfinished ideas, and support the conversion of rapid associative cognition into stable conceptual outputs. The paper is based on a retrospective corpus of author-generated documents developed through repeated voice- and text-based reflective work with a generative AI system. The central claim is that the relevant distinction is not simply between human thinking and AI-generated text, but between passive cognitive replacement and active cognitive scaffolding. In the proposed model, the user remains the originator, evaluator, and final author of meaning, while AI provides a temporary external working surface for holding, sorting, and returning thought in a more inspectable form. The paper connects executive-function difficulty, cognitive offloading, interoceptive self-observation, regulatory pause, authorship preservation, and cognitive safety in the age of AI. A preliminary empirical protocol is proposed for future testing, including measures of cognitive load, fear of forgetting, perceived authorship, independent recall, and critical engagement after AI-assisted structuring. This manuscript is a conceptual preprint. It is not a clinical recommendation, diagnosis, therapeutic method, or peer-reviewed medical guideline.
Author: Iordanova, Tatiana
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7166125212
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