Human Development through AI-Mediated Environments (HD-AIME): A Framework for Cognitive Autonomy, Human Capability, and Longitudinal Assessment
Year: 2026
Type: preprint
Abstract: Artificial intelligence is increasingly embedded in the environments through which people learn, reason, create, and exercise judgment. Yet the quality of human–AI collaborative outputs does not necessarily reveal how sustained interaction affects the independent capabilities of the human participant. This preprint presents Human Development through AI-Mediated Environments (HD-AIME), an independent conceptual and methodological research framework for investigating human capability development under sustained AI mediation. The framework distinguishes two complementary outcomes: Artifact One, the observable product of human–AI collaboration, and Artifact Two, the longitudinal trajectory of independently demonstrable human capability. It proposes that these outcomes should be evaluated separately while examining their relationships across individuals, tasks, and institutional contexts. HD-AIME integrates research on cognitive offloading, automation, distributed cognition, learning, and human–AI interaction. It introduces the Capability–Authority Gap as a proposed analytical construct for examining circumstances in which formal human responsibility may become misaligned with the competence required to exercise meaningful oversight. Developmental Governance extends this inquiry to the longer-term implications of AI deployment for human capability and institutional resilience. The framework outlines complementary research approaches, including longitudinal observation, Cognitive Provenance documentation, and the proposed Human Capability Test (HCT). Initial HCT feasibility work is focused on Evidence, Reasoning, and Judgment, with broader capability domains reserved for future investigation. HD-AIME does not presume that AI necessarily enhances or diminishes human capability. It also considers the possibility of cognitive restructuring, in which sustained AI collaboration produces new patterns of human competence that conventional assessments may not adequately capture. Research status: This publication presents a conceptual framework and proposed research agenda. It does not report original participant data, validated assessment instruments, or completed experimental findings. The HCT and associated methodological proposals require independent evaluation, feasibility testing, and empirical validation. Author: Joseph Korson, Independent Researcher. Version: 1.1 — Foundational Scholarly Preprint, November 2026.
Author: Korson, Joseph
Language: English
Subfield: Human Factors and Ergonomics
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225663310