Operationalized Self-Research with AI: A Conceptual Position Paper on Person-Specific Empirical Research in Elite Sport

Abstract

Population-based evidence is indispensable in sport psychology but does not, by itself, establish how performance-relevant processes are organized within a specific elite athlete. This conceptual position paper develops Operationalized Self-Research with AI as a person-specific empirical research architecture that combines longitudinal qualitative and quantitative N-of-1 data to generate revisable process knowledge for the athlete whose data are being analyzed. Four research questions address whether generative AI changes the technical and economic feasibility of such research, whether longitudinal person-specific data improve athletes’ understanding of their own processes, whether systematic comparison of successful and unsuccessful situations improves the calibration of self-attributions, and whether more differentiated process knowledge supports regulation and judgment under pressure. Generative AI is treated as research infrastructure rather than epistemic authority. The paper defines methodological limits, including causality, nonstationarity, measurement reactivity, subjective data, and AI-generated coherence, and specifies empirical criteria for testing the proposed architecture.