Jung Taek Shin
DOI:10.26584/RDPA.2026.10.2.95 Vol.10(No.2) 95-118, 2026
Abstract
This conceptual review proposes a structured, evidence-informed framework for integrating artificial intelligence (AI) into applied sport psychology, focusing on psychological skills training (PST) and athlete counseling. The supporting literature was identified through a structured search of five international and Korean databases across three clusters: AI technologies, sport psychology and PST, and the ethical and cultural dimensions of digital mental health. Traditional PST―goal setting, mental imagery, arousal regulation, self-talk, and attentional control―shows consistent efficacy, though its evidence base is methodologically limited (Lange-Smith et al., 2024; Wang et al., 2025). Its reach remains constrained by geographic access, cost, scheduling, and stigma―barriers especially pronounced in South Korea, where only 7.2% of adults with a 12-month mental disorder diagnosis received professional care (Rim et al., 2023). AI tools―LLM-based conversational agents, mobile applications, and wearable biofeedback systems―offer scalable, personalized support. A South Korean scoping review (Yeo & Sohn, 2025) found chatbots predominant but noted gaps in theory-grounded design and outcome verification. Preliminary evidence (Im, 2026) suggests LLM-assisted PST may aid performance anxiety management and reduce help-seeking barriers in East Asian contexts, pending confirmation. Pilot trials (Bordo et al., 2025) and broader reviews (Erbe et al., 2017) support blended human-AI delivery outperforming either modality alone, though confirmatory trials remain scarce. Documented risks―algorithmic bias, iatrogenic dependency, weak crisis response, and data privacy vulnerability―require robust ethical governance (APA, 2025; AASP, 2024). Building on this, the paper proposes the AI-Augmented Sport Psychology Framework (AASPF), offering several sport-specific contributions that extend prior general “AI-as-adjunct” discussions: (a) an integration architecture in which escalation to a human practitioner is a built-in safety feature rather than an optional add-on; (b) a sport-specific data-access firewall that protects athlete psychological data from secondary use in selection, contract, or organizational performance decisions; and (c) an explicit examination of how AI adoption costs may reproduce, rather than reduce, the access inequities AI promises to address. The AASPF organizes evidence-informed protocols across four iterative domains―assessment, AI-supported between-session PST practice, continuous monitoring, and human-led counseling support―and is offered as a conceptual template to guide future empirical validation. It positions AI as a strategic augmentation tool within human-centered practice―never as a replacement for the qualified sport psychologist. Beyond reiterating that AI should augment rather than replace the practitioner, the AASPF's distinctive value lies in converting that general principle into three enforceable design commitments―runtime escalation encoded at the middleware layer, an athlete-owned data firewall, and design-stage cost-equity appraisal―thereby yielding a deployable and empirically testable model for applied sport psychology practice.
Key Words
Artificial Intelligence, Psychological Skills Training, Sport Psychology Counseling, Conversational Agent, Athlete Mental Health, Blended Intervention, Human–AI Collaboration, Ethical Framework