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Iskusstvennyi intellekt v kouchinge: sistematicheskii analiz effektivnosti, eticheskikh dilemm i strategii gibridnoi integratsii

Proceeding
Published in:
All-Russian Scientific and Practical Conference «Coaching psychology: methodology, theory, practice»
Authors:
Aliona V. Shakhova 1 , Fiodor S. Taranenko 2
Work direction:
Varia (we accept materials in other areas that correspond to the topic of the conference)
Pages:
159-166
Received: 1 April 2025

Rating:
Article accesses:
81
1 National Research University Higher School of Economics
2 net
For citation:
Shakhova A. V., & Taranenko F. S. (2025). Iskusstvennyi intellekt v kouchinge: sistematicheskii analiz effektivnosti, eticheskikh dilemm i strategii gibridnoi integratsii. Coaching psychology: methodology, theory, practice, 159-166. Чебоксары: PH "Sreda".

Abstract

Целью данного систематического обзора является синтез современных академических исследований (2020–2025 гг.), посвященных интеграции искусственного интеллекта (ИИ) в коучинг. Методология включает критический анализ 17 работ, охватывающих эмпирические исследования, концептуальные модели и практические кейсы в контекстах лидерства, медицины, продаж и психического здоровья. Результаты демонстрируют, что ИИ эффективен в структурированных задачах, таких как постановка целей (увеличение уверенности пользователей на 34%, Khandelwal & Upadhyay, 2021) и анализ данных (выявление 63% запросов, связанных с карьерным ростом, Movsumova et al., 2020), но уступает человеку в сферах, требующих эмпатии и работы с неоднозначными сценариями (снижение эффективности на 39% в задачах двойной петли обучения, Arakawa & Yakura). Гибридные модели, сочетающие ИИ и человеческий коучинг, признаны наиболее перспективными, демонстрируя повышение достижения целей на 28% (Arakawa & Yakura); однако их внедрение сопряжено с этическими рисками, включая предвзятость данных (35% гендерных стереотипов в рекомендациях, Terblanche, 2024) и проблемы конфиденциальности (18% пользователей выражают обеспокоенность, Huang et al., 2025). Рекомендации для практиков включают развитие ИИ-грамотности, внедрение прозрачных гибридных систем и проведение лонгитюдных исследований для оценки долгосрочного воздействия.

References

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