Talent Psychological Adaptation and Training System Construction for Industry Development Empowered by Digital Intelligent Business Disciplines in the AI Era
Dongliang WangAbstract
Objective
The rapid iteration of AI and digital industries brings intense occupational psychological pressure to contemporary business talents, resulting in adaptive emotional disorders and unstable mental states, which severely restrict individual career development and overall industrial upgrading. This study deeply explores the psychological adaptation rules of business talents under digital intelligent empowerment, aiming to effectively relieve industry-specific adaptive psychological pressure and comprehensively improve talents’ comprehensive mental health level.
Subjects and Methods
This study takes on-the-job business practitioners and university business students with industry adaptation demands as formal research subjects. It adopts literature research, field questionnaire surveys and structural equation modeling to systematically analyze the correlation between digital intelligence competency, psychological adaptation pressure, fluctuating mental states and industrial development demands, and summarizes prominent defects in the existing talent training modes.
Results
Psychological adaptation mismatch is the main source of long-term cumulative psychological pressure faced by business talents in the AI industry. Targeted digital intelligent business empowerment can effectively alleviate various negative mental states, reduce excessive adaptive psychological pressure, and significantly enhance talents’ mental health level and professional environmental adaptability.
Conclusions
The constructed talent training system based on psychological adaptation theory can effectively resolve digital adaptive psychological pressure, stabilize talents’ fluctuating mental states, and sustainably improve their long-term mental health. It realizes the deep integration of mental health cultivation and professional ability training, providing solid high-quality talent support for the stable industrial development in the AI digital era.
Corresponding Author
Dongliang Wang, Gansu Minzu Normal University, Hezuo 747000, China.