Personality is considered to be a factor affecting athletic performance. However, inconsistency in the research results regarding size and even direction of the relationship. An evaluation of the evidence of the relationship between personality and athletic performance was conducted in order to summarize the evidence available. A systematic literature search was conducted in March 2023. Sport performance and the Big Five personality model were identified in our research. We used PubMed, Web of Science, Embase, Cochrane Library, Wang Fang (Chinese), Wei Pu (Chinese), and CNKI (Chinese) databases for the systematic literature search (Prospero registration number: CRD42022364000), screened 4,300 studies, and found 23 cross-sectional studies eligible for inclusion in this review. The results of this systematic analysis show that, besides neuroticism, openness, conscientiousness, extraversion, and agreeableness are all positively correlated with sports performance. Conscientiousness and extraversion are the two main personalities in team sports. Openness and agreeableness show different results in different sports, and it is not clear to which project they are beneficial. The value of personality as a possible predictor of athletic performance is generally positive. Therefore, professionals such as applied sports psychologists, coaching personnel, athletes, and sports administrators must comprehensively grasp the significance of personality’s role in achieving success in major competitions. Considering these facts, sports practitioners should promote personality screening and personality development programs.

The design of physical education (PE) lessons is an ongoing research project that encompasses elements such as teaching ideas, teaching objectives and teaching methods. Music is regarded as the pinnacle form of beauty and combining it with PE not only improves the effectiveness of PE lessons, but also increases the artistry of PE lessons. The purpose of this study is to examine the combination of music and cycling lessons in order to determine the effectiveness of cycling lessons in a musical environment. For study one, 95 undergraduate students were randomly selected to participate in the experiment in two conditions (M age = 20.00 years, SD = 1.00 years): (1) with music, (2) without music. For study two, 10 students were randomly selected to cycle in three study conditions: (1) synchronous music, (2) asynchronous fast music and (3) asynchronous slow music. Heart rate (HR), rating of perceived exertion (RPE), Exercise-Induced Feeling Inventory (EFI) and sport performance were measured. There were significant differences in HR, RPE, EFI and exercise performance between the two conditions with and without music, and the group with music performed higher than the group without music. In study two, ratings of perceived exertion were significantly lower for the synchronous music condition at 25, 30 min of the steady state portion of the cycling trials. No significant difference between conditions were found in HR, EFI and sport performance. An innovative fundamental model for teaching physical education courses in a musical environment was developed, including five sections: (1) selection of teaching mode, (2) setting of teaching objectives, (3) teaching process and content arrangement, (4) teaching evaluation and (5) precautions. With the addition of a musical environment, sport performance can be enhanced by triggering students’ emotions and cognition.

The study presented in the paper focuses on the development of a psychological model for disabled athletes, leveraging artificial intelligence (AI) technologies. The research now investigates the psychological traits across a diverse array of disabled athletes, including those in both high-intensity and precision sports, broadening our understanding and applicability of the AI-driven psychological model, and examines how these traits differ from those of able-bodied individuals. The study proposes an emotional psychological model based on AI, which simulates the generation and fluctuation of emotions using a Differential Evolution (DE) algorithm. By simulating emotional states in response to external stimuli, the model aims to achieve emotional balance and stability, crucial for the mental well-being of athletes. The results suggest that AI can play a significant role in understanding and improving the psychological conditions of disabled athletes, ultimately enhancing their performance and quality of life. The research provides valuable theoretical insights and practical implications for integrating AI into the psychological training and support of disabled athletes.