Leveraging Data Science and Generative AI to Explore Human Behaviour and Health Issue

Integration of newer data science concepts with the human behavioral science perspective reveals an entirely new dimension in the evolution of personality psychology with respect to preparing an individual for a profession. This study intends to understand the influence of personality on learning behavior and workplace readiness of students with personality traits according to the Big Five Personality Model: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The main intention of this study was to identify patterns through which certain traits hinder or facilitate adaptation, cooperation, and decision-making in working environments. Data were collected through customized Google Forms surveys targeted at students from various academic backgrounds. The questions aimed to establish the five principal traits, along with additional questions to capture subtle behavioral aptitudes in academic and social settings. The collected data were cleaned and preprocessed in Python using its pandas, NumPy, and scikit-learn modules to effectuate efficient data treatment. K-Means clustering, an extremely popular unsupervised learning technique, was then applied to identify natural groupings in the dataset and associate them with different personalities. Moreover, an application of generative AI was developed to generate individualized feedback on the personality clusters identified. This would support the simulation of behavior-driven scenarios and readiness responses in different roles, so that one may visualize the varying ways in which potential performances within a workplace by individuals with different trait profiles might be realized. Being able to produce scalable, context-aware, and adaptive content, Gen AI opens new vistas for simulating real-life interactions and delivering developmental feedback to students. These insights provide firm indications of behavior trends and their correlates to learning outcome patterns and work behaviors. For example, a higher Conscientiousness score aligns with better organizational and time management skills in the workforce, whereas a higher score of Neuroticism associates with reduced adaptability and ability to handle stress under pressure. The model assesses and classifies students in terms of readiness for the world of work, which in turn can provide a scalable solution for assessment of behavior and personality online for institutes and organizations in the future. Other scope for improvements will look into clustering algorithms, finer-grained behavioral indicators, and advanced capabilities along the lines of Principal Component Analysis (PCA) and deep learning for enhancing classification performance. The changing scope of Gen AI shall additionally be channelized toward real-time adaptive personality development tools and professional guidance.