This study explores the factors influencing the adoption and use of ChatGPT among university students in higher education. It examines the behavioral determinants associated with the use of AI chatbots, focusing on ChatGPT's self-learning capabilities, personalization, and novelty value. The research highlights that ChatGPT's self-learning capabilities enhance knowledge acquisition and application, positively impacting individual impact. Personalization and novelty value are also found to be significant factors, influencing perceived benefits and behavioral intention. However, privacy concerns, technophobia, and guilt feelings are identified as potential barriers to adoption. Despite these challenges, innovativeness is shown to positively influence behavioral intention and actual behavior. The study provides valuable insights for developers and educators, aiding in the effective integration of AI chatbots in educational settings. The findings are supported by a comprehensive survey of 273 university students, analyzed using Partial Least Squares (PLS) methodology. The results confirm the positive correlation between self-learning, knowledge acquisition, and individual impact, while also addressing common method bias and validating the constructs' reliability and validity.This study explores the factors influencing the adoption and use of ChatGPT among university students in higher education. It examines the behavioral determinants associated with the use of AI chatbots, focusing on ChatGPT's self-learning capabilities, personalization, and novelty value. The research highlights that ChatGPT's self-learning capabilities enhance knowledge acquisition and application, positively impacting individual impact. Personalization and novelty value are also found to be significant factors, influencing perceived benefits and behavioral intention. However, privacy concerns, technophobia, and guilt feelings are identified as potential barriers to adoption. Despite these challenges, innovativeness is shown to positively influence behavioral intention and actual behavior. The study provides valuable insights for developers and educators, aiding in the effective integration of AI chatbots in educational settings. The findings are supported by a comprehensive survey of 273 university students, analyzed using Partial Least Squares (PLS) methodology. The results confirm the positive correlation between self-learning, knowledge acquisition, and individual impact, while also addressing common method bias and validating the constructs' reliability and validity.