Published Paper Details:

Abstract Purpose: The purpose of this study is to investigate the impact of personality traits and external factors on the use of Chat GPT, a generative AI tool, among university students. Understanding the psychological and technological factors of AI use is critical for optimizing learning outcomes and increasing digital engagement as it becomes more integrated in higher education. Design/Methodology/Approach: The research takes a quantitative approach, with a standardized questionnaire delivered to university students. The study is based on the Big Five Personality Traits framework and known technology adoption frameworks, including the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The data will be evaluated using statistical methods such as SPSS to investigate the correlations between personality traits, perceived utility, simplicity of use, and behavioral intent to use Chat GPT. Findings: According to preliminary expectations, personality traits such as openness and conscientiousness are predicted to correlate positively with the perceived usefulness and simplicity of use of Chat GPT. Additionally, social influence and favorable environments are predicted to have a substantial impact on adoption behavior. These findings are intended to emphasize the complex interaction of individual psychological elements and external technical cues in affecting user behavior. Practical Implications: The findings of this study can help educators, developers, and institutions create AI-based instructional tools that are suited to different personality types and user preferences. Institutions may boost student engagement, increase learning efficiency, and promote responsible AI use in academic contexts by matching implementation techniques with student behavior and personality profiles. Originality/Values: This study offers new insights by combining personality psychology with technology adoption frameworks to evaluate Chat GPT usage in higher education. It addresses a significant research vacuum by concentrating on both system-related aspects and user-centric psychological motivations, providing a more comprehensive understanding of AI adoption behavior.