ADOPTION READINESS AND PERCEIVED RELIABILITY OF GENERATIVE AI TOOLS IN SOFTWARE DEVELOPMENT EDUCATION

Abstract

Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) have proliferated so quickly that they have caused a revolution in computer science and software engineering education. The adoption readiness and perceived dependability of GenAI technologies, like GitHub Copilot and ChatGPT, among undergraduate students in an emerging educational ecosystem are examined in this study. This study states about all the aspects of how AI usage is impacting the software development. The current discrepancy between the high frequency of ad hoc student usage and the serious ethical and technical hazards noted in growing literature, together with the absence of formal institutional frameworks, serves as the foundation for this study. Data was gathered from a stratified sample of 150 undergraduate students in universities over several semesters and skill levels using a quantitative descriptive survey design. The study integrates aspects of perceived reliability and trust using an enhanced Unified Theory of Acceptance and Use of Technology (UTAUT) paradigm. It takes a proper look at the revolution caused in software engineering by the usage of AI. The findings show that although Effort Expectancy and Performance Expectancy are the main factors influencing adoption, there is still a significant "reliability gap" where students express doubts about the veracity of intricate logical outputs. The results similarly show a favourable correlation between perceived performance gains and the use of structured AI, but they also raise serious worries about "hallucinations" and security flaws in AI-generated code. The findings are properly analysed to see how and what impact is brought on the education by the integration of AI. In order to prevent the creation of new digital divisions, the study suggests that successful integration necessitates a shift from syntax-focused courses to logic-driven verification models, backed by open institutional policies and strong AI literacy initiatives.