The rapid integration of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, Blackbox.AI, and Microsoft Copilot, into computing education has transformed how students learn programming languages like C#. These tools enhance learning by offering immediate code generation and support; however, they challenge academic integrity by facilitating potential misuse, such as submitting AI-generated code as original work. This study explores the use of Generative Artificial Intelligence (GenAI) in C# programming courses through four research questions, utilising a mixed-methods approach with 368 questionnaire responses, code examination, and grade comparisons. Results reveal that ease of use drives model preference, with ChatGPT being the most favoured. Generative Artificial Intelligence (GenAI) code exhibits detectable structures, and grading analysis shows it consistently outperforms human-written code, with Blackbox.AI leading, followed by ChatGPT and Microsoft Copilot. However, Generative Artificial Intelligence (GenAI) accessibility poses integrity risks, necessitating robust detection and assessment strategies. This research contributes to computing education by thoroughly examining Generative Artificial Intelligence (GenAI) C# code and proposing practical measures, such as code interviews and process documentation, to balance Generative Artificial Intelligence (GenAI) benefits with equitable evaluation, ensuring students develop authentic programming skills. These findings offer actionable insights for educators adapting to AI-driven education landscapes.