CAET: Code Analysis and Education Tutor.

The introduction of OpenAI's ChatGPT in 2022 kickstarted the release of Generative Artificial Intelligence (GAI) applications to the public domain. Such chat interfaces are based on large language models (LLMs) and possess a vast array of abilities spanning conversation, the writing and debugging of code, the writing of papers, and the creation of images, music, and songs. With students now having access to a myriad of GAI tools, academia has been permanently altered. Our proposed system, named Code Analysis and Education Tutor (CAET), integrates GAI into early Computer Science education by providing students with an ethical alternative to existing GAI tools. CAET is designed to assist students with programming tasks in a manner tailored to their individual needs without jeopardizing the integrity of their learning. A point of uniqueness from existing works is CAET's ability to display or hide generated code based on its pertinence to the problem at hand. After subjecting multiple GAI models to common programming errors and queries, we settled on OpenAI's GPT-3.5 Turbo model due to its comprehensive capabilities and cost-effectiveness. Overall, CAET underscored the model's conversational dynamics and provided insights for creating a more personalized learning experience for students in an introductory computer science course.