## 1 Introduction
Its natural language conversational capabilities have also made it a popular teaching tool, facilitating interactive engagement between teachers and students [12]. Particularly, ChatGPT has shown significant potential in providing instant feedback and error correction guidance. While earlier studies have utilized automated feedback tools to offer immediate feedback on error identification and correction, these systems often lack the depth needed to support further learning [13]. Most feedback mechanisms in such systems provide only single-level responses or fixed feedback types tailored to specific errors, leaving students unable to address more complex problems effectively [14,15]. To address these challenges, this study integrated generative AI technology to develop ITS-CAL (Intelligent Tutoring System for Coding and Learning), a system designed to respond to students' questions instantly and provide targeted guidance. The intelligent teaching assistant functionality leverages natural language processing technology to comprehend students' inquiries and deliver appropriate responses. However, given that generative AI is capable of resolving most programming problems and even directly generating program code, directly providing hints that closely resemble correct answers risks fostering students' dependency on the system. This dependence may lead to students simply submitting generative AI-provided code as their own, thereby undermining the original intent of the system as a tool for fostering critical thinking [16]. To address this concern, the study employed a step-by-step approach to delivering hints. Initially, the system provides a description of the process structure. If students are unable to write code based on this structure, the next step is to present the explanation using mathematical formulas, catering to students who may excel in numerical reasoning. If mathematical formulas still do not enable students to apply appropriate syntax to solve the problem, the system then provides pseudo code. As pseudo code is straightforward and easier to interpret, it helps students comprehend algorithmic logic and data structures. This multi-layered feedback mechanism allows the intelligent teaching assistant to alleviate the workload of instructors and teaching assistants while enabling students to receive immediate help, thereby enhancing learning continuity and efficiency. Summarizing the research findings, the intelligent teaching system ITS-CAL, driven by an LLM, demonstrates significant application value in programming education. It enables the analysis and exploration of students' learning behaviors, outcomes, and subjective experiences before and after its implementation. The following subsections elaborate on the three main research focuses:  
1. Students' Usage Behaviors and Patterns Understanding students' usage behaviors is fundamental to optimizing learning system functionalities. When using ITS-CAL, students may exhibit diverse patterns of interaction due to variations in their learning backgrounds, programming knowledge levels, and individual needs. For instance, some students may frequently rely on the Hint function to seek guidance, whereas others may prefer the Debug function for correcting programming errors. Analyzing these patterns provides insights into the distinct learning needs of various student groups, enabling the system's functions to be refined to better address individual requirements. Furthermore, investigating the types and depth of questions posed by students offers a deeper understanding of their conceptual grasp and challenges during the learning process. For example, students with lower programming knowledge may tend to ask basic syntax-related questions, while those with intermediate or advanced knowledge levels might focus on topics like logic design or program integration. These insights not only reflect students' learning stages but also aid in devising targeted strategies for learning assistance. 2.