This thesis addresses the challenge of supporting novice programmers in the GenAI era without undermining fundamental skill acquisition. Using digital activity logs, self-reported characteristics, and GenAI usage patterns from an introductory programming course, it develops models that capture student learning behaviors. These models are then used to create personalized AI-based intervention strategies that guide students toward independent problem-solving.

Introductory programming courses remain challenging for many students, which motivates educators to adopt gamification to enhance engagement and learning. More recent work explores adaptive gamification, where game elements and task flow are tailored to individual learners. A key requirement for such adaptation is the ability to predict student success on upcoming tasks. Using a dataset of task attempts collected from a gamified introductory programming activity, we examine the predictive value of coarse-grained knowledge components, task difficulty, and dynamic student performance features. The results show that behavioral signals are substantially more informative than task properties: a student's prior success history and their position within a lesson sequence are the strongest predictors of future correctness. Although advanced topics such as file handling and structures are associated with increased failure rates, their impact is secondary to students' evolving engagement patterns. These findings highlight the role of momentum and practice effects in gamified programming environments and suggest that adaptive systems should prioritize real-time learner progression when providing instructional support. Dataset and the code for our experiments is available at https://osf.io/cajby.

Introductory programming courses present significant challenges for novice learners, often leading to frustration and difficulty in identifying learning gaps.This research aims to develop an AI-driven tool that provides personalized guidance, moving beyond traditional "one-size-fits-all" approaches.Recognizing the limitations of relying solely on digital interaction logs in the era of generative AI, we explore the integration of student personal characteristics and fine-grained programming interactions to predict learning behavior and performance.We will investigate how to accurately predict student outcomes early in the semester, analyze the dynamics of learning behaviors, and design an AI-assisted tool to recommend tailored learning materials and feedback.Our goal is to foster effective learning and mitigate the risks associated with over-reliance on general-purpose AI, ultimately enhancing knowledge retention and problem-solving skills.

Introductory programming courses are widely known for their difficulty among students.Success in courses is commonly measured in the form of final grades, which might not capture the challenges students face during their learning process.In this paper, we predict students' success and their future compiler errors based on previously made errors.Furthermore, we examine the effect of applying two clustering techniques before making the predictions and identify key weeks and errors that have the greatest impact on predictions.Experimental results show that students' compiler errors observed through the semester are an important predictor of students' achievement and future struggles.Predictions are further improved using sentence encoder-generated embeddings with K-Means algorithm.Our study suggests that students' errors, particularly the most recent ones, enable meaningful clustering that enhances performance prediction after only three weeks of the semester.