Introductory programming (CS1) courses often struggle to support students' understanding of program execution. While visualizations can make execution processes explicit, their effectiveness depends on design and context, and empirical evidence for AI-generated visualizations remains limited. We propose Generated Animated Traces (GATs), AI-generated, analogy-based, narrated animations that coordinate source code, execution state, and conceptual analogies. We conduct a study at two institutions in CS1 courses (Python N=961; Java N=151) comparing GATs to textual explanations. We measure immediate learning performance and experience, end-of-course engagement and exam performance. Results show that GATs can yield selective benefits for immediate learning, but benefits are context-dependent and short-term. We observe that GATs' influence on performance is moderated by learner engagement profiles. This finding underscores the importance of personalized approaches.

When students work on self-paced, unsupervised practical assignments in Machine Learning courses, teachers have little visibility into the difficulties students encounter. In our Machine Learning course, students completed a practical assignment using JELAI, an AI-supported programming environment. The recorded student-AI interactions were classified into pedagogically meaningful categories and visualized in a teacher-facing dashboard. An initial evaluation showed that teachers found the dashboard useful, while also identifying areas for improvement. We describe our experience and discuss the potential and challenges of using student-AI interactions to improve teaching practice.

The in-IDE learning format represents a novel way of teaching programming to students entirely within an industry-grade IDE, allowing them to learn both the language and the necessary tooling at the same time. In this tutorial, we will teach the audience everything they need to know to create in-IDE courses and analyze how the students are working in them. In the first part of the tutorial, the audience will get to know the JetBrains Academy plugin that allows creating courses for IntelliJ-based IDEs such as IntelliJ IDEA and PyCharm. The participants will develop their own simple courses with theory, programming tasks, and quizzes, as well as employ some LLM-based features like automatic test generation. In the second part, we will learn how to use another plugin to collect code snapshots and the usage of IDE features of students when they are solving the tasks. Finally, the participants will solve tasks in their own course while using the data gathering plugin, and we will show them how to process and analyze the collected data. As the outcome of the tutorial, the audience will know how to create in-IDE courses, track the students' performance and analyze it, and will already have their own simple course and a dataset that can be expanded or used for further research.

Many universities digitize exams or the process of grading the exams. This potentially allows for faster grading, is less labor intensive and less error-prone. But are the grades produced by online grading consistent with how we grade on paper? In this paper we present preliminary results of the comparison between scores given by grading online and grading on paper.

Detecting fraud in digital assessment is currently done by human proctor, that observes recordings of the exam. This is costly, tedious and time consuming process. In this paper we present preliminary results on automated video proctoring, which has the potential to significantly reduce manual effort and scale-up digital assessment, while retaining good fraud detection.