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.

Automated feedback generation plays a crucial role in enhancing personalized learning experiences in computer science education. Among different types of feedback, next-step hint feedback is particularly important, as it provides students with actionable steps to progress towards solving programming tasks. This study investigates how students interact with an AI-driven next-step hint system in an in-IDE learning environment. We gathered and analyzed a dataset from 34 students solving Kotlin tasks, containing detailed hint interaction logs. We applied process mining techniques and identified 16 common interaction scenarios. Semi-structured interviews with 6 students revealed strategies for managing unhelpful hints, such as adapting partial hints or modifying code to generate variations of the same hint. These findings, combined with our publicly available dataset, offer valuable opportunities for future research and provide key insights into student behavior, helping improve hint design for enhanced learning support.

In-IDE learning became a popular approach, integrating programming education with professional development tools in a seamless environment. Kotlin Notebook extends this concept by enabling highly interactive lessons within an industrial IDE while leveraging its capabilities, such as code quality inspections or refactorings. Kotlin Notebook structures programming content into interactive sections, enhancing both engagement and comprehension. This talk explores the combination of in-IDE learning and Kotlin Notebook with the integration of LLMs to create a powerful tool for interactive learning within an industrial-grade IDE.

Debugging is an essential skill in programming education. Current debugging approaches lack interactivity and personalization for students. To address this gap, we introduce an AI Debugging Assistant integrated into JetBrains IDEs. The tool analyzes the students' errors in real-time and guides them through the debugging process by recommending breakpoints and explaining them on each step. This poster invites discussion on the effectiveness of the AI Debugging Assistant and its implications for programming education.

Project-Based Learning (PBL) emphasizes real-world problem-solving and critical thinking, while in-IDE learning integrates education directly into professional Integrated Development Environments (IDEs), promoting focus and skill development. This paper introduces the Kotlin Onboarding in-IDE project-based course, which combines the advantages of both approaches. Covering CS1 concepts in Kotlin, it targets students with prior programming experience who are adopting Kotlin as a secondary language. Published on JetBrains Marketplace and integrated into university curricula, the course has received positive feedback and shown high engagement.

GenAI is playing an increasingly important role in computing courses at all levels, offering new opportunities to support teaching and learning. However, using GenAI effectively raises important concerns regarding trust, academic integrity, and broader social and ethical dimensions. This Working Group was formed to report on the current state of the art in using GenAI in upper-level computing courses to aid educators. The working group will undertake a methodological review of published work and solicit input from the computing educational community as part of the report.

Collecting data from students solving programming tasks is valuable for both researchers and educators. Such data can be used to analyze student behavior, identify errors and misconceptions, and for many other purposes. In this work, we propose KOALA, a configurable tool to collect student data using JetBrains IDEs. This tool collects code snapshots and IDE interactions, converts them into the ProgSnap2 format, and provides visualization analysis.

The rise of AI tools is reshaping computer science education, shifting the focus from coding skills to teaching students how to effectively use these technologies. Understanding students' mental models and fostering computational and metacognitive skills are now essential, as over-reliance on AI can weaken critical thinking. This panel explores how a human-centered approach can balance these challenges, sharing strategies to optimize learning while addressing the risks of cognitive offloading in an AI-driven world.

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.

LLMs change the landscape of software engineering, and the question arises: “How can we combine LLMs with traditional teaching approaches in computer science?”. In this work, we propose to teach students in a low-code environment of code generation, developing not only their coding but also decomposition and prompting skills.

In this work, we developed an algorithm for detecting code quality issues in the templates of online programming tasks, validated it, and conducted an empirical study on the dataset of student solutions. The algorithm consists of analyzing recurring unfixed issues in solutions of different students, matching them with the code of the template, and then filtering the results. Our manual validation on a subset of tasks demonstrated a precision of 80.8% and a recall of 73.3%. We used the algorithm on 415 Java tasks from the JetBrains Academy platform and discovered that as much as 14.7% of tasks have at least one issue in their template, thus making it harder for students to learn good code quality practices. We describe our results in detail, provide several motivating examples and specific cases, and share the feedback of the developers of the platform, who fixed 51 issues based on the output of our approach.

In this paper, we present Lupa --- a platform for large-scale analysis of the programming language usage. Lupa is a command line tool that uses the power of the IntelliJ Platform under the hood, which gives it access to powerful static analysis tools used in modern IDEs. The tool supports custom analyzers that process the rich concrete syntax tree of the code and can calculate its various features: the presence of entities, their dependencies, definition-usage chains, etc. Currently, Lupa supports analyzing Python and Kotlin, but can be extended to other languages supported by IntelliJ-based IDEs. We explain the internals of the tool, show how it can be extended and customized, and describe an example analysis that we carried out with its help: analyzing the syntax of ranges in Kotlin.

In software engineering, it is not enough to simply write code that only works as intended, even if it is free from vulnerabilities and bugs. Every programming language has a style guide and a set of best practices defined by its community, which help practitioners to build solutions that have a clear structure and therefore are easy to read and maintain. To introduce assessment of code quality into the educational process, we developed a tool called Hyperstyle. To make it reflect the needs of the programming community and at the same time be easily extendable, we built it upon several existing professional linters and code checkers. Hyperstyle supports four programming languages (Python, Java, Kotlin, and Javascript) and can be used as a standalone tool or integrated into a MOOC platform. We have integrated the tool into two educational platforms, Stepik and JetBrains Academy, and it has been used to process about one million submissions every week since May 2021.

The process of writing code and use of features in an integrated development environment (IDE) is a fruitful source of data in computing education research. Existing studies use records of students' actions in the IDE, consecutive code snapshots, compilation events, and others, to gain deep insight into the process of student programming.