The widespread adoption of Generative AI (GenAI) tools by students across different educational levels highlights the need for them to develop robust GenAI literacy, including a working understanding of these systems' fundamental concepts, their limitations, and implications for responsible use. However, misconceptions about GenAI, such as perceiving these systems as mere search engines or database lookup systems, are commonly observed among students, while the availability of teaching resources remains fragmented, and learning objectives lack alignment. This Working Group aims to design pedagogical resources for computing science instructors, enabling them to develop students' GenAI literacy. To achieve this, the Working Group will first identify a concise set of GenAI literacy learning objectives informed by instructor experience, research literature, and community input, and subsequently design pedagogical resources aligned with these objectives.

Despite the growing demand for programming skills, introductory computer science courses (CS1) remain a significant hurdle for many students, often leading to high dropout rates. The considerable number of new concepts can be challenging for beginners, leading to misunderstandings and frustration within the learning process. Addressing the factors contributing to student dropout in CS1 is essential to improve retention rates. Therefore, this study analyzed the behavior of 747 students enrolled in a CS1 course at the University of Sydney during the second semester of 2023. Data from the university's learning management system (LMS), combined with student submission scores and demographic information, was used to identify early warning signs of dropouts and differentiate between successful and unsuccessful students. Students were grouped into three categories: i) non-completers (less than 50% at the final grade), ii) low completers ($<75 \%$), and iii) high completers ($>75 \%$). Five machine learning models were trained to identify the most important features contributing to course success. Our work illustrates that non-completers show distinct behavior from the start, particularly in lab performance, where they consistently underperformed compared to other groups. While dropout predictions can be made with an accuracy of 80% as early as week 2, later semester activities and assessments have a greater impact on final grades. Engagement in lab units proved to be crucial for student success, with non-completers exhibiting performance differences early on. These results underscore the importance of monitoring lab engagement to identify students at risk of dropping out and suggest that early intervention within the course framework could help mitigate attrition rates.

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.

Large Language Models represent a significant breakthrough in Natural Language Processing research and opened a wide range of application domains. This paper demonstrates the successful integration of Large Language Models into immersive learning environments. The review highlights how this emerging technology aligns with pedagogical principles, enhancing the effectiveness of current educational systems. It also reflects recent advancements in integrating Large Language Models, including fine-tuning, hallucination reduction, fact-checking, and human evaluation of generated results.

Problem planning is a fundamental programming skill, and aids students in decomposing tasks into manageable subtasks. While feedback on plans is beneficial for beginners, providing this in a scalable and timely way is an enormous challenge in large courses.

We present our work in progress, a real-time mixed reality communication system for remote assistance in medical emergency situations. 3D cameras capture the emergency situa-tion and send volumetric data to a remote expert. The remote expert sees the volumetric scene through mixed reality glasses and guides an operator at the patient. The local operator receives audio and visual guidance augmented onto the mixed reality headset. We compare the mixed reality system against traditional video communication in a user study on a CPR emergency simulation. We evaluate task performance, cognitive load, and user interaction. The results will help to better understand the benefits of using augmented and volumetric information in medical emergency procedures.

Serious games have an already well-known positive impact on students’ motivation and their learning experience. In computer science education a majority of games and approaches exist, that provide engaging environments for students. On the one hand, these can be games related to learning algorithmic thinking but on the other hand, games to learn coding. Besides single-player games, there are also multiplayer games where players compete against each other or collaboratively work on programming tasks. However, many existing multiplayer games offer pre-defined levels where teachers have limited flexibility and individuality for their students. Additionally, many existing game environments use competitive over collaborative approaches. In our project, we extended an existing game by a multiplayer mode where players work together on coding tasks. The game elements support many coding-related concepts but also computational skills such as sequencing, conditionals, loops, and also advanced topics such as concurrency or dependencies using meaningful levels and tasks. However, we conducted an evaluation including 41 participants in three workshops: two in-class activities with secondary school students and an online activity with computer science students. Within these workshops, the students collaboratively worked on coding tasks within the game environment. Thereby, we observed the communication between the students while working on the tasks. Additionally, we evaluated the students’ attitudes towards collaborative learning. We found out that the in-game chat is barely used while in-class activities, especially when the tasks require only low coordination between players. We also found out, that students learn from each other and are more motivated when working together. With our approach, we want to provide educators with a flexible game environment where students can collaboratively improve their coding skills while solving engaging tasks.

Questionnaires are important instruments to gather information from people in a wide range of application scenarios, such as analyzing the responses for evaluations, collecting preferences, or retrieving self-estimations in learning settings. However, based on the setting and design of a questionnaire participating can be boring or frustrating and consequently have a negative impact on results or disengage users. Literature reports negative findings in immersive simulations and learning games, where assessment and questionnaires are provided outside the learning experience. In this paper, we introduce an approach to integrate questionnaires into a game environment. Thereby, we want to provide learners with an engaging way to answer questions. To encourage them in participating, they receive rewards for completing questions. We performed two evaluations, an A/B study with 22 participants and an evaluation with 14 experts in subject-related fields. We could show that learners are more engaged in the in-game questionnaire and integrated questions make them more likely to respond. We could also identify strategies to obtain more reliable responses such as asking questions right after a task or including gamification elements. Findings can contribute to design more engaging applications or learning environments where evaluation and feedback have significance for both educators and learners.

Twitch is a live streaming platform that was originally designed for gamers to share their gaming experiences. The viewer culture on Twitch supports a lively exchange between streamers and viewers and also among viewers themselves. While the potential of Twitch for game tutorials and instructions has been explored, its potential for university lectures still appears to be undiscovered. In this paper, we discuss the potential of Twitch for teaching. We ran two different courses on Twitch, one for CS beginners, the other a game development course for students in a master's program. Evaluations with students show Twitch benefits, including increased engagement and interaction in chat and the ability to watch videos directly afterward. On the other hand, we also discovered several challenges, such as in-chat spam by bots. In this paper, we would like to discuss initial experiences from different perspectives and discuss best practices. Our aim in doing so, is to help other teachers decide whether Twitch might be a suitable online learning platform for their course.

The proposed project, sCool, is an adaptive mobile game designed for STEM education. In this work, we present a new iteration of sCool in efforts to further examine contributing factors of engagement and comprehension. The new developed game experience for acquiring object-oriented programming skills is divided into two parts: concept-learning and practical. In the concept-learning part, the users explore a virtual environment filled with puzzle-pieces hinting theoretical concepts, preparing the users for the practical part. Users need to solve a programming challenge in Python, directing game-objects on a checkerboard to certain fields. Conducting a study involving 39 school students and three teachers, we are able to successfully display an enhanced understanding of different programming concepts.