We report on the use of a GenAI-based chatbot as a replacement for the traditional project specification document in a second-year object-oriented programming course. Instead of receiving a written specification, students interacted with a fictional non-technical client, Tio Bob, who answered questions about the expected behavior of a board game they had to implement. The chatbot used an LLM-based backend with safeguard features (e.g., code blocking), and the platform logged all exchanges for instructor analysis. We present a preliminary analysis of these logs, which included 13,816 interactions across a cohort of 97 students (median 106 per student) with an average prompt length of 14.1 words.

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.

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.

The advent of Large Language Models (LLMs) has created multiple challenges for the Computer Science Education Community. This research project aims at integrating LLMs into Object-Oriented Programming courses, by generating and evaluating new teaching methodologies and tools suitable for this paradigm's specificities.

Recent studies show that AI-driven code generation tools, such as Large Language Models, are able to solve most of the problems usually presented in introductory programming classes. However, it is still unknown how they cope with Object Oriented Programming assignments, where the students are asked to design and implement several interrelated classes (either by composition or inheritance) that follow a set of best-practices. Since the majority of the exercises in these tools' training dataset are written in English, it is also unclear how well they function with exercises published in other languages.