Simone Opel

Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capable of solving most introductory programming tasks and are causing disruptions throughout the curriculum. These tools can write and explain code, enhance error messages, create resources for instructors, and even provide feedback and help for students like a traditional teaching assistant. In 2024, new research started to emerge on the effects of GenAI usage in the computing classroom. These new data involve the use of GenAI to support classroom instruction at scale and to teach students how to code with GenAI. In support of the former, a new class of tools is emerging that can provide personalized feedback to students on their programming assignments or teach both programming and prompting skills at the same time. With the literature expanding so rapidly, this report aims to summarize and explain what is happening on the ground in computing classrooms. We provide a systematic literature review; a survey of educators and industry professionals; and interviews with educators using GenAI in their courses, educators studying GenAI, and researchers who create GenAI tools to support computing education. The triangulation of these methods and data sources expands the understanding of GenAI usage and perceptions at this critical moment for our community.

Zusammenfassung: Aufbauend auf dem Positionspapier der Gesellschaft für Informatik zur Bedeutung von Künstlicher Intelligenz (KI) in der Bildung wird im Beitrag die Notwendigkeit diskutiert, Schüler*innen sowie Lehrkräfte auf die Chancen und Herausforderungen von KI vorzubereiten. Es werden verschiedene Ansätze zum Lehren und Lernen über und mit KI vorgestellt, darunter die Integration von KI in den Informatikunterricht, die Verwendung von Unplugged-Ansätzen und die Entwicklung von KI-bezogenen Kompetenzen in der beruflichen Bildung. Der Beitrag betont die Bedeutung von Informatik als Pflichtfach im allgemeinbildenden Unterricht. Abschließend werden die Konsequenzen von KI für die Bildung und die Notwendigkeit eines interdisziplinären Diskurses über die Gestaltung von Bildung in einer digital geprägten Welt diskutiert.

Generative AI (GenAI) has seen great advancements in the past two years and the conversation around adoption is increasing. Widely available GenAI tools are disrupting classroom practices as they can write and explain code with minimal student prompting. While most acknowledge that there is no way to stop students from using such tools, a consensus has yet to form on how students should use them if they choose to do so. At the same time, researchers have begun to introduce new pedagogical tools that integrate GenAI into computing curricula. These new tools offer students personalized help or attempt to teach prompting skills without undercutting code comprehension. This working group aims to detail the current landscape of education-focused GenAI tools and teaching approaches, present gaps where new tools or approaches could appear, identify good practice-examples, and provide a guide for instructors to utilize GenAI as they continue to adapt to this new era.

Most computing students enter the industry once they graduate. As future software engineers, they will be in powerful positions, making decisions that impact their personal lives, others, and society. Thus, preparing graduates for their careers is crucial by addressing ethical considerations, decision problems, and other concepts related to morals, values, and legal aspects (e.g., data protection, privacy, security, etc.) as part of computing curricula. In this paper, we propose the integration of data ethics into computing programs and provide a framework for an ethics module, including relevant competency-based learning objectives. The proposed module is based on a curricular analysis of all 71 German data science degree programs focusing on ethics courses. The course contents and competency goals were analyzed and classified based on their cognitive complexity. As the results proved the lack of competency-based learning outcomes, we designed observable competency goals, meaning knowledge, skills, and dispositions taken in the context of a task. In addition, we provide suggestions for contents, pedagogical instructions, and assessments in such a course. The proposed module serves as a first draft and resource to support other educators aiming to design such a course and who are willing to integrate it into computing curricula.