@inproceedings{KieslerOpelThorbruegge2024, author = {Kiesler, Natalie and Opel, Simone and Thorbr{\"u}gge, Carsten}, title = {With Great Power Comes Great Responsibility - Integrating Data Ethics into Computing Education}, series = {Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1}, booktitle = {Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1}, publisher = {ACM}, address = {New York, NY, USA}, doi = {10.1145/3649217.3653637}, pages = {471 -- 477}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{PratherLeinonenKiesleretal.2024, author = {Prather, James and Leinonen, Juho and Kiesler, Natalie and Benario, Jamie Gorson and Lau, Sam and MacNeil, Stephen and Norouzi, Narges and Opel, Simone and Pettit, Virginia and Porter, Leo and Reeves, Brent N. and Savelka, Jaromir and Smith, David H. and Strickroth, Sven and Zingaro, Daniel}, title = {How Instructors Incorporate Generative AI into Teaching Computing}, series = {Proceedings of the 2024 on Innovation and Technology in Computer Science Education Vol. 2}, booktitle = {Proceedings of the 2024 on Innovation and Technology in Computer Science Education Vol. 2}, publisher = {ACM}, address = {New York, NY, USA}, doi = {10.1145/3649405.3659534}, pages = {771 -- 772}, year = {2024}, abstract = {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.}, language = {en} }