Generative artificial intelligence (GenAI) is increasingly becoming a part of work practices across the technology industry and being used across a range of industries. This has necessitated the need to better understand how GenAI is being used by professionals in the field so that we can better prepare students for the workforce. An improved understanding of the use of GenAI in practice can help provide guidance on the design of GenAI literacy efforts including how to integrate it within courses and curriculum, what aspects of GenAI to teach, and even how to teach it. This paper presents a field study that compares the use of GenAI across three different functions - product development, software engineering, and digital content creation - to identify how GenAI is currently being used in the industry. This study takes a human augmentation approach with a focus on human cognition and addresses three research questions: how is GenAI augmenting work practices; what knowledge is important and how are workers learning; and what are the implications for training the future workforce. Findings show a wide variance in the use of GenAI and in the level of computing knowledge of users. In some industries GenAI is being used in a highly technical manner with deployment of fine-tuned models across domains. Whereas in others, only off-the-shelf applications are being used for generating content. This means that the need for what to know about GenAI varies, and so does the background knowledge needed to utilize it. For the purposes of teaching and learning, our findings indicated that different levels of GenAI understanding needs to be integrated into courses. From a faculty perspective, the work has implications for training faculty so that they are aware of the advances and how students are possibly, as early adopters, already using GenAI to augment their learning practices.
Democratizing Generative AI for Sustainable Competitive Advantage
ISEG – Lisbon School of Economics（里斯本经济管理学院）；INESC - ID（信息与系统研究所）；Instituto Superior NOVA Information Management and Management, Técnico, School (NOVA IMS), Universidade de Lisboa（里斯本大学技术学院新葡京信息管理与管理高等研究院）；Universidade NOVA de Lisboa（新里斯本大学）
Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Education
University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；Kent State University（肯特州立大学）；Pennsylvania State University（宾夕法尼亚州立大学）；The University of Alabama（阿拉巴马大学）；George Washington University（乔治华盛顿大学）；Pepperdine University（佩珀代因大学）；Universiti Malaya（马来亚大学）；Begum Rokeya University（贝古姆·罗姬娅大学）
Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；University at Buffalo（布法罗大学）
Context Collapse: Barriers to Adoption for Generative AI in Workplace Settings
Intel, Inc.（英特尔公司）；Data & Society Research Institute（数据与社会研究所）；University of Virginia（弗吉尼亚大学）；Oregon State University（俄勒冈州立大学）；Northwestern University（西北大学）
(Meta) Competences for Digital Practice: Educational Scenarios for the Workplace of the Future Exemplified by Building Information Modeling Work Processes
SCAN: A Decision-Making Framework for Effective Task Allocation with Generative AI
Independent Researcher, London, United Kingdom（伦敦，英国独立研究员）；School of IT and Engineering, Kazakh-British Technical University, Almaty, Kazakhstan（阿斯塔纳，哈萨克-英国技术大学信息与工程学院）
The Illusion of Understanding: How Middle-Schoolers Fail to Regulate Inquiry with ChatGPT in a Science Task
Hector Research Institute, University of Tübingen（蒂宾根大学赫克托研究所）；Department of Psychology, University of California Berkeley（加州大学伯克利分校心理学系）；Inria Research center, University of Bordeaux（波尔多大学法国国家信息与自动化研究所研究中心）
