Lessons for GenAI Literacy From a Field Study of Human-GenAI Augmentation in the Workplace

2025-02-01

AI Summary

This study addresses the challenge of designing generative AI (GenAI) curricula in higher education that align with evolving workplace demands. Through cross-functional fieldwork across product development, software engineering, and digital content creation—grounded in human–AI augmentation theory and practice-oriented needs modeling—the research systematically maps the GenAI application gradient, ranging from no-code API invocation to domain-specific fine-tuning and deployment. It introduces, for the first time, a layered GenAI literacy framework, empirically demonstrating a strong correlation between users’ computational literacy and their modes of GenAI engagement. The study’s contributions include: (1) role-aligned, differentiated curriculum integration strategies; (2) scalable faculty development pathways; and (3) a validated, actionable framework enabling synergistic implementation of GenAI education across general education and discipline-specific programs—thereby providing an evidence-based foundation for talent development in the GenAI era.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Generative AI
• Workplace Application
• Educational Curriculum Design

Innovation

• Generative Artificial Intelligence
• Curriculum Development
• Industry-specific Training