Developing AI literacy through HRD: Preparing organizations for AI

Abstract: Artificial Intelligence (AI) is fundamentally transforming people’s lives, and digital workplaces require advanced skills (Ezziane, 2023). As AI becomes integral to modern work environments(Ismail & Hassan, 2019; Rymarczyk, 2020), its impact on employee performance and behavior varies based on individual capabilities (Cetindamar et al., 2022; Long & Magerko, 2020).The emerging concept of AI literacy is attracting multidisciplinary attention (Ali et al., 2021; Chai et al., 2020). AI literacy can be defined as a set of skills that enable people to use, evaluate, and collaborate with AI (Cetindamar et al., 2022; Long & Magerko, 2020). It is considered vital for individual and organizational success in the AI era, especially as younger generations educated in AI enter the labor market (Adams et al., 2023; Jang et al., 2022). Within HRD, AI literacy will become essential for both individuals and organizations. Those AI skills extend beyond mere understanding of AI but encompass the application, evaluation, creation, and even ethical dimensions of AI (Ng et al., 2021). The current literature requires a better understanding of the individual capabilities of AI (Cetindamar et al., 2022). Therefore, this paper aims to explore the development of AI literacy among employees and organizations from an HRD perspective. The research questions that guide this review are (1) “What are skills that comprise employees’ and organizations’ AI literacy?” and (2) “What can be the approaches to developing employees’ AI literacy?”
Method
To answer the research question, we reviewed and synthesized the literature on AI literacy using an integrative literature review (Torraco, 2016). This approach aims to synthesize and present a comprehensive view of a topic in an integrated way to generate new knowledge. This method is particularly valuable in answering research questions from immature fields by drawing concepts and theories from different disciplines (Snyder, 2019; Torraco, 2016). We chose an integrative literature review because it is effective when prior research is dispersed among different disciplines and has not been comprehensively analyzed and integrated. Literature was searched in two databases: Web of Science (WOS) and Scopus. Based on the following criteria: (1) AI literacy is the focus of the research, or AI literacy is an important factor in implications, and (2) The study examines AI literacy in adult or employee context. (3) The paper is written in English. A total of 18 articles were excluded due to irrelevance, and 125 pieces were selected, 67 of them were in the context of K-12. As the ken of HRD concerns adult learning (Swanson, 2009; Torraco & Lundgren, 2020), the author decides to exclude K-12 articles for final analysis. As a result, 55 articles were reviewed. Among the final article list, 19 of them are conference papers, 4 of them are editorial papers, and 32 of them are journal articles. Most of them were published after 2021, and this bibliometric data shows that AI literacy is a relatively new concept in literature.
Conceptual Framework
Technology Acceptance Model (TAM), proposed by Davis (1989), has been widely utilized to predict user acceptance of various technologies across different contexts until today (Casillo et al., 2020; Kashive et al., 2020). Scholars have made efforts to improve the theoretical functions of TAM. One of the most cited models was the Unified Theory of Acceptance and Use of Technology (UTAUT) that is a unified model of several TAMs proposed by different researchers (King & He, 2006; Venkatesh et al., 2003; Venkatesh & Davis, 2000).
Adopted from the TAM and UTAUT, capturing the emergence of AI technology, an AI acceptance model has been developed and validated (Gado et al., 2022)). The AI acceptance model suggests that students' attitude towards AI was most strongly predicted by their perceived usefulness and ease of use. The model demonstrated that the intention to use AI was predicted by the user's perceived ease of use, perceived usefulness, and social norm. Additionally, the user's perceived knowledge of AI was found to be a predictor for both intentions to use and attitude towards AI. Interestingly, the results also suggested that attitude toward AI mediated the relationship between perceived knowledge and attitude toward AI. Gado et al. (2022) also showed that perceived knowledge of AI has a statistical influence on the perceived usefulness and perceived ease of use. The perceived knowledge in the AI acceptance model can be extended to AI literacy. AI literacy not only includes one’s knowledge of AI but also one's ability to use it, evaluate it, and address its ethical issues.