Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

The workforce revolution: Reimagining work, workers, and workplaces for the future

“…The world is undergoing a profound transformation, wherein the fundamental nature of work (i.e., the responsibilities and tasks that people perform in exchange for remuneration), workers (i.e., the people performing work), and workplaces (i.e., the spaces where work is performed by workers) is being reshaped. This transformation is driven by externalities and trends such as: Groundbreaking technological advancements like Generative AI (e.g., ChatGPT; Dwivedi et al., 2023; Lim, Gunasekara et al., 2023) and virtual reality (e.g., metaverse; Dwivedi et al., 2022; Kraus et al., 2023); Increasing competitive pressures that necessitate new ways of thinking and functioning (e.g., the challenger approach; Lim, 2020); Shifting demographics with the rise of new generations (e.g., Generation Z or zoomers; Lim, 2022b) and intergenerational transitions (e.g., Generation X or baby boomers moving into the aging population and Generation Y or millennials becoming middle‐aged adults; Lim, Kumar et al., 2023); Evolving societal expectations such as corporate social responsibility or CSR (Castillo, 2022; Prasad et al., 2022), diversity and inclusion (Arora & Patro, 2021; Yilmaz et al., 2021), environmental social governance or ESG (Lim, Ciasullo et al., 2023), lifelong learning and upskilling (Lang, 2023), purpose‐driven work (Collins & Saliba, 2020; Jones‐Khosla & Gomes, 2023), sustainability (Lim, 2022a), and work‐life balance (Chigeda et al., 2022; Mello & Tomei, 2021; Naim, 2022); and Ongoing global crises or mega‐disruptions such as the COVID‐19 pandemic (Lim, 2021, 2023b) and the Ukraine‐Russia conflict (Lim, Chin et al., 2022), among others. …”

University students as early adopters of ChatGPT: Innovation Diffusion Study

“…ChatGPT is built on the GPT-3 family of large language models, deemed signi cant due to their extensive training on vast amounts of text data, from millions to billions of words (Tate et al, 2023). In AI's early years, the algorithmic focus was primarily on supervised and unsupervised learning (Dwivedi et al, 2023). Traditional AI algorithms require structured data for modeling and information processing.…”

“…Traditional AI algorithms require structured data for modeling and information processing. However, modern algorithms have advanced and can now process data in its most natural form and language (Dwivedi et al, 2023). Concurrently, there has been a surge in chatbot-related research (Lokman & Ameedeen, 2018).…”

“…Concurrently, there has been a surge in chatbot-related research (Lokman & Ameedeen, 2018). Chatbots typically utilize Natural Language Processing (NLP) to respond to user-generated queries (Dwivedi et al, 2023). Chatbots have evolved by integrating deep learning with language models, addressing various NLP-related challenges (Kushwaha, Kumar, and Kar, 2021).…”

“…ChatGPT has expanded chatbot capabilities in this context by incorporating deep learning and Generative Pre-Trained Transformer-based language models (Radford et al, 2018).Combining unsupervised pre-training with supervised ne-tuning, ChatGPT can effectively respond to user-generated queries (Doshi et al, 2023). The GPT-3 model consists of 175 billion parameters, focused on a diverse array of naturally occurring text sourced from the internet, such as webpages, books, research articles, and social conversations (Dwivedi et al, 2023).ChatGPT has proven to be a valuable resource in various elds, such as poetry, essay writing, business correspondence, research, software development, and testing (Tung, 2023). However, it has also been perceived as threatening content creation roles like teachers and content creators (Roose, 2023).…”