Animal-vehicle collisions, roadkilled animals, and human health: A scoping review protocol.

Artificial intelligence (AI) is progressively being incorporated into multiple sectors, with its use in educational contexts emerging as a notable area of interest. Although its origins date back to 1956 (Cristianini ), AI systems have demonstrated advancements in task performance previously associated with human cognition, including providing support to innovation, reasoning, decision‐making, and problem‐solving (Sharma and Sharma ). In higher education, AI has driven a paradigm shift over the past 5 years (Chu et al. ). This is especially true for healthcare education, where emerging technologies offer AI‐enabled solutions such as predictive analytics and AI teaching bots (Chan and Zary ; Liaw et al. ). This integration of AI, however, carries several educational and clinical implications. In contemporary nursing practice, two recent state‐of‐the‐art AI technologies are Predictive AI and Generative AI (Harrington ; Lansdowne et al. ). Predictive AI uses existing data, such as patient information, to forecast future events or patterns. In contrast, Generative AI (GenAI) draws on learned patterns from large datasets to generate new textual, visual, or audio content when prompted by users (Gunawan, Aungsuroch, Marzilli, et al. ; Lansdowne et al. ). The advent of GenAI has led to a multitude of new technologies such as Wordtune, Scribe, and Claude AI. Among these, Chat Generative Pretrained Transformer (ChatGPT) is the archetypal GenAI tool, with human‐like intelligence that generates content based on existing data (Tam et al. ). Its debut on 30 November 2022 has since popularized AI technologies across academic and clinical settings (Hawk et al. ; Tam et al. ). Presently, these GenAI tools are easily accessible to the general public, with some offered at no cost. The continued evolution of these tools is expected to refine our interactions with information and technology. Notwithstanding its promising applicability, controversies surround GenAI use in academia and clinical settings. Judicious use of GenAI supports nursing clinicians in their clinical duties, such as decision‐making, diagnostic queries, and care planning, thereby optimizing work efficiency and clinical outcomes (Nilsen ; O'Connor et al. ). Moreover, GenAI‐based technologies not only aid students in scholarly research and academic writing but also enable pedagogical approaches such as simulation‐based learning, improving their clinical competencies and development of soft skills such as language and innovative thinking (Castonguay et al. ; Fawaz et al. ; Sun and Hoelscher ). Such potential benefits accordingly form the basis for the advocacy of some nursing educators and clinicians to integrate GenAI into nursing education and clinical practice (Hawk et al. ; Park et al. ). Despite these benefits, GenAI use is not without concerns. The misuse of GenAI may lead to adverse repercussions that outweigh its benefits, as underscored by moves by some universities to ban students from using them (Deshpande and Szefer ; Lund and Wang ).