AI technologies, characterized by their ability to analyze large datasets, learn from patterns, and adapt to new information, are revolutionizing traditional approaches to teaching and learning (Jiang et al., 2022; Zhang & Aslan, 2021). In higher education, AI has introduced innovative tools and methodologies that enhance the learning experience, streamline administrative processes, and provide personalized support to students (Crompton & Burke, 2023; Zhang & Aslan, 2021). Notable AI platforms that have emerged significantly in recent years include ChatGPT, which facilitates interactive and adaptive learning experiences by generating personalized responses and feedback (Lo, 2023), and Google Gemini, which offers advanced AI-driven analytics and content generation capabilities (Imran & Almusharraf, 2024).

In recent years, AI-driven platforms are increasingly used to facilitate adaptive learning, wherein educational content is tailored to meet individual students' needs and learning styles (Zhai et al., 2021). Recent literature highlights several areas where AI has shown promise in education (Crompton & Burke, 2023; Lin et al., 2023; Yekollu et al., 2024). For instance, adaptive learning platforms powered by AI may provide personalized feedback and tailored educational content, enhancing student engagement and learning outcomes (Lin et al., 2023; Yekollu et al., 2024). Additionally, AI-driven virtual teaching assistants, offer real-time support and guidance, facilitating a more interactive and responsive educational environment (Audras et al., 2022). AI tools have also been used to analyze large datasets of student performance, identifying areas for improvement and predicting student success, which can guide instructional strategies and support services (Crompton & Burke, 2023). These advancements underscore the potential of AI to augment traditional education methods, providing dynamic and individualized learning experiences that better prepare students for clinical practice.

In the context of nursing education, the integration of AI holds transformative potential (Rodriguez-Arrastia et al., 2022; Reed et al., 2023; Tam et al., 2023). Traditionally, nursing education combines theoretical instruction with practical clinical experiences, the latter being essential for developing clinical skills and decision-making abilities. The incorporation of AI technologies into nursing education processes could introduce innovative opportunities to enhance learning experiences through real-time feedback and personalized learning (Buchanan et al., 2021; Hwang et al., 2024). In a recent review, AI-based chatbots were identified as a key tool in nursing education. Their primary applications included enhancing learning and skill development, supporting simulations, providing educational assistance, and facilitating evaluation and assessment (Labrague & Al Sabei, 2024). AI-driven simulations may create immersive environments where students can practice skills in a controlled setting, improving clinical readiness and understanding (Benfatah et al., 2024a; Jung, 2023). Beyond simulations, AI tools such as adaptive learning platforms and virtual teaching assistants are revolutionizing nursing education by offering tailored learning experiences and ongoing support (Chang et al., 2022; Xin et al., 2021). Available studies have shown that nursing students hold a favorable attitude towards the use of AI in nursing education (Labrague et al., 2023a) and demonstrate readiness to embrace these tools (Labrague et al., 2023b). This enthusiasm further supports the potential for AI technologies to enhance learning experiences and contribute to the evolution of nursing education.

Despite the promising advancements, there are notable gaps in the current literature. While individual studies highlight the benefits of AI in simulations and instructional tools (Buchanan et al., 2021; Xin et al., 2021), there is a lack of synthesis that consolidates these findings and assesses their collective impact on long-term educational outcomes and their integration into comprehensive nursing curricula. Therefore, this scoping review aimed to address these gaps by systematically exploring and categorizing AI-based teaching strategies in nursing education. By mapping the existing literature, the review seeks to provide a comprehensive overview of the current state of AI integration in nursing education, identify key themes, and evaluate their impact on educational outcomes. This synthesis of individual studies will fill the existing gap by offering a cohesive analysis of AI's role in nursing education, contributing to a more nuanced understanding of how these technologies can be effectively integrated. This knowledge is crucial for nurse faculty and policymakers aiming to leverage AI to enhance teaching methods, improve student outcomes, and prepare nursing professionals for the complexities of modern healthcare environments.