Artificial intelligence (AI) has emerged as a transformative technology in the fields of education and healthcare (Sharma et al., 2024). In education, AI facilitates learning processes, enhances access to information and provides personalized learning experiences (Sharma et al., 2024). In practice-based disciplines such as nursing and midwifery, AI integration holds significant potential for developing professional knowledge and skills (O’Connor, 2022). These technologies not only facilitate access to theoretical knowledge but may also contribute to the development of critical professional competencies, such as problem-solving and clinical decision-making skills (O’Connor, 2022). AI supports nursing and midwifery students in preparing for their professional careers and participating in data-driven healthcare services (Kranz and Abele, 2024, Lin et al., 2025). Furthermore, the ability of AI technologies to personalize learning processes, enhance motivation and support analytical thinking can foster the development of students' self-directed learning (SDL) skills (Namjoo et al., 2023). Self-directed learning is considered foundational for developing higher-order cognitive skills such as critical thinking, clinical reasoning and problem-solving in nursing education (Dunk and Craft, 2025, Garrison, 1997). Therefore, enhancing SDL may indirectly support students' effectiveness in clinical decision-making processes. Contemporary educational philosophies and theories emphasize the necessity of implementing new teaching-learning strategies, methods and techniques in a student-centered learning environment. This perspective considers student-centered education a fundamental prerequisite and students' SDL skills should be examined and enhanced based on empirical data. Moreover, SDL is regarded as a critical competency for students in healthcare education, as it plays a key role in professional success and improving efficiency in patient care (Garrison, 1997). Nurse educators must acknowledge the guidance and support students may require at different stages of learning, as this is crucial for developing SDL skills (Dunk and Craft, 2025). In this context, AI presents significant opportunities for developing innovative approaches to support learning processes (Ma’amor et al., 2024). In parallel, constructivist approaches which prioritize active participation, concept-based curricula and critical thinking have been successfully applied in nursing education to support SDL, problem-solving and clinical reasoning skills (Brandon and All, 2010).

Several studies in the literature highlight the potential benefits of AI in education. For instance, Sumengen et al. (2025) examined the impact of AI literacy on academic performance, emphasizing that AI tools facilitate access to information, enhance learning motivation and improve students' independent learning skills. Similarly, El-Sayed et al. (2025) analyzed the positive effects of AI-based tools on nursing students, noting that they enhance awareness, foster innovative thinking and boost students' confidence in their abilities. Furthermore, their findings suggest that AI-based tools play a significant role in improving problem-solving skills. Hashish and Alnajjar (2024) explored how digital tools support learning processes and discussed the potential of AI to optimize individualized learning experiences. However, a substantial portion of the existing literature focuses on the general impact of AI technologies on education. There is limited research on the role of AI literacy and attitudes toward AI in applied disciplines such as healthcare education, particularly among nursing (O’Connor, 2022) and midwifery students (Kranz and Abele, 2024). This study aims to address this gap in the literature by examining the relationships between AI literacy, attitudes toward AI and SDL skills among nursing and midwifery students.