A research team at Koç University School of Nursing has successfully developed and validated a Turkish adaptation of the Artificial Intelligence Self-Efficacy Scale, providing higher education institutions with a robust instrument to measure students' confidence in utilizing artificial intelligence. Led by Associate Professor Remziye Semerci Şahin and Assistant Professor Seda Güney, the study was conducted between May and November 2025 across public and private universities in Türkiye, encompassing 284 participants. Findings were published in the International Journal of Human–Computer Interaction. As AI-driven learning platforms and automated tutoring systems become staples of modern academia, assessing student readiness requires more than tracking technical proficiency. The researchers identified a shortage of validated measurement tools tailored to educational contexts and adapted the original scale through rigorous linguistic back-translation, expert content review, and statistical validation. Psychometric analyses confirmed a four-factor structure encompassing Assistance, Anthropomorphic Interaction, Comfort with AI, and Technological Skills. The validated scale demonstrated strong internal consistency with a Cronbach alpha of 0.937, explaining nearly 74 percent of total variance. Survey data revealed that while most participants had prior exposure to AI applications, formal training remained limited. Nevertheless, a majority expressed moderate self-assessed knowledge and a strong interest in structured AI education. The study emphasizes that AI self-efficacy extends beyond technical competence to include psychological comfort and adaptive confidence during human-machine interactions. According to the authors, the adapted tool offers universities a practical instrument for evaluating educational interventions, tracking longitudinal confidence shifts, and designing targeted literacy initiatives. The findings reinforce that successful integration of artificial intelligence into academic curricula depends on parallel investments in both technical instruction and user confidence. Future research can leverage the scale to refine pedagogical strategies and ensure students are fully prepared for AI-enhanced learning environments.