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Abstract Lectures in nursing education aim to develop key competencies in Assessment, Diagnosis, and Implementation. However, classroom distractions and lack of timely feedback can hinder students’ ability to focus, retain information, and engage in deep learning cycles. New approaches are needed to support both students and educators in enhancing the effectiveness of teaching and learning. This proof-of-concept study explores how artificial intelligence (AI) can be integrated with handwritten student notes to generate feedback and support learning cycles in nursing education. The study analysed handwritten notes from 31 students following a 3-h cardiac nursing lecture. Notes were digitised and evaluated using ChatGPT, which was prompted to extract keywords, assign scores to predefined learning objectives, and generate personalised feedback. The dataset was processed and visualised using Python to identify patterns in student comprehension. Frequently identified keywords included Data Collection , Clinical Assessment , Medical Treatment , ABCDE approach , and Patient Experience . The average keyword score across student notes ranged from 7.61 to 8.94 (on a 1–10 scale), with the highest scores linked to assessment-related concepts. Variability in keyword frequency suggested areas such as Diagnosis and Implementation may require instructional reinforcement. AI tools can support nursing education by providing structured, personalised feedback that reinforces student learning and helps identify areas for improvement. This study demonstrates the potential of AI to enhance lecture-based learning through repeated, feedback-driven learning cycles. For educators, AI may offer valuable insights into student comprehension, highlighting areas where instructional strategies could be refined. While the initial findings are promising, further validation is required to confirm the effectiveness and generalisability of this approach across broader educational contexts.