AI-SUPPORTED FLIPPED LEARNING IN BREAST SELF-EXAMINATION TRAINING

IMPACT OF AN AI-SUPPORTED FLIPPED LEARNING MODEL ON NURSING STUDENTS' BREAST SELF-EXAMINATION KNOWLEDGE AND PERFORMANCE: A RANDOMIZED CONTROLLED TRIAL

The global increase in cancer cases has made breast cancer the second most common cancer after lung cancer and a primary health problem among women. Early diagnosis is the most critical factor in improving survival rates and quality of life in breast cancer. Breast self-examination (BSE), which enables individuals to notice changes in their own breast tissue during the early diagnosis process, is a low-cost and effective awareness method. It is essential that nurses, who play a key role in raising public awareness on this issue, and nursing students, who are the future healthcare professionals, have sufficient knowledge and practical skills in BSE. However, the literature shows that even if students have theoretical knowledge, their application rates are low. In this context, the "AI-Supported Flipped Learning" model, which goes beyond traditional methods and supports active learning, personalized feedback, and digital literacy, has the potential to be an innovative solution in nursing education. Objective: This study aims to evaluate the effect of AI-supported flipped learning model and traditional education on the knowledge levels and performance skills of nursing students regarding BSE knowledge and skills.

Inclusion Criteria:

• Being a second-year student in a nursing undergraduate program
• Not having previously received breast examination training
• Having signed the voluntary consent form

Exclusion Criteria:

• Having any health problem that would prevent continuing to work
• Requesting to withdraw from work voluntarily