Efektivitas Model Edukasi M-Chat berbasis ChatGPT untuk Pendeteksian Autis pada Anak - Mardiah Mardiah; Musdalina Musdalina (Program Studi S1/DIII-Keperawatan Universitas Imelda Medan, 2025)
Early detection of autism in children aged 16–30 months is crucial for timely intervention and improved developmental outcomes. This study aims to analyze the effectiveness of the ChatGPT-based M-CHAT educational model in enhancing parental knowledge and assisting with the completion of the Modified Checklist for Autism in Toddlers (M-CHAT). This model is designed as an interactive assistant that provides explanations about autism, the function of the M-CHAT, and guidelines for self-completion and initial interpretation of screening results. The research method employs a quasi-experimental approach with a pre-test and post-test design involving 60 parent respondents. The research instrument consisted of a 1-5 Likert scale questionnaire measuring three domains: knowledge about autism and M-CHAT, self-completion ability, and satisfaction and usability of the model. Validity was analyzed using Pearson correlation, reliability using Cronbach's alpha, and effectiveness was tested using a paired sample t-test. The results showed that all questionnaire items were valid and reliable. The average pre-test scores in the domains of knowledge, completion, and usability significantly increased in the post-test (p < 0.001). Thus, the ChatGPT-based M-CHAT educational model is effective in improving parents' understanding, self-completion ability, and user satisfaction and can be used as a tool for early autism screening.