Deep Learning for Gender-Sensitive Pedagogy in High School Sociology: Opportunities and Challenges - Damanik, Fritz Hotman Syahmahita (Universitas Muhammadiyah Mataram, 2025)
Gender-sensitive pedagogy plays a crucial role in fostering equitable educational environments, particularly in high school sociology, where discussions on social structures and gender dynamics are central. However, traditional teaching methodologies often reinforce gender biases, limiting opportunities for inclusive learning. With advancements in artificial intelligence (AI) and deep learning, new educational tools have emerged that can help address these biases by providing personalized learning experiences, adaptive assessments, and real-time feedback. This study explores the role of deep learning in supporting gender-sensitive pedagogy through a systematic literature review. The research method follows a qualitative approach, utilizing academic journals, conference proceedings, policy reports, and review articles published within the last five years. The collected data is analyzed using Miles and Huberman’s framework, which includes data reduction, data presentation, and conclusion drawing to identify key themes and trends. The findings indicate that AI-powered tools can detect gender biases, enable adaptive learning, and enhance inclusivity in high school sociology education. However, challenges such as AI biases, ethical concerns, technological barriers, and resistance from traditional educational institutions persist. The study concludes that while deep learning presents significant opportunities for advancing gender-sensitive pedagogy, its effective implementation requires teacher training, policy support, and ethical oversight. Future research should focus on practical applications and long-term impacts of AI in gender-sensitive education.