The Concept of Deep Learning and Its Implementation in Character-Based Learning - Rasyad, Muhammad Ghaffar; Suherman, Aris; Duryat, Masduki; Ali, Moh (CV. Strata Persada Academia, 2025)
This study aims to explore the concept and implementation of Deep Learning (DL) in the context of character-based education. Amid the growing urgency of character education in shaping a generation with integrity, advancements in information technology present new opportunities to design more adaptive and contextual learning approaches. DL, as a branch of artificial intelligence, possesses the capability to analyze large-scale digital data and automatically identify behavioral patterns and expressions of character values. This research employs a literature review using a descriptive qualitative approach, analyzing scientific literature from journals and academic publications published between 2015 and 2025. The findings indicate that DL can be utilized to detect learners’ behaviors, emotional expressions, and moral responses across digital media in a contextual manner, thereby supporting formative assessments and data-driven interventions in character education. However, challenges such as algorithm interpretability, data bias, and ethical concerns remain significant. Therefore, the study recommends the application of Explainable AI (XAI) frameworks and multidisciplinary collaboration to ensure that DL is implemented in ethically responsible and pedagogically meaningful ways. These findings offer both theoretical and practical contributions to the development of character learning models aligned with the demands of the digital age.