Implementation of Deep Learning for AI-Based Adaptive Chemistry Learning: Literature Review Method

Abstract

This literature review study aims to analyze and synthesize the implementation of Deep Learning in adaptive and inclusive chemistry learning based on Artificial Intelligence (AI). The background of this research is based on the demands of the digital era and Ki Hajar Dewantara's educational philosophy regarding guidance that humanizes humans, which requires pedagogical innovation to overcome the abstract nature of chemistry subjects. Chemistry, which involves microscopic concepts and high visual representation, is often a source of learning difficulties for students. Therefore, Deep Learning is presented as a solution to create a personalized and responsive learning system. The method used was a systematic literature study by analyzing ten relevant articles published between 2015 and 2025, with the main keywords “Deep Learning,” “Artificial Intelligence/AI,” and “Chemistry Learning.” The synthesis of the ten articles shows that the implementation of AI and Deep Learning has three main impacts: (1) Improved Understanding of Chemical Concepts: AI/Deep Learning, through differentiated learning systems, virtual laboratories, and digital tutors, effectively addresses the abstract nature of chemical concepts (A2, A4). This technology facilitates the visualization of molecular structures and provides targeted automated feedback (A10), thereby improving student understanding, (2) Strengthening Teacher Competence: AI integration assists teachers in providing simulations, digital media (A6), and rapid feedback, which significantly improves teachers' pedagogical abilities in designing innovative learning (A1, A3), (3) Development of 21st Century Skills: The use of Deep Learning (A8) and Generative AI (A10) models has been proven to develop multiple creativity, critical thinking, and collaboration among students in the context of chemistry, in line with sustainability demands (SDGs). However, consistent implementation challenges include infrastructure limitations, low digital literacy, and the need for teacher training (A1, A7). Overall, this study concludes that Deep Learning and AI are important and strategic components in shaping an adaptive, personalized, efficient, and inclusive chemistry learning environment that is capable of aligning the complexity of the material with the unique needs of each student in the digital age.