AI-Powered Virtual Laboratories: Enhancing Chemistry Education through Intelligent Stimulation Frameworks

The recent advances in the realm of Artificial Intelligence (AI) have made it possible to create intelligent virtual laboratory environments, which could overcome some of the main drawbacks of traditional chemistry teaching methods, such as limited laboratory access, potential hazards associated with practical experimentation, and prohibitive costs of equipment and materials.This paper reports on the development and evaluation of a new model of intelligent virtual laboratories powered by advanced AI algorithms.This research describes how AI tools can be utilized to improve learners' conceptual and experimental competencies in the domain of chemistry.Specifically, this work presents a design of a hybrid virtual laboratory environment featuring adaptive learning models based on reinforcement learning and learner analytics, physics-informed stimulations driven by computational chemistry algorithms, and natural language processing engines for interactive tutoring and automatic response evaluation.As part of this research, a quasi-experiment was designed and implemented to compare the efficacy of AI-driven virtual lab experience and traditional laboratory practices in enhancing students' chemistry knowledge.Participants were undergraduate students taking a required chemistry course, and they completed pre-test and post-test questionnaires and several laboratory tasks.Despite these advantages, challenges related to model interpretability, simulation fidelity, and infrastructure scalability are identified.The study underscores the potential of AI-powered virtual laboratories as scalable, data-centric educational tools capable of augmenting or partially substituting physical laboratory experiences.Future work focuses on integrating multimodal interfaces, such as augmented reality (AR), and improving model generalization across diverse chemistry curricula.