Nowadays, artificial intelligence (AI) has the strong potential to revolutionize education and training by enabling a personalized learning experience. Through advanced facial recognition (AFR) and emotion analysis tools (EAT) can be generated collaborative learning methods including Computer-Aided Language Learning (CALL) or innovative teaching approaches such as Project-Based Learning (PBL). This study investigates the applicability of AFR in educational contexts and training environments by analyzing students’ emotional responses to various video content and teaching styles. Two types of participants were included in this study, so that each exposed to soothing, humorous, or violent video stimuli, and instructional materials delivered through traditional, visual and gamified teaching style feedback emotional states. Emotional feedback was recorded using a real-time FER system. Both participants manifested negative or neutral emotional state in response to traditional and violent content, while visual and gamified materials exposed pronominally positive affective responses. Results indicate that visual and gamified methods generate a stronger positive emotional engagement sustained by high rate score related to perceive utility and understanding. These findings demonstrate and highlight the relevance of affective technologies in customizing the learning process based on emotional feedback, supporting the evolution and smart development of emotionally intelligent educational systems.