Enhancing Educational Materials: Integrating Emojis and AI Models into Learning Management Systems

Abstract

The integration of visual elements, such as emojis, into educational content represents a promising approach to enhancing student engagement and comprehension. However, existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation. This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes (DECOC), Long Short-Term Memory (LSTM) networks, and Multi-Layer Deep Neural Networks (ML-DNN) to identify optimal emoji placements within computer science course materials. The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content. A meticulously annotated dataset, comprising diverse topics in computer science, was developed to train and validate the model, ensuring its applicability across a wide range of educational contexts. Comprehensive validation demonstrated the system’s superior performance, achieving an accuracy of 92.4%, precision of 90.7%, recall of 89.3%, and an F1-score of 90.0%. Comparative analysis with baseline models and related works confirms the model’s ability to outperform existing approaches in balancing accuracy, relevance, and contextual appropriateness. Beyond its technical advancements, this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted (AI-assisted) tool that facilitates personalized content adaptation based on student sentiment and engagement patterns. By automating the identification of appropriate emoji placements, teachers can enhance digital course materials with minimal effort, improving the clarity of complex concepts and fostering an emotionally supportive learning environment. This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support. Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials, offering an innovative tool for fostering student engagement and enhancing learning outcomes. The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources, emphasizing scalability and adaptability for broader applications in e-learning.