Semantic Frame Analysis via Artificial Intelligence: Applications in Digital Platforms for Foreign Language Learning

Abstract

This research explores the integration of semantic frame analysis, rooted in cognitive linguistic theory, with artificial intelligence (AI) techniques for enhancing foreign language learning on digital platforms. Semantic frames provide structured cognitive schemas that organize meanings and contexts, aiding learners in understanding language usage in authentic situations. The study investigates how AI-powered Natural Language Processing (NLP) can automate semantic frame identification, extraction, and application within interactive language learning environments. By leveraging AI methodologies such as deep learning algorithms and annotated linguistic corpora (e.g., FrameNet), the research proposes a practical model that enables digital platforms to offer context-rich linguistic interactions. A case study demonstrates the effectiveness of the proposed system in enhancing learners' vocabulary retention, comprehension skills, and contextual interpretation abilities. Additionally, the paper addresses the challenges related to computational complexity, polysemy, and accuracy of automatic frame annotation, suggesting a hybrid approach combining human expertise with machine learning to optimize learning outcomes. The findings contribute significantly to educational technology by presenting an innovative approach to digital language education, emphasizing cognitive linguistic insights and AI-driven personalization. Finally, recommendations are made for future research, particularly in multilingual semantic frame analysis, adaptive learning technologies, and broader applications in cognitive educational frameworks.