Smart Assistants for Multimodal Learning Analytics Systems in Language Acquisition: A Systematic Review

Sammanfattning

This paper presents a systematic review of Smart Assistants integrated with Multimodal LearningAnalytics (MMLA) for enhancing language learning outcomes. The advent of AI-driven SmartAssistants, including platforms like ChatGPT, Google Assistant, and Alexa, presents unprecedentedopportunities for personalised language education. Our review critically examines existing literature toassess the linguistic capabilities and software engineering features of these systems, identifyingpotential gaps and opportunities for integration within MMLA frameworks. By focusing on how thesetechnologies can support language learning through natural language communication, feedbackmechanisms, and adaptability in language complexity, we provide recommendations for futureimplementations and research. Our findings suggest that while Smart Assistants offer considerablebenefits in terms of scalability and interactive learning, challenges remain in terms of integrationcomplexity and ensuring pedagogical effectiveness. We conclude by proposing directions for futureresearch aimed at optimising Smart Assistant functionalities for more nuanced and effective languagelearning applications within diverse educational settings.