Abstract:

This paper presents a systematic review of Smart Assistants integrated with Multimodal Learning Analytics (MMLA) for enhancing language learning outcomes. The advent of AI-driven Smart Assistants, including platforms like ChatGPT, Google Assistant, and Alexa, presents unprecedented opportunities for personalized language education. Our review critically examines existing literature to assess the linguistic capabilities and software engineering features of these systems, identifying potential gaps and opportunities for integration within MMLA frameworks. By focusing on how these technologies can support language learning through natural language communication, feedback mechanisms, and adaptability in language complexity, we provide recommendations for future implementations and research. Our findings suggest that while Smart Assistants offer considerable benefits in terms of scalability and interactive learning, challenges remain in terms of integration complexity and ensuring pedagogical effectiveness. We conclude by proposing directions for future research aimed at optimizing Smart Assistant functionalities for more nuanced and effective language learning applications within diverse educational settings.

Keywords:

Smart assistants, Multimodal Learning Analytics, Language Acquisition, Artificial Intelligence.