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Google Flow for Task-Based Multimodal Language Learning: Pedagogical Applications and Challenges

Research output: Contribution to journal › Journal article › peer-review

Abstract

This tech review explores Google Flow, an AI-powered filmmaking platform, as a potentially transformative tool for multimodal language learning. By generating text, image, and audio-synchronized video, Flow can empower educators to create highly contextualized and personalized learning materials that highlight specific linguistic and pragmatic features. Furthermore, it may further shift learners from passive consumers to active content producers within task-based frameworks, fostering collaborative design and critical reflection. However, the platform presents distinct challenges regarding equitable access, technical reliability, and ethical concerns like deepfakes and cultural thinness. Ultimately, Flow serves best as a multimodal simulator requiring critical comparison, human oversight, and integration with authentic communication.
Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalRELC Journal
DOIs
Publication statusE-pub ahead of print - 30 Sept 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 4 - Quality Education
    SDG 4 Quality Education

User-Defined Keywords

  • Multimodal Language Learning
  • Task-based Language Instruction
  • Generative AI
  • AI video generation
  • Google Flow

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