Maintaining Meaningful Human Interaction in AI-Enhanced Language Learning Environments: A Systematic Review
DOI:
https://doi.org/10.53286/arts.v6i3.2083Keywords:
Artificial intelligence, language learning, Human-computer interaction, adaptive learning, Technology integrationAbstract
This systematic review examines strategies for designing AI-enhanced language learning environments anchored in collaborative partnerships between humans and AI. The review involved searching multiple databases for relevant literature published between 2000-2023, applying inclusion/exclusion criteria, and coding articles according to a predefined scheme. A total of 10 studies were identified that addressed guidelines for structuring roles, coordinating AI with human priorities, assessing user perceptions, applying AI to personalized learning, or leveraging AI capabilities while maintaining central human involvement. Key findings indicate guidelines emphasize delineating roles between humans and AI through frameworks balancing autonomy and expertise. Techniques show potential for aligning AI with human input, though ensuring real-world coordination requires ongoing refinement. Research underscores generally positive user perceptions depending on individual attributes and initial adoption intentions. Personalized learning through AI modeling emerges as promising when guided by educators. Designing AI to enhance rather than replace teachers emphasizes collaborative problem-solving. Findings offer guidance on thoughtful AI integration respecting people as learning relationships evolve. Continued investigation refining coordinated approaches across contexts could help realize equitable AI-augmented models optimizing outcomes through empowered human partnerships as technologies progress. This provides direction for responsibly advancing the field to maximize AI's contributions to language education.
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