Human-machine teaming perspective on college English speaking classroom design: Targeting the enhancement of students' willingness to communicate

Abstract

This review article examines the integration of human-machine teaming principles into college English speaking classroom design with the specific objective of enhancing students' willingness to communicate. As educational technology continues to evolve, the convergence of human expertise and artificial intelligence capabilities presents unprecedented opportunities for language learning pedagogy. This comprehensive review synthesizes current research across multiple domains including second language acquisition theory, educational technology, human-computer interaction, and artificial intelligence to propose a novel framework for classroom design. Through systematic analysis, the research identifies key HMT components that directly impact WTC: adaptive feedback mechanisms, empathetic AI interactions, collaborative task design, and personalized learning environments. The findings indicate that well-designed human-machine partnerships can significantly reduce speaking anxiety, increase learner autonomy, and enhance communicative competence. The review proposes a multi-layered theoretical framework that positions educators as orchestrators of human-AI collaboration rather than sole content deliverers, while AI systems serve as adaptive learning partners providing real-time feedback, conversation practice, and anxiety-reducing interventions. Key recommendations include implementing transparent AI systems that build trust, designing collaborative speaking tasks that leverage both human creativity and AI analytical capabilities, and developing teacher training programs for effective HMT integration. This work contributes to the growing body of knowledge on AI-enhanced language education and provides practical guidelines for educators seeking to modernize speaking instruction through human-machine collaboration.