Rooted in and beyond interaction: A systematic review of interactive affordances of chatbots for language learning amidst the rise of large language models - Yunfei Du; Barry Lee Reynolds (Acta Psychologica)
As chatbots evolve from scripted responders to reasoning companions, their role in language education is rapidly shifting. Yet, while their presence is expanding, a deeper understanding of how they interact—not just respond—with learners remains elusive. This systematic review repositions interaction as the core of chatbot-assisted language learning, drawing on 100 peer-reviewed studies published between 2009 and 2024. Through a three-phase grounded coding approach to extract and synthesize these literature, we identify four distinct interaction styles, such as interlocutor, narrator, entertainer, and facilitator, each shaping engagement across pedagogical, affective, and cognitive domains. We find that large language models (LLMs) have broadened the educational affordances of chatbots, particularly in writing, speaking, and emotional support. However, their potential to foster higher-order thinking remains largely untapped. Limitations such as hallucination, bias, and ethical risks are seldom addressed in practice, underscoring the need for more critical integration. We argue that effective use depends not only on technological sophistication, but on prompt design, emotional connection, and teachers as ethical gatekeepers.