Framing Human–AI Collaboration in Language Education

Abstract

The increasing use of generative artificial intelligence in language education has been accompanied by an increasing tendency to describe human-AI interaction as “collaboration.” This paper examines that framing by first clarifying what collaboration entails and then considering the extent to which human-AI interaction aligns with these requirements. Drawing on work in collaborative learning, human-AI teaming, and workplace collaboration, the paper identifies seven commonly cited features of collaboration: shared goals and purpose, interdependence and coordination, reciprocal communication, distributed accountability, complementary expertise, calibrated trust, and acknowledged contribution. While human-AI interaction in language education displays surface similarities to collaboration, particularly in terms of task interdependence and functional complementarity, it differs in more fundamental respects. In particular, human-AI interaction lacks shared purpose, reciprocal understanding, and distributed accountability, all of which are central to established conceptions of collaboration. These distinctions matter for how teachers, learners, and researchers interpret AI use and for how responsibility, evaluation, and learning are understood in AI-mediated activity. The paper argues that greater conceptual precision around collaboration is necessary if claims about human-AI relationships in language education are to remain theoretically grounded and pedagogically meaningful.