AI as a reflective partner in EFL speaking: a qualitative study informed by cyborg psychology.

INTRODUCTION

Speaking anxiety and limited opportunities for spontaneous English interaction remain persistent constraints for many EFL learners in Middle Eastern university settings. This interpretive qualitative study examines how a reflection-enhanced AI speaking design shapes learners' affect, motivation, and future self-concepts.

METHODS

Twenty-five Jordanian undergraduates completed a 12-week module in which they practiced weekly speaking with ChatGPT in voice mode (official interface; model label recorded at the time of use). A fixed system prompt configured a supportive, non-judgmental speaking partner, introduced the weekly topic, and triggered two brief in-task reflective questions. Data comprised weekly reflective journals, semi-structured interviews, and saved interaction transcripts.

RESULTS

Using reflexive thematic analysis, we identified three patterned orientations in learners' accounts and interaction traces: initial curiosity with cautious engagement, increasing emotional regulation and psychological safety during speaking, and strengthened future-oriented identity work linking present effort to imagined competent English-speaking selves.

DISCUSSION: Interpreted through cyborg psychology and complementary motivational frameworks, these findings suggest that embedding reflection into human-AI dialogue can support willingness to communicate and identity-related development alongside speaking practice.