Frontiers in psychology · 2026
AI as a reflective partner in EFL speaking: a qualitative study informed by cyborg psychology.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Aldamen Hesham, Almashour Mohamad, Al-Deaibes Mutasim, Alsharefeen Rami
Аннотация
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.
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.