Exploring EFL Teachers’ Experiences with AI-Based Language Assessment in Digital Classrooms
Keywords:
Artificial Intelligence, Phenomenology, EFL Teachers, AI-Based Assessment, Professional Identity, Digital PedagogyAbstract
Artificial intelligence (AI) has become a transformative force in education, reshaping how teachers assess and interact with learners in digital learning environments. Within English Language Education, the integration of AI-based assessment tools has expanded rapidly, yet little is known about how educators experience and interpret their engagement with these systems on a personal and professional level. Despite technological progress, limited research has examined the subjective meanings and lived experiences of teachers who navigate AI-mediated assessment practices. This study addresses that gap by asking how EFL teachers construct meaning, negotiate identity, and maintain pedagogical authenticity while working with AI-driven evaluation systems. Using a hermeneutic phenomenological approach, the study explores teachers’ lived experiences to uncover the existential dimensions of “being-a-teacher” in AI-mediated classrooms. The study employed purposive sampling to recruit twelve EFL teachers with at least three years of experience using AI-based assessment tools, ensuring that participants possessed rich, relevant insights aligned with the study’s phenomenological aims. Data were collected through in-depth semi-structured interviews with twelve EFL teachers and analyzed using Van Manen’s reflective method. The findings revealed four interrelated themes trusting and questioning the machine, negotiating pedagogical identity, experiencing ethical and emotional tensions, and reconstructing authenticity in assessment practices. These themes illustrate that AI integration is not merely a technological transition but a human and ethical transformation affecting teachers’ professional identities and relationships with knowledge. The study contributes to a more human-centered understanding of AI-mediated pedagogy and provides insights for teacher education, institutional policy, and future research on human–machine collaboration in education.
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