DUTIES: Education and Humanities International Journal

AI as a Metacognitive Mirror: How Students Use AI to Monitor and Repair Reading Comprehension Breakdowns

2026-01-24T21:16:47+07:00

The increasing availability of artificial intelligence (AI) tools has transformed how EFL students engage with academic reading, yet little is known about how AI shapes learners’ metacognitive processes during reading. This qualitative study conceptualizes AI as a metacognitive mirror and investigates how EFL students use AI to monitor and repair reading comprehension breakdowns. Data were collected from undergraduate EFL students at a public university through academic reading tasks, screen recordings, think-aloud protocols, AI interaction logs, and stimulated recall interviews. Thematic analysis revealed that students used AI to externalize comprehension monitoring by confirming interpretations and articulating sources of confusion. AI also supported comprehension repair through strategy-specific and iterative regulation, enabling learners to request paraphrases, examples, and simplified explanations in response to perceived difficulties. However, the findings also indicate tensions between productive metacognitive support and uncritical reliance on AI, particularly when learners accepted AI-generated explanations without verification. The study contributes to AI-assisted reading research by shifting attention from learning outcomes to metacognitive processes and learner agency. Pedagogical implications highlight the importance of guiding students toward reflective and responsible AI use to support academic reading comprehension.

When Feedback Must Be Human: Pedagogical Resistance to AI in EFL Speaking Classrooms

2026-01-29T22:35:50+07:00

The rapid advancement of artificial intelligence (AI) has intensified debates about its role in language education, particularly in providing automated feedback. While existing research has largely focused on teachers’ acceptance and use of AI tools, limited attention has been given to teachers’ deliberate decisions not to use AI in specific pedagogical contexts. This qualitative study investigates EFL teachers’ pedagogical resistance to AI-mediated oral feedback in speaking classrooms. Drawing on in-depth semi-structured interviews and reflective accounts from EFL teachers, the study employs thematic analysis to explore how teachers explain their resistance and the pedagogical values underlying their decisions. The findings reveal that resistance is grounded in teachers’ concerns about interactional immediacy, learner affect, dialogic engagement, and ethical responsibility. Oral feedback is viewed as a relational practice that requires human sensitivity to timing, tone, and emotional cues, which teachers perceive as inadequately addressed by current AI technologies. Rather than signaling technological reluctance, pedagogical resistance emerges as an enactment of teacher agency and professional judgment. The study contributes to critical discussions on AI integration in education by reframing non-use as a principled pedagogical choice and highlighting the need for context-sensitive, human-centered approaches to AI use in EFL speaking instruction.

Deciding When and How to Use AI in EFL Speaking Instruction: Evidence from Surveys and Teacher Interviews

2026-01-15T11:14:42+07:00

The integration of Artificial Intelligence (AI) in English as a Foreign Language (EFL) instruction has expanded rapidly, yet little is known about how teachers make pedagogical decisions regarding AI use in speaking classrooms. This study investigates how EFL teachers explain and justify their decisions about when and how to use AI tools in speaking instruction, employing a convergent mixed-methods design. Quantitative data were collected through a survey of 24 teachers, analyzed using descriptive statistics, while qualitative insights were obtained from semi-structured interviews with 12 teachers, analyzed thematically. Findings reveal that teachers adopt a selective, context-sensitive approach, prioritizing AI for pronunciation practice and fluency exercises, where immediate feedback and structured practice are most effective. Teachers exercise professional judgment to balance AI affordances with pedagogical objectives, contextual constraints, and ethical considerations, ensuring that AI supplements rather than replaces human interaction. The study highlights the multidimensional nature of teacher agency in AI-supported speaking instruction and provides practical implications for professional development and curriculum design. Future research could examine AI integration across other language skills, diverse educational contexts, and longitudinal impacts on learners’ speaking proficiency and autonomy.

Teachers’ Beliefs and Decisions Regarding Artificial Intelligence Use in Education

2026-01-29T22:50:07+07:00

The increasing integration of artificial intelligence (AI) into educational settings has raised important pedagogical and ethical questions, particularly regarding how teachers understand and decide to use AI in their instructional practices. This qualitative study explores teachers’ beliefs about AI use in education and examines how these beliefs shape their instructional decisions. Drawing on semi-structured interviews and thematic analysis, the study reveals that teachers hold nuanced and evaluative beliefs about AI, viewing it as a supportive pedagogical tool while expressing concerns about overreliance, learning quality, and professional responsibility. Rather than adopting AI uncritically, teachers exercise agency through selective integration and pedagogical regulation of AI use, particularly in relation to assessment and student accountability. Teachers’ decisions are shown to be context-sensitive and grounded in humanistic values that emphasize ethical judgment and meaningful teacher–student interaction. The findings suggest that AI use in education is best understood as a belief-driven and value-laden practice rather than a purely technical innovation. This study contributes to educational and humanities-oriented discussions by foregrounding teachers’ professional judgment in shaping responsible and pedagogically sound AI integration.

AI Game-Based Learning in Low-Resource Classrooms: Teachers’ Innovation Under Constraint

2026-02-05T15:20:02+07:00

Abstract: The integration of artificial intelligence into game-based learning has been widely promoted as a means of enhancing engagement and personalization in education. However, existing research largely assumes well-resourced classroom conditions, offering limited insight into how such approaches are enacted in constrained contexts. This qualitative multiple case study explores how teachers design and implement AI-supported game-based learning in low-resource classrooms and how contextual constraints shape their pedagogical reasoning and innovative practices. Drawing on interviews, classroom observations, teaching artifacts, and stimulated recall, the study foregrounds teachers’ agency in mediating AI use under conditions of limited infrastructure, device access, and institutional support. The findings reveal that teachers adopt selective and hybrid approaches to AI game-based learning, combining AI-generated content with offline activities and teacher-led scaffolding. Contextual constraints function not merely as barriers but as catalysts for reflection, improvisation, and ethical decision-making related to fairness and inclusivity. This study contributes to AI in education research by reframing innovation as a situated, teacher-driven process and by highlighting the importance of context-sensitive approaches to AI-supported pedagogies, particularly in underrepresented low-resource educational settings.