Generative AI as a Conditional Job Resource: Examining ELT Teacher Well-being and Critical AI Literacy through the JD-R Model

M. Talha Isik — Wed, 11 Mar 2026 00:00:00 +0300

The English Language Teaching (ELT) profession faces a systemic crisis of well-being characterized by high rates of burnout and administrative intensification. While Generative AI (GenAI) offers the technological affordance to automate monotonous tasks and alleviate these pressures, its integration into the teacher's workflow presents a complex psychological paradox. Grounded in the Job Demands-Resources (JD-R) Theory, this qualitative phenomenological study investigates the lived experiences of 16 ELT professionals across diverse institutional contexts in Turkey. The findings reveal that GenAI functions not as a static benefit but as a Conditional Job Resource; its capacity to enhance well-being is contingent upon the teacher’s Critical AI Literacy (CAIL). The analysis uncovers an efficiency paradox, where the reduction of manual labor often leads to a treadmill effect of work intensification rather than psychological recovery. Furthermore, the integration of GenAI instigates a fundamental shift in professional identity from creator to curator, introducing a new cognitive Verification Burden. The study distinguishes between high-agency Architects, who utilize CAIL to maintain pedagogical sovereignty, and Imposters, who experience efficiency as a threat to their competence. The research concludes that for GenAI to serve as a sustainable resource for well-being, the profession must cultivate human-in-the-loop competencies that empower teachers to navigate the boundary between automation and agency.

A Corpus-Based Evaluation of Lexical Coverage in A2-Level EFL Coursebooks

Ömer Faruk Kaya, Aylin Han — Wed, 04 Mar 2026 00:00:00 +0300

This article reports a corpus-based study of A2-level English as a Foreign Language (EFL) coursebooks, focusing on their lexical coverage, frequency-based vocabulary distribution, and cross-linguistic lexical overlap. Textbook corpora compiled from student books were analysed using the New General Service List (NGSL), the NGSL supplementary word list, and the New Academic Word List (NAWL) to establish vocabulary load and coverage in relation to the 95% and 98% comprehension thresholds. The results show that the coursebooks rely heavily on high-frequency vocabulary. Combined coverage from the NGSL bands, supplementary list, and NAWL ranges from 94.47% to 95.89%, indicating that the materials support comprehension with some instructional assistance, while remaining below the level associated with largely independent reading. NGSL-only coverage remains high (92.15%–93.93%), whereas academic vocabulary contributes minimally. Analysis of Turkish–English cross-linguistic overlap reveals that cognates constitute a small proportion of the lexical input, while false cognates occur at consistently higher rates across materials. These results suggest minimal variation in lexical demands across publishers and provide empirical evidence to inform textbook selection, materials development, and vocabulary pedagogy for A2-level learners.

Language Education and Technology Journal

Generative AI as a Conditional Job Resource: Examining ELT Teacher Well-being and Critical AI Literacy through the JD-R Model

A Corpus-Based Evaluation of Lexical Coverage in A2-Level EFL Coursebooks