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On April 5, 2017, at 06:09:12 UTC, a shallow-focus earthquake with a moment magnitude (Mw) of 6.1 struck the Alghur region in northeastern Iran. Understanding how fault structures influence rupture processes and how satellite-based Interferometric Synthetic Aperture Radar (InSAR) observations can improve seismic hazard models remains a key challenge in earthquake research. This study employs Sentinel-1 Terrain Observation with Progressive Scans SAR (TOPS) InSAR data to estimate co-seismic and post-seismic ground deformation associated with the Alghur earthquake. We conducted time-series InSAR analyses to detect displacement patterns and produce high-resolution deformation maps, complemented by field-based structural investigations to identify and characterize major fault traces. Results reveal localized deformation along the southwestern block of the Alghur fault, with fault-plane solutions indicating crustal shortening consistent with the tectonic regime of the eastern Alborz range, in agreement with USGS observations. The interferometric analysis indicates uplift of up to 9 cm in the northeastern fault block, suggesting a complex deformation mechanism. This study demonstrates the effectiveness of Sentinel-1 InSAR combined structural analysis for quantifying earthquake-induced ground deformation and understanding fault behavior in tectonically active regions. The graphical abstract provides a concise visual overview of the study by integrating key geographic, methodological, and analytical components. Regional satellite imagery and geological maps position the study area in northeastern Iran, emphasizing the tectonic setting and the epicentral location of the 5 April 2017 Alghur earthquake. The central methodological framework outlines a sequential workflow comprising data acquisition, Sentinel-1 SAR pre-processing, DInSAR-based deformation analysis, field structural investigations, and the final integration of remote sensing and field-derived results. This workflow demonstrates how co-seismic and post-seismic deformation signals were extracted from SLC data, transformed into interferograms, and subsequently converted into LOS displacement maps. Complementary field photographs, structural measurements, and geological mapping validate fault orientations and kinematic characteristics through direct on-site observations. The final deformation products, particularly the DInSAR displacement maps, highlight the principal finding of up to 9 cm of uplift in the northeastern fault block, underscoring the complexity of the earthquake-induced deformation pattern. Sentinel-1 InSAR mapped co- and post-seismic deformation of the 2017 Alghur quake. Localized slip detected along the southwestern block of the Alghur fault. Up to 9 cm uplift observed in the northeastern fault block from InSAR data. Integrated InSAR and field evidence improved the understanding of fault behavior.
Background: Despite growing interest in artificial intelligence (AI)-mediated second or foreign language (L2) learning, little is known about the role of AI-driven informal digital learning of English (AI-IDLE) in shaping learners' communicative intention and motivation. Furthermore, how L2 learner traits such as grit may influence the relationship between AI-IDLE and communicative outcomes remains largely underexplored.Purpose: This study examined the association between AI-IDLE and two communicative variables - L2 willingness to communicate (WTC) and speaking motivation - with a particular focus on the mediating role of L2 grit.Methods: Participants were 244 English as a foreign language (EFL) learners (123 males, 121 females). Structural equation modeling was employed to examine the direct and indirect associations among AI-IDLE, L2 grit, WTC, and L2 speaking motivation.Results: Findings revealed that AI-IDLE significantly and directly predicted grit and WTC but not speaking motivation. L2 grit emerged as a strong predictor of both WTC and speaking motivation, and functioned as a full mediator in the relationships between AI-IDLE and the two communicative outcomes.Conclusion: These results highlight L2 grit as a key factor linking AI-driven informal learning with learners' communicative readiness and motivation, suggesting that fostering grit may maximize the benefits of AI tools for L2 learning.
In the past couple of years, the use of Generative Artificial Intelligence (GenAI) technologies has received increasing attention in academic contexts. However, the overall ecological system of GenAI literacy has remained unclear to scholars and educators in English as a foreign language (EFL) settings. To fill the gap, the present study employed Bronfenbrenner's (1977) ecological system theory to evince factors that shape Chinese TESOL teachers' GenAI literacy. A focus group interview and a narrative frame were used to gather the needed data. The results of thematic analysis revealed that a wide range of factors at five layers constitute TESOL teachers' GenAI literacy. In particular, it was found that GenAI literacy is affected by 14 factors at micro, meso, macro, exo, and chrono-systemic levels. The study highlights the importance of cultivating an atmosphere of GenAI technology acceptance and adoption in EFL settings that strongly fosters GenAI literacy development among educators. Implications at the theoretical and practical levels are further discussed in the study to augment GenAI literacy of TESOL teachers, teacher educators, and policymakers.
This qualitative study used individual interviews and narrative frames to understand 21 Iranian bilingual teachers' lived experiences of intrapersonal and interpersonal identity conflicts. The findings based on the data analysis with MAXQDA software showed that teachers experienced different intrapersonal and interpersonal identity conflicts with different sources and outcomes. Intrapersonal conflicts were caused mainly by 'professional values and expectations', 'psycho-affective factors' and 'educational system', while interpersonal ones primarily emanated from 'teachers' dissimilar beliefs and methodologies', 'old educational policies' and 'weak communication among colleagues'. The findings also demonstrated that intrapersonal identity conflicts mostly end in 'negative emotions and behaviours', 'classroom instruction challenges' and 'professional growth hindrances' among teachers, while interpersonal identity conflicts 'damaged organisational relationships', 'created teacher isolation, 'produced negative emotions' and 'reduced pedagogical efficiency'. This study extends prior research on teacher education and teacher identity conflicts and discusses implications for theory and practice in bilingual education.
Research on the use of artificial intelligence (AI) technologies in second/foreign language (L2) education has highlighted their contributions to several teaching and learning areas. However, there is insufficient scholarship regarding the impact of AI adoption on teachers' decision-making processes in English as a foreign language (EFL) settings. To fill the gap, this phenomenological study explored Spanish EFL teachers' perceptions about the role of AI technologies in their pedagogical decision-making. A sample of 28 teachers participated in semi-structured interviews. The results of the thematic analysis revealed two spectrums of themes: one suggesting a positive impact of AI on teachers' decision-making across different aspects, while the other suggesting a minimal impact due to teachers autonomy and agency. The study discusses the findings in relation to theoretical and empirical foundations and lists actionable implications for EFL teachers, teacher educators, and policymakers to encourage an AI-informed L2 pedagogy and decision-making. This study contributes to the field by providing novel phenomenological insights into EFL teachers' pedagogical decision-making in AI-mediated contexts and by integrating social constructivism and the Technology Acceptance Model to account for this process.
