December 31 · Article · Computer-Assisted Language Learning Electronic Journal · Language and Linguistics
This research investigates the role of artificial intelligence (AI) chatbots in improving informal speaking proficiency and social pragmatic abilities in upper-intermediate male EFL learners located in Tashkent, Uzbekistan. A quasi-experimental design was utilized, involving 50 learners aged 18–24 from an English Language Institute. These participants were randomly assigned to either an experimental group (EG), which interacted with a ChatGPT-based AI chatbot, or a Control Group (CG), which engaged in teacher-led speaking practice. Pre- and post-tests, which included a speaking proficiency rubric and a social pragmatics assessment, evaluated changes in fluency, accuracy, lexical complexity, and pragmatic competence (such as turn-taking and politeness strategies). Quantitative data were analyzed using paired and independent t-tests, whereas qualitative survey responses regarding learner attitudes were explored through thematic analysis. Results indicated notable enhancements in both groups, with the EG showing more substantial progress (mean improvement = 6.39 vs. 1.59 points for speaking proficiency; 0.9 vs. 0.3 for social pragmatics; p < 0.05). Participants indicated a boost in confidence, heightened engagement, and a decrease in anxiety when interacting with chatbots. These findings highlight the potential of AI chatbots to enhance traditional instruction by offering flexible, low-anxiety practice environments. They provide valuable insights for teachers, curriculum designers, and material developers on how to incorporate AI tools to improve EFL communicative competence.
BEBotir ElovOpen access
December 31 · Article · Journal of Information Systems and Management Research · Education
Micro and small-sized enterprises (micro-SMEs) in Europe’s furniture sector continue to face significant obstacles to internationalisation, including limited digital skills, constrained resources, and a lack of strategic market insight. In response to these challenges, the INT4FURN project—supported by Erasmus+ Key Action 2—has developed a Digital Internationalisation Learning Hub designed to build international business competencies through modular training aligned with the ECVET framework. This paper offers a critical analysis of the platform, examining its pedagogical approach, structural design, and core features, including thematic learning modules, multilingual glossaries, instructional videos, and self-assessment tools. Drawing on a qualitative case study methodology, the findings indicate that the platform provides a user-centred, mobile-accessible, and digitally inclusive learning experience, tailored specifically to the needs of micro-SMEs. While challenges such as digital literacy and learner engagement remain, the INT4FURN hub emerges as a scalable, innovative solution that contributes meaningfully to vocational education and supports the broader goals of digital and sustainable transformation within traditional manufacturing industries.
DADr. Adem TekerekOpen access
December 31 · Article · Applied Sciences · Pollution
Heavy metal enrichment in agricultural soils can affect crop safety, ecosystem functioning, and long-term land productivity, yet farm-scale screening is often constrained by limited routine monitoring data. This study develops a GIS-based framework that combines field-scale spatial analysis with explainable machine learning to characterize and predict heavy metal enrichment on an intensively managed cereal farm in eastern Kazakhstan. Topsoil samples (0 to 20 cm) were collected from 34 fields across eight campaigns between 2020 and 2023, yielding 241 composite field–campaign observations for eight metals (Pb, Cu, Zn, Ni, Cr, Mo, Fe, and Mn) and routine soil properties (humus, pH in H2O, and pH in KCl). Concentrations were generally low but spatially heterogeneous, with wide observed ranges for several elements (for example, Pb 0.06 to 2.20 mg kg−1, Zn 0.38 to 7.00 mg kg−1, and Mn 0.20 to 38.0 mg kg−1). We synthesized multi-metal structure using an HMI defined as the unweighted mean of z-standardized metal concentrations, which supported field-level screening of persistent enrichment and emerging hot spots. We then trained Extreme Gradient Boosting models using only humus and pH predictors and evaluated performance with field-based spatial block cross-validation. Predictive skill was modest but nonzero for several targets, including HMI (mean R2 = 0.20), indicating partial spatial transferability under conservative validation. SHAP analysis identified humus content and soil acidity as dominant contributors to HMI prediction. Overall, the workflow provides a transparent approach for field-scale screening of heavy metal enrichment and establishes a foundation for future integration with satellite-derived covariates for broader monitoring applications.
Nail BeisekenovOpen access
