This article critically examines the distinction between English for Specific Purposes (ESP) and English for General Purposes (EGP) within language teaching contexts. It argues that the traditional dichotomy between ESP and EGP is inadequate and proposes a unified perspective. The rationale behind this argument is that all English language teaching experiences involve specificity, as learners’ language needs are influenced by their particular goals, domains, and contexts. The article challenges the prevailing assumptions and highlights the limitations of the ESP vs. EGP dichotomy. The key findings reveal that the boundaries between ESP and EGP are blurred and that considering all English language teaching as ESP provides a more comprehensive and inclusive approach. This reevaluation has significant implications for language pedagogy, curriculum design, and teacher training, urging a shift in perspective toward recognizing the specific nature of all language learning and teaching contexts.

Part-of-Speech (POS) tagging is a foundational task in Natural Language Processing (NLP), yet it remains challenging for low-resource and morphologically rich languages such as Amazigh. This paper proposes a hybrid ensemble framework for Amazigh POS tagging that integrates three complementary models: a Bidirectional Long Short-Term Memory network (BiLSTM), a Conditional Random Field model (CRF), and a rule-based morphological analyzer (RBMA). Rather than treating prior results obtained on different corpora and tag inventories as directly comparable, the study evaluates all proposed components under a common 54-tag experimental setting based on the publicly available Amazigh Linguistic Dataset. Three ensemble strategies are examined: majority voting, validation-weighted voting, and logistic-regression stacking. An additional late-fusion ablation applies hard and soft RBMA constraints to CRF and Stacking outputs; hard masking degrades performance substantially, whereas soft masking is more robust but remains below unconstrained decoding. The best micro-level performance is obtained by the stacking ensemble, which reaches 98.51% Micro-F1/accuracy, whereas the boosting-like weighted ensemble obtains the strongest Macro-F1 among the ensemble variants, reaching 74.24%. These results show that hybrid ensemble methods can improve token-level accuracy in low-resource POS tagging, while also revealing a trade-off between frequent-tag accuracy and rare-tag robustness. The findings highlight the usefulness of combining neural, probabilistic, and rule-based information for Amazigh POS tagging, and point to class-balanced meta-learning and character/subword representations as important directions for improving rare and out-of-vocabulary categories.

The literature on engineering education consistently emphasizes the importance of soft skills in the workplace. However, engineering students may not fully recognize the significance of these skills for their future employability and career progression. This paper investigates how undergraduates at the National Graduate School of Arts and Crafts (ENSAM - Meknes) assess their proficiency in soft skills and their perceptions of the role these skills play in their post-graduation employment. Additionally, the study explores the relationship between soft skills proficiency and intercultural communicative competence, aiming to offer practical recommendations for enhancing the design of soft skills development curricula.