Se reporta estudio cuyo propósito fue analizar el significado que los estudiantes de educación media general atribuyen a las Tecnologías de la Información y la Comunicación (TIC) en la emergencia del conocimiento en contextos adversos. Se identificaron vacíos ontológicos relacionados con la vivencia del estudiante frente a un sistema que presenta brechas de infraestructura y carencias en las competencias digitales docentes. Metodológicamente, se abordó bajo el paradigma postpositivista y enfoque cualitativo, utilizando el método fenomenológico-hermenéutico, para desentrañar realidades subjetivas de los actores educativos. La técnica fue la entrevista en profundidad y el instrumento un guion de preguntas generadoras. Los hallazgos preliminares revelaron que la tecnología, más que un recurso, debe transformarse en un lenguaje común donde se fusione el saber del maestro con la curiosidad del alumno para superar el analfabetismo digital, que hasta el momento ha existido en los espacios de la educación media general
Music can support affective engagement across linguistic boundaries, but the mechanisms and infrastructures of that engagement remain dispersed across sociolinguistics, translation studies, music cognition, and platform research. This conceptual article compares three regimes of cross-lingual musical circulation: analogue and broadcast distribution, platform-mediated circulation, and emerging AI-assisted production. It synthesises scholarship on prosody, multilingual composition, fan translation, recommender systems, and singing-voice synthesis to develop a music-specific account of translingual resonance: sustained aesthetic and emotional engagement with music in a language that a listener does not fully understand. The article argues that such resonance is relational rather than purely psychological, arising through interactions among musical-linguistic form, listener knowledge, technological mediation, and interpretive communities. It also proposes translingual expropriation as an ethical category for cases involving non-consensual voice or style simulation, opaque data provenance, misattribution, asymmetric benefit, loss of cultural agency, or displacement of originating musicians. Because the article is based on selected cases and conceptual synthesis rather than a representative corpus or audience study, both concepts are presented as preliminary frameworks for future reception, corpus, and ethnographic research.
This Editorial Introduction presents the intellectual rationale, thematic architecture, and editorial governance of the Special Issue “Human–AI Communication Across Generations,” developed in connection with Multilingual Dialogues–2026. The issue comprises eight Research Articles, two Critical Reviews, and two Case Studies organised around four intersecting areas: authorship and textual subjectivity; multilingualism, translation, and cultural mediation; education and disciplinary literacy; and discourse, framing, and emotion-aware natural language processing. Across the contributions, AI is treated neither as an autonomous author nor as a neutral tool. Its communicative effects depend on model and prompt conditions, language and genre, institutional setting, human selection, and verification. The term generations is therefore used cautiously: one study directly examines age-related group variation, whereas other articles address technological generations, educational cohorts, and cultural transmission. The Editorial argues that fluency is not equivalent to semantic, pragmatic, literary, legal, or pedagogical adequacy and that accountable human judgement remains indispensable. It also documents the issue’s conference relationship, double-anonymised peer review, and independent final decision-making.
Artificial-intelligence-assisted translation of culturally marked phraseology may produce semantically plausible output while failing to preserve affective valence, pragmatic force, register, or cultural symbolism. This study examines English and Azerbaijani zoonym-based phraseological units in bidirectional AI-assisted translation. A qualitative corpus-based comparative design was applied to 100 units (50 English and 50 Azerbaijani) selected from phraseological dictionaries and literary and digital sources. Translations were generated with ChatGPT (OpenAI GPT-5.5) through the official web interface between 15 and 20 July 2026. Each source item was tested three times in newly initiated sessions using a standardized prompt. Outputs were compared with reference equivalents identified in the cited lexicographic sources and verified by the author across six dimensions: semantic adequacy, idiomatic naturalness, affective equivalence, pragmatic function, cultural appropriateness, and register preservation. The qualitative case analyses illustrate successful preservation of conventional target-language equivalents in some items and literal rendering, metaphorical-image mismatch, reduced emotional expressiveness, pragmatic weakening, or loss of cultural symbolism in others. Because corpus-level frequencies and the complete item-level record are not presented in this version, the findings should be interpreted as qualitative patterns rather than statistical estimates. The study demonstrates the value of context-sensitive, linguoculturally informed evaluation of AI-assisted phraseological translation.
Legal English creates linked linguistic and doctrinal challenges for students studying across English and Bangla. This exploratory cross-sectional survey examined how Bangladeshi Bachelor of Laws (LL.B.) students use artificial intelligence (AI)-enabled language tools, what benefits and risks they perceive, and how they verify outputs. An English-language Google Forms questionnaire with closed- and open-ended items was distributed through convenience sampling from 6 May to 14 June 2026. Of 90 exported records, two blank test submissions and one ineligible non-law response were excluded, yielding 87 respondents. Among 86 valid responses, 72 (83.7%) reported difficulty with Legal English at least sometimes, and 83 (96.5%) reported using AI tools at least sometimes. ChatGPT was selected most often (65/86, 75.6%), followed by Google Gemini (36/86, 41.9%) and Google Translate (19/86, 22.1%). Respondents mainly used these tools for English–Bangla translation, simplification, and case-law summarisation. Most respondents perceived improved reading confidence (75/86, 87.2%) and assignment support (76/86, 88.4%), yet 71/86 (82.6%) reported that AI sometimes gives incorrect explanations, and 76/83 (91.6%) endorsed verification against textbooks, statutes, or teachers. Teacher explanation remained the preferred source (46/86, 53.5%). The findings position AI as a linguistic access layer rather than a legal authority. Responsible integration requires tool differentiation, jurisdictional checking, source tracing, privacy safeguards, disclosure, and teacher-guided legal AI literacy. Because the study uses convenience sampling and self-report, it does not establish improved learning outcomes.
