Indian Journal of Language and Linguistics

Nigerian English Development Strategies by Crèche Pupils

2026-04-09T19:36:17+00:00

This study investigates how Nigerian crèche pupils (aged 1–3) contribute to the use of Nigerian English (NE) through everyday classroom interactions. Guided by Labov’s (1972) variationist sociolinguistic theory, a qualitative approach was employed across twelve schools in South-West Nigeria, using participant observation and caregiver interviews. The findings reveal rich, systematic use of NE in pupils’ speech. Onomatopoeic expressions such as quack quack for ducks, muu for cows, and wee-wee for urination helped children associate sounds with objects and actions, fostering both naming and social interaction. Lexico-semantic generalisations were common: the speaker referred to phones or radios, and to pencils, crayons, and pens; papa described any older male. These flexible uses show children simplifying categories for ease of communication. Reduplication patterns such as cry-cry, beggy-beggy, and small-small functioned as intensifiers, emphasising behavioural traits, movement, or social evaluation. Pupils also coined original expressions such as Mr Do-Good (disciplinary cane) and Big Daddy (an older male figure), demonstrating their creative engagement with sociocultural norms. Language transfer was evident in the everyday use of Yoruba terms like dodo (fried plantain) and ponmo (cow skin), which were generalised to refer to various meats. Unique phonological strategies included substitution (told for gold), deletion (apoo for apple), and insertion (bikitifor biscuit), and syllable reduction (dwink for drink), while grammatical forms, such as he say and off the light, showed alignment with established NE patterns. These findings affirm that Nigerian crèche pupils are early adopters of a distinct variety of Nigerian English.

Artificial Intelligence in Language Education in the Generative-AI Era: A Systematic Integrative Review of Pedagogical Evidence, Methodological Quality, and Responsible Adoption

2026-08-12T12:03:15+00:00

Artificial intelligence has grown quickly in language teaching, particularly since the introduction of generative AI and large language models. Even so, the existing evidence is dispersed across various technologies, teaching applications, learner groups, and research approaches. This systematic integrative review examined 40 primary empirical or technical studies. It also used 15 systematic reviews, meta-analyses, and bibliometric syntheses to provide contextual evidence. The selected publications were published between 2017 and July 2026, while the majority of the primary evidence appeared after 2022. The review employed descriptive mapping, thematic synthesis, design-sensitive methodological assessment, and framework development. The primary papers were evaluated based on AI technology, educational application, language skill, learner group, research design, outcome type, multilingual representation, methodological quality, and ethical or governance concerns. Writing assistance, automated feedback, speaking practice, learner perceptions, teacher support, and AI-assisted pedagogy were the most extensively researched topics. In contrast, reading, listening, younger learners, low-resource languages, delayed learning outcomes, and validation across contexts received less attention. The evidence points to potential short-term improvements for writing revision, speaking practice, motivation, engagement, and teacher support. However, confidence in these findings is limited because many studies used small sample sizes, brief interventions, single-institution settings, self-reported measures, incomplete model and prompt reporting, and minimal external validation. Generative systems increased interactivity, adaptability, and task coverage but also introduced output variability, hallucination, authorship ambiguity, privacy risk, and reproducibility problems. Based on the integrated findings, the review proposes a responsible-adoption framework connecting AI-system characteristics, pedagogical design, human–AI interaction, contextual conditions, educational outcomes, and institutional governance. The findings indicate that educational value depends less on technological novelty than on task alignment, teacher mediation, learner agency, methodological rigour, multilingual inclusion, and accountable human oversight.

AI- and XR-Enhanced Multisensory Language Learning: A Systematic Review and Meta-Analysis of Effects on Second Language Acquisition

2025-12-19T13:40:48+00:00

Artificial intelligence (AI) and extended reality (XR) technologies, including virtual reality (VR) and augmented reality (AR), are increasingly being integrated into second language acquisition (SLA). Although individual studies have reported promising outcomes, the magnitude and consistency of their effects on language learning remain uncertain. This meta-analysis synthesized evidence from peer-reviewed experimental and quasi-experimental studies to estimate the effectiveness of AI- and XR-enhanced multisensory interventions on linguistic, cognitive, and affective outcomes in language education. Following PRISMA guidelines, 21 peer-reviewed studies published between 2010 and 2025 were systematically reviewed. Random-effects models were used to calculate pooled Hedges’ g effect sizes, with subgroup analyses conducted by outcome domain, technology type, and learner age group. Risk of bias and publication bias were also assessed. The overall meta-analysis indicated a moderate positive effect of AI- and XR-enhanced interventions on second language learning outcomes (Hedges’ g = 0.61, 95% CI [0.44, 0.78], p < .001). Domain-specific analyses suggested the strongest effects for linguistic outcomes (g = 0.68), followed by affective outcomes (g = 0.55) and cognitive outcomes (g = 0.42). Effects were generally positive across learner groups, with somewhat larger estimates for K–12 learners than for adult learners. Publication bias analyses suggested that the pooled effect may be slightly overestimated; however, trim-and-fill adjustments indicated that the overall conclusions remained substantively unchanged (adjusted g ≈ 0.54). These findings suggest that AI- and XR-enhanced multisensory interventions may support both linguistic and affective dimensions of SLA, particularly motivation, engagement, and confidence. However, the results should be interpreted cautiously given methodological heterogeneity, variable definitions of artificial intelligence, and the limited number of studies in some subgroups. Further large-scale and longitudinal research is needed to clarify the conditions under which these technologies are most effective and to support their responsible and equitable implementation in language education.