Journal of Linguistics, Literature, and Language Teaching (JLLLT)

Language Practices in Multilingual Ghanaian Nuclear Families: Exploring Mother-Child Interaction and L1 Transmission

Richard Ayertey Lawer — 2025-04-30

Ghana is a country with about 79 indigenous languages. Some of these languages have become endangered through the phenomenon of language shift. This study explored the multilingual situation of homes of Ghanaian nuclear families, seeking to understand how the language practices of nuclear families impact language endangerment in the country. The study employed a qualitative approach to analyze two nuclear families as a sociolinguistic domain where members of the families make choices regarding language use and practices. Data for the study were obtained via participant observation of two nuclear families and interviews with the spouses of both families. The findings showed that in multilingual families, the language the children acquire as their L1 is often the language of mother-child interaction. The study showed further that fathers' L1 becomes endangered when that is not the language of interaction between mother and children. The findings revealed that even though parents wish to preserve their L1 and culture by passing on their L1 to their children, this goal becomes threatened when their L1 is not the language of mother-child interaction in the home. The findings reinforce the idea that language maintenance is not just an individual choice but broader sociolinguistic and cultural forces shape it. The endangered status of the parents' L1 in nuclear families demonstrates how family language policies, gendered linguistic roles, and societal pressure interact in influencing language endangerment. The study recommended that parents need to put in the effort to ensure that their L1 is used at home to help their children develop competence in it.

Exploring Digital Communication: Language Use by South African University Students

Tiyiselani Ndukwani — 2025-04-30

This study explores the use of language on social media. The researchers investigated the problems related to language that undergraduate students encounter when they socialize on cyber socializing platforms. The study also highlights the relationship between language and cyberbullying, incorporating AI tools used by cyber socializing platforms to explore their effectiveness in detecting improper linguistic communication on social media. The study employed a qualitative approach, which entailed semi-structured interviews to collect the desired data. Ten undergraduate students volunteered to be part of the interviews. The findings of this study showed that all 10 students encountered problems related to language, and for some students, these problems led to mental health issues. The following are some of the problems encountered by students when interacting on social media: slang, misinterpretation, grammar and spelling errors, informal language, fake news, language barriers, cyberbullying, discriminatory language, the difference between face-to-face communication and online communication, and AI tools such as natural language processing, having difficulties in detecting improper linguistic communication.

Harnessing Artificial Intelligence for English Language Learning: A Systematic Literature Review

Muhammad Hasyimsyah Batubara — 2025-04-30

Integrating AI in education has transformed English language learning by offering personalized, adaptive, and interactive learning experiences. However, challenges such as technological accessibility, pedagogical integration, and maintaining human-centered learning approaches remain underexplored. This study aims to systematically review recent research on the use of AI-based adaptive platforms in ELL, focusing on their utilization, effectiveness, and overall impact. Employing the Systematic Literature Review method, 13 relevant studies published between 2020 and 2024 were analyzed using a structured protocol involving keyword identification, database selection, and strict inclusion-exclusion criteria. The findings reveal that AI technologies, such as chatbots, mobile applications, adaptive learning platforms, and virtual tutors, have significantly enhanced learners' language skills, engagement, motivation, and confidence. Challenges such as over-reliance on AI, critical thinking development, and digital inequalities were noted despite the benefits. The study implies that for AI to sustainably and effectively support ELL, balanced integration with human instruction, adequate technological infrastructure, and teacher training are essential. These insights offer valuable guidance for educators, policymakers, and technology developers in designing more inclusive, adaptive, and human-centered AI solutions for English education.