Speaker
Description
Ngozi, E. R.
Federal College of Education Technical Asaba, Delta State
roselynebohnzekwue@gmail.com
Track: AI, Emerging Technologies and Future Learning
Artificial Intelligence has increasingly transformed educational practices across the world, yet many higher institutions in Nigeria continue to experience persistent language learning difficulties among students, especially in pronunciation, grammar, vocabulary acquisition, writing competence, and oral communication skills. Conventional teaching approaches often fail to provide individualised support capable of addressing learners’ unique academic challenges. This problem motivated the present study, which examines Artificial Intelligence as a personalised learning tool for diagnosing and addressing language learning difficulties in higher education institutions in Delta State, Nigeria. The study adopted a quantitative research design anchored on Siemens’ Connectivism Theory (2005), which emphasises technology-driven learning, digital connectivity, and personalised knowledge construction within modern learning environments. The area of the study was Delta State, Nigeria, selected because of the increasing adoption of digital learning technologies in tertiary institutions within the state. The population of the study comprised 210 teachers and students drawn from selected higher institutions in Delta State. A stratified random sampling technique was employed to ensure adequate representation of both lecturers and students across the institutions. Data were collected using a structured questionnaire validated by experts in educational technology and language studies. The reliability of the instrument was established using Cronbach's alpha statistics. The mean and standard deviation were used to analyse the data and address the research questions. Findings revealed that Artificial Intelligence tools significantly improve personalised learning by diagnosing learners’ language deficiencies, providing adaptive feedback, enhancing speaking and writing skills, and increasing learner engagement and academic performance. The study also identified inadequate digital infrastructure, poor internet connectivity, and insufficient AI literacy among educators as major constraints affecting implementation. The study concludes that Artificial Intelligence has substantial potential for improving language learning outcomes in higher education. The study recommends increased investment in digital infrastructure, ongoing training for educators, and institutional policies that support the integration of Artificial Intelligence into language instruction.