Speaker
Description
Oluchukwu, N. M.
Federal College of Education (Technical), Asaba, Delta State, Nigeria.
nwadeioluchi@gmail.com
Track: Digital Transformation in Higher Education
Nwadei, Mary Oluchukwu
Federal College of Education (Technical) Asaba, Delta State, Nigeria
Department of Language and Communication Education
nwadeioluchi@gmail.com
+234 8030470831
AI and Oral English Remediation for Communicative Competence in Nigerian Secondary Education
Abstract
Persistent deficiencies in oral English proficiency among Nigerian secondary school students have remained a major challenge, affecting learners’ pronunciation, fluency, articulation, classroom interaction, and overall communicative competence. Many students experience difficulties in stress placement, intonation, and effective spoken communication due to overcrowded classrooms, inadequate language laboratories, limited exposure to standard spoken English, and the absence of personalized remediation strategies. Conventional teaching approaches often fail to address learners' specific oral English needs, thereby reducing students’ confidence and communicative effectiveness in academic and social contexts. The increasing advancement of Artificial Intelligence technologies in language education has created opportunities for personalized oral English remediation through speech recognition, automated correction, and adaptive learning systems. This study, therefore, investigates AI and oral English remediation for communicative competence in Nigerian secondary education. The study adopts a quantitative descriptive survey research design. The population of the study consists of 105 English language teachers drawn from selected public secondary schools across Delta North, Delta Central, and Delta South Senatorial Districts of Delta State, Nigeria. The total population sampling technique is adopted because the population size is manageable. Fifteen public secondary schools are selected across the three senatorial districts, with seven English language teachers selected from each school. Data are collected using structured questionnaires and oral English assessment rating scales validated by experts in English language education and measurement and evaluation. Reliability of the instruments is established using Cronbach's Alpha statistical method. Data collected are analysed using mean scores and standard deviation to answer the research questions, while t test statistical analysis is used to test the hypotheses at a 0.05 level of significance. Findings reveal that Artificial Intelligence significantly improves students’ pronunciation, fluency, speaking confidence, and communicative competence through personalized feedback and adaptive learning processes. The study also reveals that inadequate digital infrastructure, unstable electricity supply, and limited teacher competence constitute major barriers to effective AI integration in oral English instruction. The study recommends increased government investment in digital language learning facilities, provision of stable technological infrastructure in schools, and regular training programmes for English language teachers on the effective use of AI tools for oral English remediation.
Keywords: Artificial Intelligence, Oral English Remediation, Communicative Competence, personalized remediation strategies.