Speech Recognition AI for Multilingual Classroom Management and Curriculum Delivery in Nigerian Universities

This study assessed Nigerian teacher educators' perceptions of Speech Recognition AI (SRAI) for multilingual classroom management and curriculum delivery. Addressing language barriers in Nigeria's linguistically diverse universities (500+ languages), the research employed a quantitative descriptive survey with 312 teacher educators from the 2024 Annual Curriculum Organization Conference at Ebonyi State University. Participants represented federal/state universities and colleges of education across Nigeria's six geopolitical zones. A validated Likert-scale questionnaire measured perceived effectiveness, curriculum impact, benefits/challenges, and infrastructural factors. Results indicated strong agreement (μ=4.5) on SRAI's administrative utility (e.g., attendance tracking) but moderate scores for real-time translation (μ=3.9) due to accent recognition limitations. Infrastructure challenges (electricity μ=4.6; internet μ=4.5) outweighed technical concerns. Regression analysis revealed institutional support (β=0.42) and electricity reliability (β=0.38) as strongest adoption predictors, while cultural acceptability (β=0.19) was negligible. Findings highlight SRAI's potential if infrastructure and training constraints are addressed.