Evaluation of Artificial Intelligence Driven Research Methodologies on Lecturers’ Productivity in Lagos State Owned Universities, Nigeria
DOI:
https://doi.org/10.64348/zije.2026634Keywords:
Artificial Intelligence, research methodologies, lecturers' productivity, machine learning, natural language processing, LASUAbstract
This study examined the influence of Artificial Intelligence (AI)-driven research methodologies on lecturers' productivity in Lagos State University (LASU), Ojo Campus. The growing integration of AI tools such as Machine Learning (ML), Natural Language Processing (NLP), and ChatGPT into academic research and teaching, alongside concerns about lecturers' research efficiency, publication output, and teaching effectiveness in Nigerian universities, motivated the study. Specifically, the study sought to ascertain lecturers' awareness of AI-driven research methodologies, examine their knowledge of ML, NLP and ChatGPT, determine their perception of these tools in enhancing research quality and academic productivity, and establish the influence of the tools on research efficiency, publication output, and teaching effectiveness. A descriptive survey research design was adopted, with a sample of 165 lecturers drawn from seven faculties and two schools across Lagos State Owned Universities. Data were collected using a researcher-designed instrument, the Artificial Intelligence Driven Research Methodologies and Lecturers' Productivity Questionnaire (AIDRMLPQ). Out of 189 copies of the questionnaire distributed, 165 were properly completed and returned, giving a response rate of 89.2%. Data were analysed using descriptive statistics (mean and standard deviation) and Pearson Product Moment Correlation at the 0.05 level of significance. Findings revealed a significant positive relationship between the adoption of AI-driven research methodologies and lecturers' research efficiency (r = 0.742), between knowledge of ML, NLP and ChatGPT and academic productivity (r = 0.781), between lecturers' perception of AI tools and research quality (r = 0.766), and between AI tools utilisation and research efficiency, publication output, and teaching effectiveness (r = 0.754). The study concluded that AI-driven research methodologies play a significant and positive role in enhancing lecturers' productivity at LASU, and that institutional support and structured training remain critical to maximising the benefits of AI in higher education. It was recommended, among others, that the University organise regular training on AI tools, develop clear policies on ethical AI use, and invest in AI-compatible infrastructure to support lecturers' effective adoption of AI in research and teaching.
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