​Measuring Institutional Strategies for AI Teaching and Learning in Nigerian HEIs

Abstract

Purpose: This study aimed to assess institutional strategies for Artificial Intelligence (AI) teaching and learning in Nigerian Higher Education Institutions (HEIs), focusing on institutional AI policy frameworks, lecturers’ AI readiness, and ICT infrastructure investment. Methodology: This study employed a quantitative descriptive and inferential survey design. Data were collected through a structured 60-item questionnaire administered online to academic staff across tertiary institutions in Anambra State, Nigeria. A total of 33 valid responses were obtained and analyzed using descriptive statistics, Pearson correlation, simple linear regression, and robustness tests, including bootstrap resampling and permutation analysis. Results: The findings indicate that lecturers’ perceptions of institutional AI policy frameworks significantly and positively influence AI integration in teaching and learning. Lecturers’ AI readiness also demonstrates a significant positive relationship with the behavioral intention to adopt AI tools. However, ICT infrastructure investment shows a positive but statistically insignificant relationship with the perceived effectiveness of AI-enhanced teaching and learning. Conclusions: This study concludes that clear institutional AI policies and lecturer readiness are critical factors supporting AI adoption in HEIs, while the role of ICT infrastructure requires further investigation with larger samples. Limitations: This study is limited by its small sample size, cross-sectional design, and reliance on self-reported perceptions from lecturers in one Nigerian state. Contributions: This study contributes to the AI adoption literature by examining institutional policy, individual readiness, and infrastructure pathways within a single framework based on UTAUT, providing practical insights for HEI administrators in developing effective AI integration strategies.