<![CDATA[Purpose – This study examines artificial intelligence (AI) adoption in academic research by assessing academicians’ awareness of AI tools, attitudes toward AI, perceived usefulness, and the associations of attitudes and perceived usefulness with reported AI adoption.Methods – A quantitative cross-sectional survey was conducted among academicians from universities, colleges of education, polytechnics, and monotechnics in Plateau State, Nigeria. From a target population of 4,570 academicians, a sample of 368 was determined, and 307 usable responses were retained for analysis. Data were collected using the Awareness, Attitudes and Perceptions of Academicians Towards Artificial Intelligence in Research Questionnaire (AAPATAIRQ), comprising 20 items across four constructs. Descriptive statistics and multiple regression were conducted using SPSS version 27.Findings – Descriptive results indicated generally low levels across the four constructs. Perceived usefulness recorded the highest mean score (M = 2.58, SD = 1.376), followed by attitude toward AI (M = 2.42, SD = 1.403), awareness of AI tools (M = 2.36, SD = 1.219), and AI adoption in research (M = 2.35, SD = 1.331). The reported regression results indicated that attitudes toward AI and perceived usefulness were positively and statistically significantly associated with AI adoption, with perceived usefulness showing the stronger relative association.Research implications – The findings indicate that institutional strategies for responsible AI integration should extend beyond general awareness initiatives by strengthening practical AI competencies, demonstrating the research value of AI tools, and providing guidance on academic integrity, transparency, reliability, and responsible use.Originality – The study contributes context-specific evidence from a heterogeneous tertiary-education setting and analytically distinguishes AI awareness from the evaluative factors associated with reported adoption. Because the study is cross-sectional and based on self-reported data, the findings should be interpreted as associations rather than causal effects]]>
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