Examining the Moderating Effect of Instructional Risk on the Relationship Between Relative Advantage and AI Classroom Use Among Mathematics Lecturers in Ghana

Artificial intelligence (AI) is increasingly reshaping instructional planning, assessment, feedback, and learner support in higher education. In mathematics education, AI tools offer possibilities for adaptive problem solving, automated feedback, mathematical visualization, lesson preparation, and differentiated instruction. However, lecturers’ classroom use of AI may depend not only on perceived benefits but also on concerns about instructional risk, including academic dishonesty, inaccurate AI-generated solutions, overreliance, reduced learner reasoning, ethical uncertainty, and weakened pedagogical control. This study examined the moderating effect of instructional risk on the relationship between relative advantage and AI classroom use among mathematics lecturers in Ghanaian Colleges of Education. Anchored in Diffusion of Innovation Theory, the Technology Acceptance Model, and the Technology–Organization–Environment framework, the study employed a quantitative cross-sectional survey design. Data were collected from 230 mathematics lecturers and analysed using Structural Equation Modeling in AMOS. The measurement model showed acceptable reliability, convergent validity, and discriminant validity. The structural model demonstrated good fit: χ²/df = 1.91, CFI = .956, TLI = .947, IFI = .957, GFI = .921, AGFI = .895, RMSEA = .063, and SRMR = .046. Results revealed that relative advantage positively predicted AI classroom use, β = .48, p < .001, while instructional risk negatively predicted AI classroom use, β = −.29, p < .001. The interaction effect was also significant, β = −.18, p < .01, indicating that instructional risk weakens the positive influence of relative advantage on AI classroom use. The study contributes to AI adoption literature by demonstrating that perceived usefulness alone may be insufficient when lecturers perceive AI as pedagogically risky. Practical implications are discussed for AI policy, lecturer professional development, ethical guidelines, and mathematics teacher education in Ghana.