Development and Validation of the Academic AI Usage Scale (AAIUS)

The increasing use of artificial intelligence (AI) tools in educational settings has changed how students learn and engage in academic activities. However, there is still a lack of empirically validated instruments to identify the specific nature and extent of AI usage in academic settings. This study developed and validated the Academic AI Usage Scale (AAIUS) to assess students’ utilisation of AI tools for academic purposes. An initial pool of 30 items was refined to 24 through expert validation (S-CVI = 0.90). The scale was administered to 300 Indian university students. The Kaiser-Meyer-Olkin measure of sampling adequacy for the dataset was 0.889. Exploratory Factor Analysis with Maximum Likelihood and Varimax rotation identified a three-factor structure, namely, AI Dependence (9 items), Academic Support (11 items), and AI for Academic Skills (4 items), and it explained 42.5% of the total variance. The scale showed strong internal consistency (Cronbach's α = .856), and adequate test-retest reliability (r = .828, p < .001). The AAIUS demonstrates sound initial psychometric properties, offering a foundation for future work examining patterns, benefits, and risks of AI usage in higher education.