Students’ AI Dependency in 3R’s: Questionnaire Construction and Validation

The integration of Artificial Intelligence (AI) into education has introduced both groundbreaking opportunities and concerns. Among these concerns is the extent of students’ reliance on AI in the realms of reading, writing, and numeracy/arithmetic (3Rs). While existing instruments delve into the broader impact of AI, they exhibit certain limitations. Consequently, this research endeavors to develop and validate a specialized questionnaire tailored to assess students’ dependency on AI in the 3Rs. The process includes interviews with student groups, consultations with professionals in the education sector, face validation, content validation, exploratory factor analysis, confirmatory factor analysis, Rasch analysis, and reliability testing to navigate the construction and validation of the instruments. Initial item identification involved a 45-item questionnaire distributed across three constructs, derived from qualitative interviews with students and experts. The survey received a total of 727 responses. Post EFA, nine items were eliminated due to their failure to achieve a loading factor of 0.5, and certain items exhibited cross-loadings. Subsequent Rasch analysis affirmed the construct validity of the instruments, prompting the removal of three additional items. The resulting 33-item questionnaire, divided into three constructs—Reading (10 items), Writing (11 items), and Numeracy/Arithmetic (12 items)—emerges as a validated and reliable tool for measuring students’ dependency in the 3Rs. The author confirms the validity and reliability of the questionnaire. Future research should focus on longitudinal studies to assess how AI dependency evolves over time and impacts educational outcomes.