Development and Validation of a GAI-Assisted Engineering Creativity Scale for University Students

Abstract

The growing use of generative artificial intelligence (GAI) in engineering education has created a need for measures that capture students’ creativity with these tools in engineering contexts. This research developed and validated the GAI-Assisted Engineering Creativity Scale (GECS) across six studies involving university engineering students. Study 1 generated and refined the initial item pool through theory-informed content validation. Study 2 identified a four-dimensional structure comprising Intentionality, Authenticity, Human–AI Collaboration, and Critical Integration. Study 3 confirmed this structure, compared alternative measurement models, and examined measurement invariance across gender. Study 4 evaluated short-term test–retest reliability and informed the final item refinement. Study 5 examined convergent and discriminant validity in relation to established measures of AI-assisted creativity and general creative attributes and behaviors. Study 6 evaluated associations with creative thinking, critical thinking, creative self-concept, and general creativity scale. The final 16-item GECS demonstrated satisfactory internal consistency, a replicable four-factor structure, moderate short-term temporal stability, and meaningful relationships with theoretically relevant constructs. The findings support the GECS as a multidimensional measure of students’ creativity assisted by GAI in engineering work.