Practitioner Beliefs and Behaviors in AI-Enhanced Education: DOT Framework Survey Evidence

A cross-sectional survey of 72 higher education practitioners found that educators hold favorable views of AI as a pedagogical support while maintaining strong commitments to human oversight and critical evaluation, according to a study grounded in the DOT Framework. Reported practices emphasize iterative prompting and content generation, but less consistent use of needs assessment and feedback loops, with institutional barriers including limited policy, training, and infrastructure widely reported. The findings provide preliminary empirical support for the DOT Framework as a descriptive model while highlighting gaps between design-oriented theory and current implementation.

arXiv:2605.29041v1 Announce Type: new Abstract: This study reports findings from a cross-sectional survey n = 72 of higher education practitioners examining beliefs, behaviors, and institutional conditions related to artificial intelligence AI integration in teaching and learning. Grounded in the DOT Framework, which integrates design thinking and open systems theory, the study investigates AI familiarity, usage patterns, design-oriented practices, and pedagogical beliefs. Exploratory factor analysis of 19 belief items identified a three-factor structure: AI Functional Capabilities, Oversight and Governance, and Instructor Collaboration and Planning {\alpha} = .90 . Results indicate that practitioners hold favorable views of AI as a pedagogical support while maintaining strong commitments to human oversight and critical evaluation. Reported practices emphasize iterative prompting and content generation, with less consistent use of needs assessment and feedback loops. Institutional barriers including limited policy, training, and infrastructure were widely reported. These findings provide preliminary empirical support for the DOT Framework as a descriptive model of practitioner beliefs and practices, while also highlighting gaps between design-oriented theory and current implementation. The study contributes an initial measurement structure and identifies directions for confirmatory validation and outcome-based research linking AI-supported design practices to instructional quality.