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

Computer Science > Artificial Intelligence [Submitted on 27 May 2026] Practitioner Beliefs and Behaviors in AI-Enhanced Education: DOT Framework Survey Evidence David Gibson (1), M. Elizabeth Azukas (2), Gerald Knezek (3) ((1) Curtin University, (2) Georgia Institute of Technology, (3) University of North Texas) 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.29041 [cs.AI]   (or arXiv:2605.29041v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.29041 Focus to learn more Submission history From: David Gibson [view email] [v1] Wed, 27 May 2026 19:42:31 UTC (562 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)