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

A survey of 72 higher education practitioners reveals favorable attitudes toward AI in teaching while emphasizing human oversight and governance. The study, grounded in the DOT Framework combining design thinking and open systems theory, identifies significant gaps between theoretical best practices and actual implementation, with institutional barriers limiting effective AI adoption.

This academic study examines how educators perceive and integrate artificial intelligence into their teaching practices, revealing a nuanced landscape where enthusiasm meets caution. The research surveyed 72 higher education practitioners to understand their beliefs about AI capabilities, governance concerns, and collaborative approaches to implementation. Through exploratory factor analysis, three distinct belief dimensions emerged: practitioners view AI as functionally capable, demand robust oversight mechanisms, and recognize the importance of instructor collaboration in planning. The findings demonstrate that educators maintain balanced perspectives, embracing AI's pedagogical potential while insisting on human control and critical evaluation throughout the process. The gap between theory and practice represents a significant concern for educational technology developers and institutions. While practitioners report using iterative prompting and content generation techniques, they show inconsistent adoption of systematic needs assessment and feedback mechanisms that characterize design-oriented approaches. Institutional constraints—insufficient policies, inadequate training, and limited technical infrastructure—create substantial barriers preventing more sophisticated AI integration. These obstacles directly impact the quality and consistency of AI-enhanced learning experiences. For educational technology providers and institutional leaders, these findings suggest demand for better policy frameworks, comprehensive training programs, and scalable infrastructure solutions. The research indicates that educators are not resistant to AI but rather constrained by practical limitations and organizational readiness. Future research linking AI-supported design practices to measurable improvements in instructional quality could accelerate adoption by providing evidence-based guidance. The DOT Framework's validation offers a preliminary measurement tool for assessing organizational AI maturity across higher education institutions.

• →Higher education practitioners hold favorable views of AI for teaching while prioritizing human oversight and critical evaluation in implementation.
• →Three key belief dimensions emerged: AI functional capabilities, governance and oversight requirements, and instructor collaboration in planning processes.
• →Significant gaps exist between design-oriented best practices and actual implementation, with practitioners underutilizing systematic needs assessment and feedback loops.
• →Institutional barriers including limited policies, insufficient training, and inadequate infrastructure remain the primary obstacles to effective AI integration.
• →The DOT Framework provides preliminary empirical support as a descriptive model for measuring practitioner beliefs and organizational AI adoption readiness.