Practitioner Beliefs and Behaviors in AI-Enhanced Education: DOT Framework Survey Evidence
Quick Take
A survey of 72 higher education practitioners reveals positive views on AI in teaching, emphasizing the need for human oversight and highlighting institutional barriers like limited training and policy. The study supports the DOT Framework, identifying three belief factors: AI capabilities, governance, and collaboration.
Key Points
- Practitioners favor AI as a pedagogical tool, valuing human oversight.
- Three belief factors identified: AI capabilities, governance, and collaboration.
- Limited institutional policies and training hinder AI integration.
- Practices focus on iterative prompting and content generation.
- Study provides initial measurement structure for future research.
Article Content
From source RSS / original summaryarXiv: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.
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