Date
Publisher
arXiv
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 (α = .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.
Study at a glance
- Question: How do educators perceive, use, and face institutional challenges when integrating artificial intelligence into their practice?
- Setting and data: Online survey of 72 higher education practitioners, primarily US-based faculty and instructional designers, mid to late 2025.
- Method: Descriptive analysis and exploratory factor analysis of cross-sectional survey data.
Why it matters
Without shared institutional frameworks, educators develop ad hoc practices that vary in intentionality and alignment with educational goals. Understanding practitioner perspectives is essential to guide institutional strategies, professional development, and human-centered educational frameworks.
Key findings
- Factor analysis revealed three core belief dimensions: AI functional capabilities, oversight and governance, and instructor collaboration and planning, explaining 54.4% of total variance.
- Practitioners prioritized human oversight, with 97% agreeing AI-generated materials require critical evaluation, while 71% rejected replacing instructor expertise.
- Educators frequently used AI to generate materials and refine prompts, but engaged least with automated grading, personalized feedback, or systematic student needs assessment.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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