🏷️ Concept
DOT Framework Survey: Practitioner Beliefs and Behaviors in AI-Enhanced Education
A 2026 cross-sectional survey (n=72) by Gibson, Azukas, and Knezek examined how higher education practitioners think about and use AI in teaching, grounded in the DOT Framework — a synthesis of Design Thinking and open-systems-theory.
Core Findings
Three-Factor Belief Structure
Exploratory factor analysis of 19 Likert-scale items yielded a three-factor structure with strong reliability (overall α = .90):
1. AI Functional Capabilities (α = .91) — perceived utility of AI tools for teaching tasks
2. Oversight and Governance (α = .79) — need for human supervision, policy frameworks, and critical evaluation
3. Instructor Collaboration and Planning (α = .85) — value of teamwork in AI integration design
This factor structure provides a psychometric anchor for understanding how educators conceptualize AI's role — not as a monolithic "good or bad" judgment but as differentiated beliefs spanning capability, governance, and collaborative practice.
Practice vs. Theory Gap
Practitioners reported frequent use of iterative prompting and content generation, but far less engagement with needs assessment and feedback loops — the front and back ends of a full design cycle. This gap between design-oriented theory and current implementation is the study's central diagnostic finding. It echoes broader patterns in AI Literacy where educators adopt AI for productivity but not yet for systematic instructional redesign.
AI as "Fallible Intern"
The study operationalizes AI as a co-intelligent collaborator following four tenets: (1) AI is fallible — always review, (2) AI supports not replaces higher-order thinking, (3) instructors should actively experiment, (4) instructor maintains agency. This framing aligns with ai-co-intelligence and Human AI Collaboration paradigms where the human remains the epistemic authority.
Institutional Barriers
Widespread lack of policy, training, and infrastructure was reported — consistent with findings across institutional-ai-readiness and AI Governance Education. Without institutional scaffolding, even motivated practitioners remain in ad-hoc, fragmented adoption patterns.
The DOT Framework
The DOT Framework integrates Design Thinking stages (Empathize → Define → Ideate → Prototype → Test) with Open Systems concepts (Environment, Input, Process, Structure, Output, Feedback) at both classroom (micro) and institutional (macro) levels. Key intersections:
This study provides the first empirical evidence supporting DOT as a descriptive model — practitioners' beliefs and behaviors partially map to its structure, but the gaps (needs assessment, feedback) reveal where the model is aspirational rather than descriptive of current practice.