Atherton, Descamps, Holmes, Vizcarra, Sui, Webel & Foley (2026) — arXiv:2605.18140
Key Finding: AI Pedagogical Orientation
A mixed-methods survey of 90 STEM faculty in the RCSA Cottrell community identified a coherent latent construct — AI pedagogical orientation — that strongly predicts AI adoption across research, teaching, and professional activities. This orientation reflects deep beliefs about AI's role in disciplinary thinking, learning, and expertise development — not merely positive or negative sentiment.
Why This Matters
Current technology-adoption models (scaffolding-like frameworks such as DoI and UTAUT) emphasize external conditions, perceived usefulness, and social influence. This study demonstrates that for AI in higher education, these factors are surprisingly weak predictors. Instead, a faculty member's epistemic interpretation of AI — their stance on what AI means for disciplinary knowledge production — is the primary driver.
Study Details
- Population: 90 STEM faculty (from 572 RCSA Cottrell awardees, 16% response rate), Fall 2025
- Method: Mixed-methods survey → Exploratory Factor Analysis (36 variables) + qualitative coding
- Key result: One dominant factor (AI pedagogical orientation, 9 items) consistently predicted AI use
- Weak predictors: Institutional initiatives, demographics (discipline, career stage, institution type), information sources
- Information flow: Department colleagues (64%) and discipline-specific news were top sources; institutional support mechanisms were less used
Connections to Wiki
Faculty Development
This paper challenges the implicit theory behind many faculty-development-genai programs: providing tools, workshops, and institutional support may be insufficient if faculty have not developed a coherent pedagogical orientation toward AI. The Cottrell community is already pedagogically motivated — yet 22% used no AI at all. Faculty development must help instructors articulate what AI means for their discipline, not just how to use it.Teacher Adoption
The finding that concerns do not moderate adoption contrasts with teacher-ai-adoption-confidence, where institutional support → confidence → attitudes. This paper suggests a different mechanism: orientation shapes adoption directly, without being mediated by confidence or moderated by concerns. The teacher-role literature may need to incorporate epistemic dimensions alongside attitudinal ones.Institutional Change
The weak association between institutional initiatives and AI use is a cautionary note for institutional-change-framework-ai and change-management: top-down strategic plans and degree programs may have limited impact if they don't engage with faculty pedagogical orientations. Bottom-up, colleague-driven information flow (64% cited department colleagues) suggests peer networks are more influential than central initiatives.AI Literacy
The orientation construct connects to ai-literacy at a deeper level: it's not just about knowing what AI can do, but having a coherent philosophy about what AI should do in one's discipline. This aligns with the distinction between instrumental and critical AI literacy.Comparison to Computation Integration
The authors draw a deliberate parallel to the integration of computation into physics education — a decades-long process that succeeded only when faculty communities (e.g., PICUP) developed shared pedagogical frameworks. AI may follow a similar trajectory, where disciplinary communities — not institutions — drive adoption through shared epistemic norms.Qualitative Dimensions of Orientation
Faculty views clustered around three questions: 1. Disciplinary thinking: Should students learn to think with AI, or without it? 2. Learning: Is AI a prosthetic that extends learning, or a replacement that short-circuits it? 3. Expertise development: What does expertise mean when AI performs tasks once central to mastery?
These are not resolvable by more information or better tools — they require disciplinary conversation and pedagogical judgment.
Open Questions
- How stable is AI pedagogical orientation over time? Does it change with experience?
- Are there discipline-specific differences in the content of orientations (physics vs. chemistry vs. biology)?
- Can faculty development interventions shift orientation, or is it a stable trait?
- How does orientation relate to actual classroom practice (not just self-reported use)?
Related Pages
- cognitive-shift-ai-education — 471 students surveyed 2020–2026 show shift from AI preference to human intellige
- faculty-development-genai — Professional development for AI readiness
- teacher-ai-adoption-confidence — Teacher confidence pathway to adoption
- teacher-role — Teacher perspectives on AI in education
- teacher-ai-competency — Building teacher AI skills
- institutional-change-framework-ai — Institutional strategies for AI era
- change-management — Organizational change in higher ed
- ai-literacy — AI literacy as foundation
- higher-ed — AI in higher education context
- scaffolding — Scaffolding frameworks in AI education
- stem-education — STEM education context
- bridging-instructional-design-framework-math -- Proposes operationalizing learning theories as metadata dimensions for teacher-support systems in mathematics education, focusing on conceptual structure of content.
Citation
APA: Atherton, T. J., Descamps, I., Holmes, T. R., Vizcarra, C. L., Sui, N., Webel, M., & Foley, J. J., IV. (2026). Faculty orientations shape adoption of AI in research and teaching. arXiv:2605.18140.