📄 Full text: Every Learner · local · Liverpool Repository · local
Faculty professional learning around Generative AI is permanent, necessary, and structurally under-resourced. The Every Learner Everywhere / OLC playbook documents a field in pragmatic transition: not exuberant adoption, but grudging recognition that "you can't undo it."^every-learner-faculty-development-genai-playbook-2025
Study Design
Mixed-methods study of Centers for Teaching and Learning (CTLs):
- Survey: n=42 CTL leaders/directors (US and Australia)
- Interviews: n=18 semi-structured follow-ups
- Analysis: Descriptive statistics + thematic analysis (AI-assisted)
Key Findings
1. GenAI Is Permanent
Across all data, respondents agreed GenAI is here to stay. 25.64% explicitly called integration a necessity, not a choice."You can't undo it...we have to embrace what teaching in the reality of AI means."
2. Diverse PL Models
Common formats: webinars, book clubs, microcredentials, modular courses, department-specific consultations. Successful examples include Auburn's "Teaching with AI" modules and discipline-specific trainings.3. Pragmatic Engagement With Nuanced Resistance
Participants are typically pragmatic realists, not enthusiasts."They're not exuberant, but they realize they can't ignore it."
Adoption highest in STEM and education; humanities show slower, more cautious uptake due to authorship, creativity, and ethics concerns.
4. Decentralized But Guided Policies
Institutions avoid rigid mandates in favor of flexible guidance (e.g., Traffic Light model). Academic integrity codes are being updated to explicitly address AI-generated content.5. Dual Pedagogical Impacts
| Positive uses | Negative impacts |
|---|---|
| Brainstorming, assignment revision | Over-reliance |
| Feedback generation | Student deception |
| Clinical/case simulations | Degradation of critical thinking and confidence |
"My students still trust AI more than they trust their own expertise."
6. Resource and Structural Constraints
Programs are under-resourced and over-extended. Some CTL staff pay out of pocket for GenAI licenses. Faculty caught in "a sea of competing priorities." Dedicated localized AI champions within colleges are needed.7. Future Direction
Reusable modules, just-in-time PL, assessment redesign, chatbot integration in LMS, institution-wide governance. Agility (short-term planning, rapid iteration) is essential.Multimodal Integration Scaling (Varga-Atkins et al., 2025)
Beyond the Every Learner playbook's center-level focus, the Liverpool Guide offers a four-scale framework for embedding GenAI literacy:
Individual Level
- Students: workshops on creative multimodal tasks, prompt crafting practice, reflective assignments documenting AI use
- Educators: professional learning, low-stakes experimentation, ethical reflection
- Educational Developers: curate examples, develop guidelines, coach staff on GenAI-enabled pedagogies
Module Level
- Embed GenAI literacy into learning outcomes (e.g., "critically evaluate AI-generated design concepts")
- Offer optional multimodal tasks with clear rubrics and support
- Include creative and reflective components where students analyse and critique their own or others' GenAI use
Programme Level
- Develop cross-module policies and examples on GenAI use
- Promote consistency and transparency via workshops, cross-staff activities, and discussion tasks
- Align GenAI practices with graduate attributes (criticality, creativity, digital fluency)
- Consider embedding AI literacies in shared academic skills modules
Institutional Level
- Provide clear policies with checklists for permissible uses
- Offer vetted tools and enforce data privacy protocols
- Avoid rigid mandates in favor of flexible guidance (e.g., Traffic Light model)
This scaling shows that faculty development is not enough — the playbook's Stage 1–3 must be complemented by module, programme, and institutional infrastructure.
Four-Stage Development Model
Stage 1: Awareness and Foundations
Build comfort, trust, and basic understanding without pressuring adoption.- Ethical/responsible use (integrity, bias, environmental impact)
- Low-stakes entry points: "AI Playgrounds," informal labs, book groups
- Respect faculty autonomy; address fears
- Start with familiar use cases: syllabus creation, assignment design, emails, feedback
Key stat: 92.86% of CTLs offer facilitated workshops; 83.33% offer one-on-one consultations.
Stage 2: Engagement and Skill Building
(truncated in source but follows natural progression)Stage 3: Integration and Innovation
Full curricular redesign, assessment transformation, and AI-aware syllabi.Continuous: Frequent Iteration
Non-linear cycle of rapid iteration — agility over multi-year planning.Relationship to AI-in-Ed Research
The playbook's findings directly connect to research threads in the wiki:
- Student over-reliance → SafeTutors cognitive-offloading harms
- Critical thinking degradation → metacognitive suppression
- Traffic Light policy models → human-in-the-loop-ai structured governance
- Humanities resistance → authentic-assessment concerns about authorship and creativity
- Assessment redesign → formative-assessment and authentic-assessment frameworks
Related Pages
- learner-centered-feedback-ai — Experience gap: novices benefit, experts wary; de-skilling risk
- chatgpt-critical-creative-thinking-review — Systematic review: ChatGPT's dual impact on critical and creative thinking in higher education (67 studies)
- ai-pedagogical-orientation — AI pedagogical orientation drives faculty AI adoption more than institutional factors
- students-llm-usage-critical-thinking — Informing strategies for guiding student AI use
- institutional-change-framework-ai — Six-dimension framework for adapting institutional change models in STEM to generative AI
- teachingcoach-chatbot-instructor-guidance — Fine-tuned chatbot outperforms GPT-4o mini on clarity and reflectiveness
- universities-ai-era-rethinking — CTLs as agents of institutional AI transformation
- critical-thinking-genai-scaffolding — Vendrell & Johnston (2026): eight design principles for scaffolding critical thinking with LLMs in higher education.
- state-policy-teacher-ai — NASBE/CRPE: five state policy recommendations for teacher AI adoption
- ai-higher-ed-workforce-survey — EDUCAUSE survey: 94% use AI, 54% policy awareness, 56% shadow AI in higher ed
- ai-education-global-capacity — global perspective: human/institutional capacity as AI-in-education bottleneck
- ai-adult-learning-design — 19 design guidelines for AI technologies supporting adult learners
- multimodal-learning-genai — Module-level and programme-level strategies for GenAI integration
- principled-ai-education — Finkelstein's goals-models-technologies framework as a lens for faculty development design
- ai-tutor-safety-harms — Student harms that faculty development should address
- human-in-the-loop-ai — Governance and teacher control in institutional adoption
- formative-assessment — Assessment redesign as a core faculty development topic
- authentic-assessment — Redesigning assessment for an AI-present world
- ai-literacy — Student-facing literacy; faculty need analogous professional literacy
- educational-llm-alignment — Misalignment awareness for faculty evaluators
- metacognition — Faculty awareness of how their GenAI use models behavior for students
- multi-agent-instructional-design — K-12 teachers AI adoption patterns and tool development
- ai-literacy-equity-programming-policy — Teacher AI-literacy preparedness (2026-07-14)
- ai-tpack-teacher-multi-agent-workflow — Three teacher archetypes for AI workflow design and differentiated scaffolding
- ai-generated-slides-student-perception — GenAI slide generation and student perception
- teacher-ai-adoption-confidence — Institutional support builds teacher AI confidence