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Educational Development

Educational Development (also known as faculty development) — the processes, programs, and institutional supports that help educators develop the skills, confidence, and professional identity to teach effectively with AI. Educational development spans individual training, curriculum redesign, institutional policy change, and the cultural work of making sense of what GenAI means for the academic profession.

Questions to Consider

  • If faculty in the same department describe AI as a human-like 'assistant' on one hand and a mere 'tool' or 'search engine' on the other, can a training program really succeed before those underlying mental models are surfaced? What might happen if development skips that step?
  • Educational development is often treated as closing a skills gap — teach faculty the tools and they'll adopt them. But some research frames GenAI integration as an identity-level change ('what's the point of teaching in a GenAI world?'). Which framing do you think is more accurate, and what different actions does each one imply?
  • Readiness frameworks like EPIQ-AI split faculty readiness into epistemic, pedagogical, institutional, and quality-and-compliance domains. If readiness is a sociotechnical alignment problem rather than an individual skills gap, what does that say about where a lone 'AI workshop' is likely to fall short?
  • When have you felt your own assumptions about a new technology surfaced and shifted — through discussion, metaphor, or shared language rather than instruction? What role do you think shared language and metaphor-analysis play in helping educators make sense of AI?
  • Consider a low-tech 'metaphor workshop' where faculty, staff, and students articulate whether AI feels like a Swiss army knife, a helper, a black box, or a competitor. Could surfacing fears (including the fear that AI will replace teaching roles) do more for adoption than more technical training?

Introduction

Educational development in the AI era

  • Readiness frameworks: The EPIQ-AI framework identifies four readiness domains: epistemic, pedagogical, institutional, and quality-and-compliance. Faculty readiness is a sociotechnical alignment problem, not just an individual skills gap.
  • Standards for technology integration: Crompton et al. use design-based research to develop faculty standards for technology (incl. AI) integration in higher education institutions.
  • Adoption and confidence: Teacher AI adoption research identifies concerns, support, confidence, and attitudes as key predictors. Faculty-development programs must address all four.
  • Curriculum integration: Institutional change frameworks and assessment reform require faculty to redesign courses, not just add AI tools.
  • Training programs: AI upskilling frameworks and TPACK-based preservice training provide models for structured faculty AI education.
  • Governance and policy: Institutional AI policy analysis documents the gap between institutional ambitions and faculty support capacity.

Metaphors and shared language in educational development

Faculty hold heterogeneous, often deeply ambivalent mental models of AI, and effective development must surface and work with them. Gerhardt et al. show that engineering instructors frame AI through metaphors that both construct and constrain understanding (AI as a human-like "assistant" vs. a "tool" or "search engine"); because instructors within the same department often hold fundamentally different conceptualizations, a shared, accurate language about GAI is a prerequisite for effective faculty development and productive departmental adoption discussions.

A practical method for surfacing these mental models is the metaphor-analysis workshop. Vallis, Wilson & Casey (2025) designed and validated a low-tech, collaborative workshop for faculty, staff, and students to articulate their metaphors for generative AI. Participant metaphors clustered into four categories — Functions (tool-like: "Swiss army knife"), Roles (human-like: "helper," "frenemy"), Qualities (unknowable: "black box," "slippery slope"), and Agency (threatening: "competitor," "sinister robot") — surfacing persistent tensions between human vs. machine Agency and the known vs. the unknowable. The workshop helped participants surface assumptions, connect across roles, feel "not alone," and think about the ethics of GenAI, without requiring technical expertise. As a development tool, it gives program designers a low-barrier way to surface a team's assumptions before redesigning Assessment or rolling out policy, and to address fears head-on — including the fear that AI will replace teaching roles.(Fear Awe GenAI Metaphor Workshops 2025)

GenAI as identity work, not just upskilling

A threshold-informed view reframes GenAI integration as an ontological transformation for faculty, not a skills gap. Applying threshold concept theory (Meyer & Land), Laidlaw argues that GenAI exhibits all five threshold characteristics — transformative (reconstructs assumptions about assessment, pedagogy, and professional role), troublesome (violates beliefs about originality, human agency, and effort–achievement), irreversible, integrative (connects technology, pedagogy, epistemology, and identity), and bounded (GenAI fluency becomes a new marker of professional currency).(Laidlaw GenAI Identity Crisis Faculty 2026)

On this account, faculty asking "what's the point of teaching in a GenAI world?" are not deficient in competence; they are in a liminal threshold-crossing phase where anxiety, resistance, and confusion are necessary parts of transformation, not obstacles to eliminate. Skills-based training that answers a competence question faculty are not asking can become peripheral to the real transformation, and well-intended governance can lapse into an "enforcement illusion" — communicating rules rather than supporting change.(Laidlaw GenAI Identity Crisis Faculty 2026)

Design implication: complement (don't replace) skill building with identity-supporting practices — open sessions with identity questions rather than technical demos, run ongoing discipline-specific cohorts where faculty explore what GenAI means for their field's purpose, create peer-mentoring structures that honor different transformation timelines, and distinguish fear-based hesitation (which benefits from support) from principled non-adoption grounded in legitimate disciplinary values (which deserves respect).(Laidlaw GenAI Identity Crisis Faculty 2026) The metaphor-workshop model above is one concrete instantiation of this: rather than starting from technical upskilling, it opens with the interpretive, identity-laden question of what GenAI means to participants.

Connections

Educational development connects to Teacher AI Competency (the outcome), Teacher Role (how AI changes instructional work), AI Literacy (faculty must model AI literacy for students), and Educational Policy AI (institutional policies that enable or constrain development).

Practical guidance for program designers

For faculty developers, academic leaders, and instructional designers planning AI professional development, the knowledge base's evidence suggests:

Address the four adoption drivers, not just knowledge. Confidence, attitudes, support, and concerns predict whether faculty actually adopt AI — a knowledge-only workshop that ignores these is unlikely to change practice. Design development to build confidence through hands-on use, provide ongoing support (not one-shot training), and actively surface and respond to faculty concerns.(Teacher AI Adoption Confidence)

Treat readiness as a sociotechnical alignment problem. The EPIQ-AI framework shows faculty readiness spans epistemic, pedagogical, institutional, and quality-and-compliance domains. Programs that only train the individual miss the institutional levers (policy, workload, incentives, quality standards) that enable or block change — align those alongside training.(Sangwa Epiq AI Faculty Readiness 2026)

Build toward curriculum redesign, not tool adoption. The goal is faculty redesigning courses and assessment, not just adding AI tools. Ground professional development in course-level redesign work and assessment reform, and give faculty structured frameworks for doing so (e.g. assessment scales, institutional change frameworks).(Institutional Change Framework AI)(AI Assessment Scale Reform)

Anchor in a competency framework. Use a structured model like TPACK (TPACK-based training) or an AI upskilling ladder (AI upskilling frameworks) so that development is sequenced and measurable rather than ad-hoc, and so faculty can see their own progression toward Teacher AI Competency.(Crewscaler AI Upskilling Framework)(AI TPACK Preservice Math Teachers)

Surface and address fears and mental models. Before redesigning teaching, use a metaphor-analysis workshop (Vallis, Wilson & Casey 2025) to surface a team's assumptions and anxieties about GenAI — including the fear that it will replace teaching roles — and build development that responds to them rather than ignoring them.(Fear Awe GenAI Metaphor Workshops 2025)

Model AI literacy and measure real gains. Faculty development should itself embody the practices being taught — using AI pedagogically, evaluating outputs critically — and should assess demonstrated competence rather than self-reported confidence, since self-perception reliably overestimates AI skill.(AI Literacy Assessment Misalignment)(GenAI Pd AI Pck Learning Gain 2026)

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