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Educational Development

created 2026-08-09 · updated 2026-10-16

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 is the professional and institutional work through which faculty build the capability to design, teach and assess well — and in the AI era it has become a precondition for any pedagogical change to take effect. The concept spans the individual (AI literacy, confidence, competency, attitudes) and the institutional (standards, policy, quality assurance, and alignment between stated AI expectations and actual assessment design). A recurring finding across this knowledge base is that the evidence for effective practice substantially leads what most institutions have implemented, which makes development work — not more primary research — the binding constraint. The learning sciences produce the findings this work carries: where that field establishes empirically how people learn and which designs change outcomes, educational development is the institutional practice that moves such evidence into how faculty actually teach and assess. Its work, though, is bounded by an institutional edge: development builds capability in the staff inside an institution, while the tools those staff are then asked to adopt are usually built outside it by Educational Technology Developers, whose design and procurement decisions — defaults, configurability, what survives the funding — arrive as constraints on the program rather than as products of it.

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.

  • Check what a framework's levels describe before adopting it. Across 16 institutionally endorsed AI competence frameworks, AI use, output evaluation and domain application were substantive in all 16, while problem formulation (7), data competence (9) and AI creation (8) were not; proficiency levels also describe learner development, occupational role or grade, so they are not directly comparable (Fitsilis (2026)).

  • 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.

  • AI-mediated professional reflection. The Reflective Triangle Model positions AI as a cognitive mediator across self-reflection, collaborative reflection, and community reflection in a professional learning community, tested in a four-week feasibility case with six teachers; the boundary it draws is that individual insight does not automatically become shared professional knowledge (Nguyen (2026)).

  • AI Governance and policy: Institutional AI policy analysis documents the gap between institutional ambitions and faculty support capacity.

  • AI literacy is institutionally invisible. A systematic review of 32 studies of language-teacher AI literacy found it absent from workload models, promotion policies and resource allocation, with development fragmented and accountability displaced across institutions and agencies — so literacy development defaults to individual effort rather than structural support (Deng, Çelik & Duran, 2026).

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 Learner 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 and Awe: Making Sense of Generative AI Through Metaphor)

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).(When faculty ask, 'what's the point of teaching?': GenAI as identity crisis, not skills gap)

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.(When faculty ask, 'what's the point of teaching?': GenAI as identity crisis, not skills gap)

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).(When faculty ask, 'what's the point of teaching?': GenAI as identity crisis, not skills gap) 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.

Empirical support for the identity-work framing. Farazouli et al. (2026) provide direct qualitative evidence for this account: 24 Swedish university teachers described GAI's emergence as alarming and overwhelming, reporting a "state of vulnerability" (low confidence, insecurity, fear of "not being ahead of students") and feeling "stuck" between utopian and dystopian discourses. Their limited knowledge and experience, alongside feelings of vulnerability, highlight teachers' lack of readiness to navigate, assess, and adopt GAI — and the authors argue institutions must provide designated spaces and time for teachers to experiment with GAI, exchange ideas, and collaboratively develop practices and guidelines at institutional, departmental, and course levels. This grounds the identity-work and support-oriented approach in teachers' own reported experience rather than only in theory.

A programme that worked the identity question directly. Chick, Morello & Staffey (2026) report a six-week summer institute that took ten faculty from Education, Health Sciences, Business and other programs out of low AI fluency (mean self-rating 1.89 on a six-point scale) to 60% reporting high confidence in guiding student AI use and 90% positive perceptions, and they attribute the shift less to tool training than to structured, safe experimentation that opened with the existential question of professional value rather than with demos. The vocabulary change is the marker they lean on: early framings of AI as a "cheating machine" or a "dehumanizing force" gave way to "curious assistant," and their four themes — fear to curiosity, a desire for ethical clarity the policy vacuum did not supply, inclusive design as an equity amplifier, and faculty moving from gatekeepers to guides — describe a partial, deliberate reorientation of instructional identity. The ten capstone redesigns are the harder evidence, with eight of ten positioning AI as a scaffold supporting rather than replacing thinking, and the authors' symbiotic pedagogy formalises what participants built: complementary design, transparent process, critical integration, embedded ethical reasoning and iterative refinement. The caveat is structural rather than individual: the same participants named contradictory policy signals, personal tool subscriptions and no time release for the redesigns, so the authors conclude that resistance was never the problem and that piecemeal responses leaving policy and infrastructure untouched will fail even an enthusiastic cohort.

Development as mediation, and the first question a developer asks. Sithole (2026) studies academic development — the field's older name, and the term used by the International Journal for Academic Development, which published the paper — at two South African Historically Disadvantaged Institutions, and reframes developers not as technical implementers but as digital mediators working across three registers: pedagogical (translating AI discourse into viable teaching, learning and Assessment practice), ethical (holding automation against Academic Integrity, innovation against care), and institutional (working the liminal space between management imperatives and academic concerns). Two findings matter for program design. First, the filtering question is contextual before it is technical — "what does this mean for teaching here, for our students from poor schools?" — and slowing adoption down counts as part of the work rather than resistance to it. Second, the affective cost is structural, not personal: developers were expected to project expertise in tools whose implications they were still working out ("I feel like an impostor. I am learning AI as I go, but the institution expects expertise"), and their response was peer professional learning aimed at judgment rather than skill, including deliberately trying to break the tools. The paper's analytical contribution is to separate digital inequality, a distributive problem, from algorithmic coloniality, an epistemic one that survives full access — and to place both inside Global South higher education, where development work is expected to be transformative rather than a neutral support service.

Guilt and moral discomfort as a developmental signal. Vassallo (2026) supplies the affective counterpart to the identity account. In a survey of academic staff at a Maltese university (109 responses, a 3.5% rate the paper reads as itself informative about the state of ethical debate), guilt tracked concealment rather than disclosure — limiting AI use because of unease correlated with the AI Guilt Index at r = .62 and avoiding disclosure to colleagues at r = .50 — while formal disclosure in academic outputs showed no relationship (r = .08). Only 31.2% saw clear institutional guidelines, and the correlation between that clarity and lower guilt was weak (r = −.25, p = .010), which the paper reads as evidence that top-down mandates cannot resolve a partly emotional and identity-based problem. Its recommendations are directly developmental: treat guilt and identity concern as normal transitional responses rather than faults to correct, use structured low-stakes experimentation to move staff through anticipatory anxiety, and lean on mentorship by senior academics, since guilt was highest among early-career staff (M = 2.71) and lowest among senior ones (M = 2.03).

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.(AI Adoption Among Teachers: Insights on Concerns, Support, Confidence, and Attitudes)

Confidence and literacy are not interchangeable targets: in a 502-respondent SEM of university faculty, AI literacy was the dominant predictor of AI well-being (β = 0.713) while technological self-efficacy contributed little (β = 0.082), and support reached well-being only indirectly (Liu et al. (2026)).

Treat guideline demand and guideline adequacy as separate problems. Faculty at one university strongly backed institutional measures against unethical AI use (M = 4.59) while rating existing guidelines lowest (M = 2.99), and named data privacy and cultural inclusivity their weakest knowledge - a concrete target for professional development (Bilgic & Sever, 2026).

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.(Faculty Readiness for AI-Supported Teaching and Scalable Online Program Delivery in Higher Education: The EPIQ-AI Framework for Epistemic Integrity) Do not equate infrastructure investment with transformation. Across 125 studies, institutional investment in GenAI infrastructure and educational development correlated only weakly with actual pedagogical change (r = 0.12), while 60–70% of instructors felt unprepared to integrate GenAI — making development, not procurement, the binding constraint.(Rathnayake (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).(A Framework for Institutional Change in the Age of AI)(A bit of chaos and madness: The AI Assessment Scale and the work of assessment reform)

Convene the village, not the individual. Across 12 academics and 17 students interviewed, GenAI-era assessment redesign exceeded any single academic's capability — inflexible processes, long lead times and workload burden were named — so the authors propose a program-wide team combining assessment-design, GenAI, subject-matter, program and industry expertise (Nicola-Richmond et al., 2025).

Run the series on the room's own data rather than imported best practice. Perl-Nussbaum and Finkelstein (2026) document a worked model from a large public R1 physics department: six biweekly sessions of 60 to 75 minutes, nineteen faculty participating across the series and about ten at any meeting, in which every session opened with local data or department-sourced materials, moved to small-group testing of AI against real anonymized student homework, and closed in collective discussion. What faculty found by testing was more useful than any package the facilitators could have imported — AI solved every problem they tried, but its feedback on real student work was uneven, prompt-dependent and surface-focused unless given explicit goals and rubrics, and students prompted shallowly and modified output superficially unless productive use was modeled. The first concrete output was a living repository of five entries — syllabus policy statements, classroom discussion materials, the student survey, AI-integrated homework tasks and assessment structures — which is why the authors treat the session structure itself as the argument rather than any content it delivered.

Anchor in a competency framework. Development should also build pedagogical prompt literacy — the capacity to encode pedagogical intentions into prompts — since Talebzadeh (2026) finds it is pedagogical expertise, not AI fluency, that determines the quality of teachers' AI-assisted design work.(AI-accelerated End-to-End Framework for Rapid Professional Upskilling)(AI literacy-related domains and AI-TPACK readiness among preservice mathematics teachers: A factor-informed structural equation modelling study)(The Architecture of Roles in AI-Designed Group Activities: A comparative inductive analysis of novice and experienced teachers' differentiated instruction within the IAT framework)

Sequence understanding before automation. A critical discourse analysis of 14 pieces of gray literature published between November 2022 and April 2025 advising practitioners on generative AI for constructive alignment finds that capability must precede tool access: because quality of engagement precedes quality of understanding in a Constructivism framework, risk scales inversely with familiarity, and novices are the most exposed to plausibility-driven acceptance of plausible-looking but pedagogically thin outputs. McInnes et al. (2026) further argue that development units should resist a technicist brief — helping faculty "acquire fluency" in tools that do "the heavy lifting" of course design — because that framing shuts developers out of a closed educator–GenAI loop and recasts values-led, relational development as a mediating mechanism for software adoption. The defensible alternative keeps educational developers as curators and rule-setters of any institutionally bounded retrieval-augmented alignment assistant that poses probing questions, flags under- and over-assessment and generates alternatives but never finished artifacts, with escalation to human judgment on accreditation and cross-program matters treated as a feature rather than an exception.

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 and Awe: Making Sense of Generative AI Through Metaphor)

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.(How to Assess AI Literacy: Misalignment Between Self-Reported and Objective-Based Measures)(Efficacy of an Intensive Generative AI Professional Development Program on Pedagogical Content Knowledge (AI-PCK) and the Comparative Analysis of Learning Gain between Experienced and Pre-service Teachers)

Do not assume subject-matter expertise carries over to GenAI. Sutedjo, Chowdhury & Liu (2026) surveyed 127 U.S. research-university faculty using the TPACK-21 instrument and found content knowledge (CK) showed no significant correlation with technological knowledge or any technology-integrated domain (r =.11–.15, ns) — disciplinary expertise did not predict GenAI-integration knowledge. The technology-integrated domains (TPK, TCK, TPACK) inter-correlated so strongly (r =.81–.91) that they may function as a single GenAI-integration factor. Practically: build GenAI integration through discipline-specific activities that connect GenAI affordances to each faculty member's subject matter, and treat the technology-integrated domains as one shared GenAI-literacy foundation rather than train them as separate skills.(Assessing faculty self-perceived knowledge in using generative AI to teach 21st-century skills)

Discipline-specific smart-classroom models. Zhu, Liang, Mao, and Wang (2026) show how a mathematics M.Ed. course can be enhanced with intelligent educational technologies (automated scoring, personalized recommendations, multi-AI feedback) integrated across pre-, in-, and post-class stages within a three-dimensional smart-classroom framework. Their quasi-experiment found statistically significant gains in instructional-objective design proficiency, offering a transferable D-T-E Model (Disciplinary Demand–Technological Empowerment–Evaluation Loop) for teacher educators and educational developers looking to move smart-education frameworks from macro concepts into discipline-specific practice.

Measure attendance, not the feeling of engagement. In a six-month Intelligent-TPACK program for 64 university teachers, attendance rate predicted the gain in AI competency (β = .407, p = .001) while self-perceived participation did not (β = .049, p = .692), which the authors read as concrete behavioral investment against surface-level engagement (Tan, Cheng & Ling, 2025). The program paired online lectures with three face-to-face workshops, then required teachers to revise their instructional designs and implement them the following semester, and the authors recommend tying attendance into appraisal alongside meaningful incentives.

Benchmark against what faculty themselves report. Watson & Rainie (2026)'s survey of 1,057 US faculty finds the institutional layer thin in ways program designers can measure directly: 59% judged their school unprepared to use generative AI effectively for preparing students for the future and 68% said it had not prepared faculty to use it for teaching and mentoring, while the structural response ran to a task force in 55% of institutions but an AI literacy general education outcome in only 13%. Faculty had not waited for policy — 87% wrote their own assignment-level rules against 48% who could point to an institutional one — and they named colleagues' resistance (82%) and unfamiliarity (83%), not mandate, as the obstacles to departmental adoption. For development programs this argues for treating peer norms, shared assignment-level policy language and explicit measures of institutional readiness as part of the intervention rather than leaving them to the policy document.

Instructor response reflects power centers, not attitude. In an instrumental case study of 33 writing instructors, four polycentric power centers — the global GenAI trend, the university/department, colleagues, and students — shaped practice, and adjuncts constrained by limited discourse power shifted from detection toward teaching acceptable use (Zuo et al. (2026)).

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Metadata

LevelHigher Ed, K 12
Intended audienceFaculty Developers, Instructors
Institutions and policyEducational AI Policy
Foundations of AI in educationAI Literacy, Curriculum Design, Learning Design, Teaching, Teacher AI Competency
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