Top 5 Jobs in Education That Are Most at Risk from AI in Richmond - And How to Adapt

Virginia's education leaders and classroom staff in Richmond are already feeling AI's ripple: a state-level push for clear rules and a startling BestColleges survey cited in local reporting found 60% of college students haven't been taught how to use AI tools ethically, even as many expect them to become normal in coursework - a concern outlined in the VCU report on the governor's AI directive (VCU report on the governor's AI directive).

At the same time, the University of Richmond's GenAI rollout and SpiderAI pilot show how campuses can provide controlled access - SpiderAI logged roughly 800 users and over 27,000 requests in fall 2024 - while Richmond Public Schools moves toward an AI task force; the takeaway is clear: teachers, librarians, and support staff should prepare now or risk being outpaced by tool-driven workflows, and practical training like Nucamp AI Essentials for Work bootcamp registration can bridge policy, pedagogy, and hands-on skills to make the transition manageable rather than disruptive.

“Well, that wasn't true… you can't stop the calculator, the computer, the internet, or AI. You have to adjust.” - Micah Voraritskul

To identify the top five education jobs in Richmond that face the greatest AI pressure, the analysis started with Microsoft Research's occupational study - a ranking built from 200,000 anonymized Copilot conversations that maps AI actions to U.S. work activities and produces an “AI applicability” score for each role (Microsoft Research generative AI occupational study (Copilot analysis)).

The team focused on occupations from Microsoft's top-40 list that are core to campus and district operations - postsecondary business, economics and library science teachers, technical writers, proofreaders and editors, web developers, archivists and customer-facing student support - because CNBC and other coverage show AI excels at information gathering, writing, and communication tasks that underpin these roles.

Methodology steps: extract high AI-applicability jobs from the Microsoft ranking, cross-reference where tasks are routine or digital, and then test local relevance using Richmond-focused examples such as admissions automation and onboarding workflows documented in regional case studies (Richmond admissions automation and onboarding workflows case study) and the technical pipeline described in reporting on Copilot data (Copilot conversation methodology report); the result is a short, practical list of education roles in Richmond where routine digital tasks are most likely to be augmented - or redistributed - by generative AI.

“Every job will be affected, and immediately. You're not going to lose your job to an AI, but you're going to lose your job to someone who uses AI.” - Jensen Huang

Postsecondary instructors in business, economics, library science and web development in Richmond are flagged as especially exposed because Microsoft's Copilot-based analysis shows high overlap between generative AI capabilities and the routine, information-heavy tasks these roles perform - research, syllabus design, grading, code examples, and cataloging are all activities that AI already handles well, and that can be scaled across courses and programs (Microsoft Research generative AI occupational impact rankings).

In practical Richmond terms that means faculty at VCU, UR, and community colleges could see parts of course prep and assessment automated, while library instructors face tools that can auto-index and surface resources - shifts local administrators are already testing in admissions and onboarding pilots (Responsible AI guidance and case studies for Richmond education institutions).

The upshot: these are not wholesale replacements but fast, measurable productivity changes that reward instructors who learn to steer and supervise AI-driven workflows rather than compete with them; a single well-crafted prompt can already produce a full lecture outline or scaffolded grading rubric in minutes, so adaptation is the practical advantage.

“You're not going to lose your job to an AI, but you're going to lose your job to someone who uses AI.” - Jensen Huang

Technical writers, proofreaders and editors in Richmond are squarely in AI's path because publishers and campus communications teams already use generative tools to spit out first drafts, ad copy, summaries and rough translations - work that once filled many editorial hours - so the role is shifting toward post‑editing, rights oversight and quality assurance.

The Authors Guild's best practices warn against treating LLMs as authors and call for disclosure, licensing and human-led revision (Authors Guild AI best practices for authors and publishers), while reporting from The Markup report on AI models translating literature and impacts on publishing shows translators and editors are already polishing machine drafts - and that many translators have lost work - highlighting a local risk that routine editorial tasks could be automated unless teams insist on careful post‑editing, cultural nuance checks and contract clauses that forbid unlicensed training on creators' work.

For Richmond employers and staff the practical move is clear: redeploy editorial skills toward supervising AI outputs, metadata and ethical compliance so the polished, publishable page keeps a human heartbeat rather than becoming a flood of unchecked machine text.

“Do not use AI to write for you. Use it only as a tool - a paintbrush for writing.”

Richmond's registrars, enrollment clerks, schedulers and other K–12 and postsecondary administrative staff are squarely in AI's crosshairs because the mix of routine scheduling, correspondence and record‑keeping they do maps directly onto the highest‑exposure task clusters in recent studies: Microsoft's Copilot analysis (built from 200,000 anonymized conversations) flags many contact‑center and clerical roles as highly applicable to generative AI, and global research finds clerical work has the largest share of highly and moderately exposed tasks - about 24% highly exposed and 58% medium exposed - meaning AI can already take over appointment-making, bulk communications and much of data entry unless processes change (Microsoft Research Copilot study on occupational impact of generative AI, ILO generative AI exposure analysis report).

Local pilots show how tangible this is: admissions automation and onboarding workflows can triage forms, auto-schedule advising slots and surface missing documents, so the practical risk is that routine admin work will be absorbed into automated pipelines unless Richmond districts and colleges redesign roles around oversight, exception-handling and student experience improvement (Admissions automation and onboarding workflows case study in Richmond education).

The “so what?”: administrators who upskill to manage AI-driven workflows will be the ones keeping the human touch when exceptions, privacy nuances, and relationship work matter most.

Admissions counselors, first-line student advisors and customer-service reps in Richmond are already seeing the contours of a new workflow: AI chatbots and automated admissions pipelines can confirm application status, schedule visits, route students to financial aid, and nudge admitted students through missing‑document checklists - tasks that once filled inboxes and phone lines (one admissions team reported getting 50–80 “did you get my transcript?” messages daily).

Tools like Element451's BoltBot show how a branded chatbot can deliver timely status updates and calendar bookings, while higher‑ed pilots demonstrate real outcomes - Georgia State's bot experiments increased fall starts and cut “summer melt” substantially - so routine triage can be handled at scale and around the clock.

The practical risk for Richmond staff is less about replacement and more about role change: if routine outreach and document checks are automated, the value shifts to exception management, relationship-building and privacy/ethical oversight.

Local teams that pair chatbots with clear escalation paths and a Nucamp-style admissions automation playbook will keep the human connection where it matters and reclaim hours for high‑touch advising (Element451 admissions chatbot for higher education, LearnWise guide to AI chatbots in education, Richmond admissions automation case studies and AI prompts).

“AI isn't just a trend; it's a new way of listening to learners at scale. By understanding what learners are searching for, we can conceptualize new ways to help them find the resources and tools they need to succeed.” - Lauren Gomez, Vice President of Technology and Innovation, Boundless Learning

Archivists, library technicians and media assistants in Virginia face fast, visible change because AI can now auto‑tag, index and surface assets at scale - turning dusty stacks and hour‑long lecture recordings into a browsable, searchable library in seconds - so routine cataloguing work like face/object recognition, transcription and preliminary subject assignment is increasingly automated by tools that “add structured metadata to every file” (Iconik AI metadata tagging guide for automated metadata) and by platform assistants that can process images, extract text and propose MARC fields for review.

That promise comes with limits and guardrails: Ex Libris' AI Metadata Assistant is built to suggest and enrich MARC 21 records (Phase I) while preserving cataloger oversight, normalization rules and authority validation so human experts still correct, approve, and add nuanced subject headings and local provenance tags (Ex Libris AI Metadata Assistant documentation and implementation notes).

In practical Richmond terms the “so what?” is simple: tech can reclaim hours of repetitive logging and surface hidden resources, but library jobs will shift toward quality control, taxonomy design, rights & privacy oversight, and exception handling - skills that keep the human judgment that discovery and cultural context require.

“The notion that freely-available, general-purpose AI systems are able to solve cataloguing problems easily, with the click of a button, if only the right prompt is created, is problematic to perpetuate – at least for now.”

Richmond and Commonwealth educators can adapt by treating AI as a policy-and-skills problem rather than just a technology one: lean on VDOE's Executive Order 30, AI Integration Guidelines and statewide supports (including the Generative AI teacher training that enrolled 360 educators) to build shared rules and classroom-ready practice, use the VirginiaHasJobs AI Career Launch Pad to access free and low-cost upskilling and scholarships, and pair local partnerships with higher ed to close resource gaps and co-design curricula for equitable AI literacy (VDOE Technology in Education guidance on technology in schools, VirginiaHasJobs AI Career Launch Pad for AI workforce training).

On the job-side, practical moves include shifting roles from routine processing to oversight and exception-handling (admissions automation, metadata QA, chatbot escalation), planning vendor-management and impact assessments given Virginia's AI regulatory work, and taking focused, hands-on courses - like Nucamp's AI Essentials for Work - to learn prompt-writing, workflow integration, and job-specific AI skills that turn exposure into opportunity rather than displacement (Nucamp AI Essentials for Work registration and syllabus).

Two memorable facts to guide planning: Virginia lists roughly 31,000 AI-related job openings while statewide initiatives are already funding teacher training and district cohorts to make classroom AI responsible, local, and practical.

“What we need is professional development by experts in specific fields that helps teachers understand what AI use looks like when it's not cheating. That helps students understand the ethics of it but also helps them get a handle on what the tool is, how it works, and how it can help them learn, especially as it relates to specific disciplines.”