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

Chesapeake educators should pay attention because major vendors are embedding AI into everyday classroom tools: the Microsoft 365 Roadmap Teams Copilot announcement notes a Teams Copilot feature that can analyze screen‑shared content (rollout starts August 2026), and Microsoft guidance confirms Copilot prompts and responses can be stored in user mailboxes - raising privacy, compliance, and classroom workflow implications (Microsoft Copilot and Office 365 privacy guidance).

That means routine tasks - summaries, rubrics, basic technical writing and attendance notes - can be automated, so the practical “so what?” is immediate: learn to use and govern these tools to keep instructional value and student data safe.

For Virginia educators wanting hands‑on, job‑relevant AI skills, Nucamp's 15‑week AI Essentials for Work teaches prompts and workplace AI applications; syllabus and registration are available here: Nucamp AI Essentials for Work syllabus and course details.

Selections began with Microsoft's empirical list - built from 200,000 anonymized Copilot interactions mapped to O*NET task data - so priority went to education roles with high “AI applicability” scores and task mixes that match generative‑AI strengths (writing, research, communication); see the Microsoft study of jobs most exposed to AI (Microsoft study of jobs most exposed to AI).

Next, scoring methodology and real‑world usage patterns informed weighting (frequency of content‑creation tasks, grading and admin time, and remote‑friendly workflows), following the Forbes analysis of Microsoft's AI applicability score and its emphasis on observed Copilot use (Forbes analysis of Microsoft's AI applicability score).

Jobs that both ranked high and are core to Virginia postsecondary and extension systems - economics and business instructors, library science faculty, technical‑writing teachers, and farm/home management educators - moved to the top of the Chesapeake risk list because their day‑to‑day tasks are most immediately automatable; the practical takeaway: if a role spends hours on content, feedback, or information retrieval, it faces faster task disruption than hands‑on occupations.

“Our research shows that AI supports many tasks, particularly those involving research, writing, and communication, but does not indicate it can fully perform any single occupation.”

Postsecondary economics instructors in Virginia appear on Microsoft Research's AI‑applicability list - “Economics Teachers, Postsecondary” ranks 32 on the 40‑job list - because the job centers on tasks generative models do well: research synthesis, drafting lecture notes and explanations, answering student questions, and producing written feedback; the Fortune coverage of the Microsoft study frames these as the exact strengths LLMs automate (Microsoft Research study: jobs most exposed to AI).

The practical “so what?” for Chesapeake: routine content and information‑retrieval work can be accelerated, so using available training and governance funding matters - tap Virginia grant programs that offset AI training and implementation costs to learn how to integrate AI into assessments while preserving instructional judgment (Virginia AI training grant programs for educators); doing so frees time for activities AI can't replicate, like real‑time discussion, applied policy projects, and mentoring.

“Our research shows that AI supports many tasks, particularly those involving research, writing, and communication, but does not indicate it can fully perform any single occupation.”

Library science instructors face high exposure because day‑to‑day work - curating databases, running literature searches, teaching information‑literacy, managing metadata and digital‑preservation workflows - matches what generative models automate best: rapid retrieval, bibliographic synthesis, and first‑draft metadata generation; the University of Miami Libraries Databases A–Z (research databases list) documents the very services at risk (Databases A‑Z, Research & Write, Data & Visualization Services, subject librarians and research consultations) that AI can speed up.

Conference programming for library technologists also emphasizes the blending of information and automated workflows, highlighting that librarians now work at the nexus of data, systems, and policy - see the NLIT Summit conference agenda for library technologists.

So what? In Chesapeake, routine literature scans and bibliography prep will be faster, which means postsecondary library faculty should double down on higher‑value offerings - data stewardship, copyright and governance instruction, and critical source evaluation - and tap available Virginia training and grant programs for implementation guidance (Virginia AI training and grant programs for educators).

Microsoft Research ranks Business Teachers, Postsecondary 22nd on its list of occupations with high “AI applicability,” because many day‑to‑day tasks - drafting syllabi and lecture notes, assembling case studies, generating rubriced feedback, and answering routine student questions - align with what large language models do well; Fortune's coverage of the study underscores that writing, research, and communication tasks are the exact vectors for AI adoption (Microsoft Research study on occupations exposed to AI).

The practical “so what?” for Chesapeake postsecondary and vocational instructors: automating first‑draft content and standard feedback can be turned into a net gain - reclaiming hours to run supervised simulations, build employer partnerships, or deliver real‑time coaching - if districts and colleges pilot governed AI workflows using available Virginia training and grant support (Virginia educator AI training and grant resources).

“Our research shows that AI supports many tasks, particularly those involving research, writing, and communication, but does not indicate it can fully perform any single occupation.”

Farm and Home Management and Extension educators in Virginia are exposed because a large share of their output - family‑facing summaries, outreach charts, workshop handouts, and basic data syntheses - overlaps with what generative models automate best; Nucamp's template shows how to turn PBIS data into parent‑friendly chart explanations that distill complex information into one‑page takeaways (AI Essentials for Work: PBIS chart explanation templates and workplace AI skills).

The practical “so what?” for Chesapeake: routine document drafting and baseline analysis can be accelerated, freeing educators to focus on in‑person field demonstrations, community coaching, and applied troubleshooting that AI can't deliver.

To adopt responsibly, leverage Virginia grant programs that offset AI training and implementation costs for local organizations (Nucamp scholarships and Virginia grant resources for AI training) and study adaptive‑learning examples from Chesapeake classrooms to guide outcome measurement and iterative rollout (Web Development Fundamentals: adaptive‑learning classroom examples and implementation guides).

Technical writing instructors and lecturers in Chesapeake are especially exposed because their core day‑to‑day work - scoring multiple drafts, applying rubrics, and giving line‑level feedback - maps directly onto what automated essay scoring (AES) and AI‑assisted grading do well: fast rubric checks, grammar and structure analysis, and consistent first‑pass comments that scale across large classes (Ohio State: AI and Auto‑Grading in Higher Education - capabilities, ethics, and evolving role of educators; Automated Essay Scoring explained - how AI grades essays).

The practical “so what?” is concrete: research shows AI grading can reach parity with an overburdened grader on many routine checks - saving the dozens of hours instructors often spend on first drafts - while still requiring human oversight for nuance, voice, and curriculum decisions (Hechinger Report: proof points on AI essay grading effectiveness).

Chesapeake programs that pilot AI for first‑pass scoring, paired with clear disclosure and faculty review, can redirect saved time into higher‑value work: in‑class writing workshops, industry collaboration, and one‑on‑one conferences that preserve instructional judgment and learning outcomes.

“roughly speaking, probably as good as an average busy teacher”

Chesapeake educators can protect jobs and amplify instruction by taking three concrete steps now: enroll in no‑cost and low‑cost training through Virginia's new AI Career Launch Pad and scholarship programs to build baseline AI literacy (Virginia AI Career Launch Pad and scholarships), join hands‑on programs that teach prompt design and classroom application - for example, Virginia Tech's part‑time AI & Machine Learning Bootcamp and Nucamp's 15‑week AI Essentials for Work - and run small, governed pilots with district IT to automate only first‑pass drafting or grading while keeping human review for nuance and ethics (Virginia Tech AI & Machine Learning Bootcamp program; Nucamp AI Essentials for Work syllabus and registration).

One concrete detail that matters: Virginia currently reports roughly 31,000 AI‑related job listings, so demonstrating AI competence can translate directly into local opportunity.

Start by applying for VirginiaWorks/Grow with Google scholarships, schedule a faculty PD with a nearby university partner, and use clear disclosure + assessment rubrics when piloting tools so time saved becomes more coaching and less clerical work.

“AI is increasingly part of every aspect of work, and we're excited to launch this opportunity for Virginians to take part in this future.”