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

AI is no longer an abstract threat in Wilmington, NC - it's already changing classrooms, campus policy and local workforce talks. UNCW's campus programs and a campus Community of Practice are prepping faculty and students even as AI-related honor-code cases climbed to 47% in 2024–25, underscoring that the issue is how, not if, AI will be used (UNCW AI higher education coverage).

Citywide conversations - from Wilmington University's “Impact of AI in the Workforce” panels to new community-college courses - are centering ethics, access and rapid reskilling.

That local push mirrors regional equity efforts and makes the choice practical: move from routine tasks to higher-value teaching by learning prompt-writing, practical AI workflows and classroom safeguards, or risk losing hours to automation; hands-on options include bootcamps such as Nucamp's AI Essentials for Work bootcamp and public events that break AI down into usable skills for educators (Wilmington University Impact of AI in the Workforce event).

“It's not a question of if we are going to use AI, but it's definitely a matter of how we are going to be using AI.” - Dr. Carol McNulty, UNCW

Learn more and register for the Nucamp AI Essentials for Work bootcamp: Register for AI Essentials for Work.

To pinpoint the five education roles in Wilmington most vulnerable to AI, the analysis combined task-level automation exposure scores (the LMI/O*NET 1–10 approach) with North Carolina employment data and education‑level correlations, then filtered for K–12 and postsecondary occupations dominated by routine, repeatable tasks - think grading, clerical data entry and fixed-response assessment design.

Practically, that meant mapping O*NET task weights to local workforce shares (the state shows slightly higher exposure than the U.S. with roughly 40% of jobs facing notable automation risk), prioritizing positions with lower typical credential requirements and high routine-task intensity, and validating those findings against real classroom use-cases where AI already automates lesson planning and initial grading workflows.

The method also layered policy and reskilling insight from state research - emphasizing worker‑focused retraining and employer partnerships - so the resulting list highlights not just who's exposed, but where prompt, targeted upskilling (from community colleges to short bootcamps) will make the biggest difference; see the North Carolina automation exposure analysis for the data and an AI‑in‑classrooms roadmap for common tool use-cases that drove our task-level checks (North Carolina automation exposure analysis, AI in classrooms roadmap).

“Technological change is simultaneously replacing existing work and creating new work. It is not eliminating work altogether.”

Basic administrative roles - school clerks, attendance clerks and routine data-entry staff - are squarely in the crosshairs of arrival-technology automation in North Carolina: pilots like New Hanover County's proposed AI school-safety system show how AI can plug into existing camera and alerting systems to send real-time notifications to principals, SROs or assistant principals (think a text ping the moment an incident begins), which can speed response but also replace repetitive monitoring and logging tasks (WECT coverage of New Hanover County AI school-safety pilot).

Local debate - including a narrowly failed school-board motion to accept the SB 382 grant - underscores privacy and vendor-trust concerns even as districts weigh efficiency gains (WWAY coverage of New Hanover County AI pilot decision).

The state's living AI guidance stresses job-embedded professional development, policy clarity and AI literacy so clerks can shift from rote entry to higher-value tasks (audit oversight, data verification, family outreach) - and short, practical reskilling (workshops or bootcamps) can turn an at-risk position into a coordinating role that supervises AI workflows (NASBE guidance on North Carolina AI policy for schools).

“The program could alert a principal or assistant principal to stop a possible dangerous situation in real time.” - Pete Wildeboer, New Hanover County School Board member

Grading and routine assessment design are already fertile ground for automation in North Carolina classrooms: a UNCW-led study on an adaptive automated-grading system found it can store correct/incorrect responses, speed up scoring and increase the quantity of feedback for computer-skills assignments - yet it did not improve the quality of written feedback, signaling that human judgment still matters (UNCW adaptive automated-grading study on computer-skills).

Modern tools split the difference - fast, objective scoring for multiple-choice, code and structured responses, and emerging LLM-driven assistance for essays - but researchers and practitioners warn about bias, transparency and reliability, recommending hybrid workflows that pair AI efficiency with educator oversight (OSU overview of AI and auto-grading ethics and capabilities).

For Wilmington schools the takeaway is practical: automation can shrink grading "stacks" from days to seconds and free time for coaching, but the highest-value pivot is toward designing better rubrics, auditing AI for fairness, and supervising AI-generated scores so students get faster, fairer, and pedagogically sound feedback (Automated grading systems benefits and considerations for educators).

Basic content creators and curriculum writers in North Carolina face a clear pivot: AI can now scan student data to spot knowledge gaps, draft modules, and recommend resources - speeding routine content creation and even personalizing lessons - so much so that automated lesson planning already “saves teachers hours” in real examples used by local educators (YSU article on AI impacts on curriculum design, Illinois College of Education article on AI in schools, Wilmington automated lesson-planning examples and AI prompts).

That means lower‑level drafting and template writing are most exposed, while the highest-value work becomes curriculum strategy: designing rigorous, AI-resistant rubrics, curating and vetting AI outputs for accuracy and bias, aligning materials to state standards, and building process‑focused assessments (think portfolios, oral defenses, or staged drafts) that reveal student learning.

The practical takeaway for Wilmington teams and district curriculum writers is vivid: keep the human in the loop - treat AI like a draft-generating sous‑chef that shaves prep time but still needs a chef to taste, adjust, and teach the craft of thinking and writing.

“If a student uses one of these bots as a tool, the student has to recognize whether or not the product answers the question.” - Dr. John Durkin

Entry-level tutors and online teaching assistants in Wilmington are already feeling AI's double-edged impact: AI-powered chatbots and voice agents are automating routine Q&A and out-of-hours support while intelligent tutoring systems (ITS) deliver instant, personalized practice and feedback that can reach students 24/7 - turning the midnight “why doesn't this step work?” question into immediate corrective guidance so human tutors can focus on deeper coaching (AI's impact on entry-level jobs, the rise of intelligent tutoring systems).

Importantly, a controlled trial of a tutor-facing AI assistant showed that giving AI to tutors - not students - increased tutors' capacity and raised student mastery (especially for novice tutors), which points to a practical local path: scale tutoring programs with AI copilots while investing in tutor training and AI literacy so entry-level educators move from answering repetitive problems to supervising, auditing, and coaching AI-driven learning paths (Tutor CoPilot trial).

For Wilmington districts and bootcamps that train new tutors, the clearest adaptation is hybrid skill-building - promptcraft, ITS oversight, and conversational UX - so early-career tutors become AI-augmented educators who expand access without losing the human relationship that motivates learning.

“By providing generative AI to the educator instead of directly to the student, you may get some of the benefits of this immediate feedback while still maintaining the really important parts of the in-person, relationship-based educational approaches.” - Susanna Loeb, quoted in Education Week

Library and information assistants in Wilmington face a fast-moving shift: AI is already handling resource discovery, metadata work and routine reference so effectively that many campuses are piloting chatbots and discovery assistants to give students instant, 24/7 help - picture a thesis-writer at 2 a.m.

getting a quick pointer to a full‑text article from a virtual reference bot - while staff pivot to higher‑value work like research consultations, instruction and ethical oversight.

Evidence from Clarivate's Ex Libris whitepaper shows over 60% of libraries are planning AI integration, and practical deployments (catalog enrichment, personalized recommendations, metadata assistants) can cut tedious cataloging and search friction; at the same time, cautionary research flags real risks - privacy, bias, budget strain and a two‑tiered digital divide - and a U.S. survey of academic library employees found modest AI literacy and a clear readiness gap, with many staff reporting little hands‑on experience.

The local strategy is therefore hybrid: treat chatbots and automation as workflow copilots that staff train, audit and escalate, and invest in focused AI literacy so information assistants move from routine responders to trusted curators and supervisors of AI-driven discovery (Clarivate Ex Libris whitepaper on AI in libraries, Overview of AI chatbots for library reference services, U.S. survey of academic library AI literacy and preparedness).

“The adoption of AI is likely to produce an impact and changes that go far beyond the local improvements that libraries may initially be looking for. … ensure AI benefits the broad academic and library ecosystem in the manner that is ethical, responsible, equitable and sustainable.” - Bohyun Kim, Associate University Librarian

Wilmington educators facing the fast pace of arrival‑tech should treat the moment as a threefold mandate: reskill, lead, and advocate. Reskill by building AI literacy now - NCDPI's guidance recommends job‑embedded professional development and curriculum infusion so teachers and support staff learn when to use AI, how to verify outputs, and how to design “AI‑resistant” assessments (NCDPI guidance on AI use in schools); districts can follow NHCS's practical stance that vets tools, protects student data, and requires clear classroom rules before adoption (NHCS generative AI resources and policy).

Lead by turning efficiency gains into higher‑value roles - use automation to shave hours off lesson planning and grading so staff can focus on coaching, assessment design and equity work - and advocate for equitable access, funding, and transparent vendor contracts so AI doesn't widen local divides.

For practical, career‑ready upskilling, short programs that teach promptcraft and workplace AI workflows (for example, Nucamp's AI Essentials for Work bootcamp) offer a hands‑on path to translate policy into classroom impact and new career options (Register for the Nucamp AI Essentials for Work bootcamp); think of it as reclaiming an evening of prep time and turning it into structured coaching that only humans can provide.

“It's not a question of if we are going to use AI, but it's definitely a matter of how we are going to be using AI.” - Dr. Carol McNulty, UNCW