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

Lawrence educators should pay close attention: generative AI is already reshaping higher education by automating routine grading, drafting lesson materials, and accelerating administrative workflows - changes documented in EDUCAUSE's analysis of generative AI in education and echoed across policy tracking that shows states moving fast to publish guidance and task forces (EDUCAUSE analysis of generative AI in education, Education Commission of the States guidance on AI task forces).

For K‑12 and postsecondary staff in Lawrence this means the near-term risk is less about wholesale job loss than about role-shift: tasks that follow predictable patterns are most exposed, while human strengths - mentoring, assessment design, equity-aware instruction - remain vital.

A practical response is rapid reskilling: a focused 15‑week program like Nucamp's Nucamp AI Essentials for Work bootcamp (early-bird $3,582; learn prompts, tool workflows, and workplace applications) equips educators with concrete skills to keep work meaningful and locally relevant.

"This is an exciting and confusing time, and if you haven't figured out how to make the best use of AI yet, you are not alone." - Bill Gates

Methodology: identification combined local reporting, KU research, and program artifacts to score roles by three practical axes - task predictability (how often a job follows repeatable rules), student‑facing legal/privacy exposure, and employer demand for AI skills - then ranked positions most susceptible to near‑term automation or role‑shift.

The process cross‑checked the University of Kansas' CIDDL guidance on audits, task forces and human‑centered policies (KU CIDDL guidelines for responsible AI in education), KU AAI reporting on local grants and workforce signals (including employer preference data and multi‑million dollar projects), and Lawrence‑area incidents and procurement records documenting real deployment and harms.

Concrete local markers - USD 497's $162,000 Gaggle purchase and hundreds of system detections - were weighted heavily for privacy/legal risk, while funded innovations such as KU's $2.5M iKNOW/VOISS work signaled where AI augments specialized instruction rather than replacing staff.

The result: roles that do high volumes of predictable grading, content moderation, or routine admin work score highest for immediate re‑skilling priority - so what: a single district purchase and its 408 detections moved surveillance‑adjacent roles to the top of the at‑risk list.

“We see this framework as a foundation. As schools consider forming an AI task force, for example, they'll likely have questions on how to do that, or how to conduct an audit and risk analysis. The framework can help guide them through that, and we'll continue to build on this.” - James Basham, CIDDL director

Adjunct and entry-level instructors in Lawrence are especially exposed because their workloads concentrate on high-volume, predictable tasks - course prep, repeated assignment grading, and quick feedback loops - that AI tackles first; a University of Georgia study found LLMs grade far faster but rely on “shortcuts” and jump from roughly 33.5% accuracy without guidance to just over 50% when given human rubrics, so unchecked automation risks mis-scoring student work and creating extra disputes or appeals (University of Georgia study on LLM grading accuracy).

Practical adaptation means three concrete moves: (1) protect professional judgment by adopting human‑in‑the‑loop workflows and transparency about AI's role (Ohio State University synthesis on auto‑grading ethics and human oversight); (2) redesign assessments toward in‑class, performance, or process‑based tasks that AI finds hard to fake - an approach many instructors have started using to preserve authentic learning (Chronicle of Higher Education coverage of classroom responses to AI); and (3) build small, high‑impact artifacts adjuncts can use immediately: shared rubrics that train any AI assistant, quick prompt libraries for consistent feedback, and a documented revision policy so students who use tools must still demonstrate understanding.

So what: by insisting on rubriced, auditable grading and switching at least one major assessment to an in‑person or portfolio format this semester, adjuncts can turn AI from an existential threat into a time‑saving aid without surrendering evaluative authority.

“We still have a long way to go when it comes to using AI, and we still need to figure out which direction to go in.” - Xiaoming Zhai

Teaching assistants and grading aides in Lawrence are among the most exposed staff because their day-to-day work - bulk multiple‑choice scoring, unit test runs for code, and first‑draft essay feedback - maps directly to what auto‑graders and NLP tools handle best; practitioners should read the OSU synthesis on AI and auto‑grading to understand capability boundaries (OSU synthesis on AI and auto-grading) and MIT Sloan's cautionary review on bias and human oversight (MIT Sloan analysis of AI‑assisted grading).

Practical re‑skilling priorities for Lawrence TAs: master rubric design and prompt‑based QA so AI outputs align with learning goals; learn to configure automated tools (Gradescope-style test suites and LMS integrations) to handle objective tasks; and adopt human‑in‑the‑loop audits and anonymization to catch bias and edge‑case errors.

The payoff is concrete: AI workflows can accelerate initial marking - learners and instructors report up to an 80% speed gain on routine passes - freeing dozens of hours per semester for small‑group tutoring and equity work, but only if audit samples and transparency are non‑negotiable (LearnWise guide to AI‑powered feedback).

“It (AI) has the potential to improve speed, consistency, and detail in feedback for educators grading students' assignments.” - Rohim Mohammed, University College Birmingham

Curriculum and content developers who manage standardized courses in Lawrence must reckon with generative systems that can produce aligned modules, practice sets, and near‑instant formative feedback, but also introduce risks around bias, student privacy, and academic integrity; practical redesign tactics start with treating AI as a drafting engine - use it to generate differentiated lesson frameworks quickly, then apply human review, bias checks, and provenance documentation before release (analysis of generative AI for adaptive content and ethical considerations).

Next, redesign assessments so that high‑stakes judgments rely on authentic, process‑based artifacts and in‑class demonstrations rather than solely on take‑home items AI can replicate; publish clear course‑level AI policies and require transparency in student tool use to protect integrity (classroom strategies and teaching guides for promoting responsible AI use).

Operational tactics include prompt‑engineering templates tied to district or state standards, rubric engineering with routine human‑in‑the‑loop sampling, and a documented QA step for FERPA/privacy review; these steps let a curriculum team convert an AI draft into a classroom‑ready, equity‑checked module in minutes instead of hours, preserving educator oversight while capturing time savings reported in teacher planning guides (teacher guide to AI‑powered lesson planning and quality control).

Academic administrative staff in Lawrence face fast-moving exposure as AI agents and embedded productivity tools automate predictable workflows - admissions triage, scheduling, routine helpdesk tickets, document processing, and compliance reporting are all prime targets - so small campuses must treat automation as a risk‑management project, not a one‑time efficiency hack.

EDUCAUSE's roadmap for leveraging AI at smaller institutions recommends a coordinator role, clear data governance, and pilot-first deployments to keep human oversight in place while capturing efficiency gains; it also flags licensing pressure (estimates of roughly $140–$300 per user per year) that can force narrow, request‑based access instead of blanket rollouts, a concrete budgeting constraint for Kansas offices (EDUCAUSE roadmap for leveraging AI at smaller institutions).

Practical safeguards include documented data flows and FERPA checks, human‑in‑the‑loop approvals for escalations, and using AI agents only to auto‑triage then route complex cases to staff - an approach vendors like Supervity advertise for admissions, HR, and helpdesk automation while preserving staff time for high‑value advising and equity work (Supervity AI agents for university admissions and helpdesk automation).

So what: by funding a short coordinator role, piloting an agent for one high‑volume queue, and requiring audit logs, Lawrence admin teams can cut repetitive load without surrendering control.

“Partnering with Supervity has been a turning point in our automation and AI adoption journey. Their AI Agents quickly adapted to our workflows, handling everything from vendor inquiries and customer support to internal product-related queries with precision.”

Entry-level instructional designers and media editors in Lawrence will see routine production tasks sharply accelerated - AI can spin up course outlines, quizzes, and multimedia drafts in minutes, and in some workflows a 30‑minute training video can be drafted in as few as 10 minutes - but human expertise remains essential for accuracy, ethics, and pedagogy.

Use AI to automate boilerplate: generate first‑draft storyboards, automatic captions, rough edits and voiceovers so editors skip repetitive trimming and noise reduction, then apply human review to correct bias, check provenance, and tune learning objectives (AI in Instructional Design - eLearning Industry, Video editing and voiceover automation - University of Cincinnati guide).

At the same time, preserve the parts AI struggles with - accessibility, rubric alignment, and formative assessment design - and treat AI output as a first draft rather than a finished product, following EDUCAUSE's guidance to balance speed with human‑in‑the‑loop quality control (10 Ways AI Is Transforming Instructional Design - EDUCAUSE).

The practical payoff: faster turnaround on media and drafts while safeguarding learning outcomes through targeted human oversight.

A practical 12‑month roadmap for Lawrence institutions starts with governance and capacity-building: in months 0–3 convene an AI in Education task force (district + KU + community partners) to map risks and align with state guidance trends noted by the Education Commission of the States review of state AI task forces (ECS review of state AI task forces) and national actions such as the White House Advancing AI Education for American Youth order that sets 90–120 day milestones and a Presidential AI Challenge within 12 months (White House: Advancing AI Education for American Youth).

Months 3–6 run focused pilots (one high‑volume admin queue, one grading workflow, one curriculum module) with strict FERPA/data governance and human‑in‑the‑loop audits; months 6–9 scale professional development using targeted programs (short, applied cohorts - e.g., Nucamp's 15‑week AI Essentials for Work - to equip staff with prompt engineering and tool workflows) and align assessment redesigns; months 9–12 evaluate results, document audit trails, and adopt policies that lock in transparency, equity, and selective automation.

So what: a single, well‑scoped pilot plus staff reskilling can capture the speed gains reported for routine tasks while preserving educator judgment, turning risk into capacity and keeping Lawrence educators firmly in the driver's seat (Register for Nucamp AI Essentials for Work (15‑week program)).

“Leveraging AI's transformative power, we can drive human progress by revolutionizing education globally, democratizing access and preparing future generations for the challenges and opportunities of a rapidly evolving world.” - Narmeen Makhani, World Economic Forum / TeachAI