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

Orem's education workforce is standing at a fork: rapid AI advances make routine tasks cheaper and faster, and local classrooms will feel that pressure first.

Stanford HAI's 2025 AI Index documents big technical gains and a collapse in inference costs - making powerful tools far more accessible - and notes AI's steady move into education; PwC's 2025 Jobs Barometer shows the same trend nationally, where AI both automates tasks and augments skilled workers, shifting required skills and boosting pay for those with AI fluency.

In Orem that already looks tangible - pilot programs report automated grading that speeds feedback and cuts faculty grading time by roughly 70% - so roles like proofreaders, instructional content editors, student-services clerks, interpreters, and entry-level analysts face disruption unless they reskill.

Schools and staff who learn practical prompt-writing and workplace AI workflows can pivot from risk to advantage; training such as Nucamp's AI Essentials for Work bootcamp teaches those exact skills and practical use cases to keep local educators competitive in the near-term transition.

“AI is going to change my job, but I will still bring value.” - Eliza Gil

To pick the five Orem jobs most exposed to AI, the team triangulated three practical inputs: a national scan of state K–12 AI guidance to see where policy is moving and what uses are allowed or prohibited (see the State AI Guidance for K12 Schools resource), close-reading of Utah's own P–12 framework and policy updates to capture local rules and scale (Utah's AI Education Policy Landscape), and reporting on how districts and HR teams are already applying AI to routine tasks like job postings, screening prompts, and recruitment workflows (Education Week).

That mix - policy + local context + on-the-ground HR and pilot evidence - guided simple, defensible criteria: how routine a task is, how often it touches student data or assessments, and whether state policy or integrity risks limit automation.

The result is a list rooted in Utah's regulatory reality and in measurable pilots (for example, automated grading that speeds feedback and can cut faculty grading time by roughly 70%), not speculation - so the recommendations that follow focus on concrete reskilling and prompt-and-workflow practices that local educators can adopt immediately.

“If you're not good at prompting AI, that leads to bad data going in, and bad data coming out. You actually have to practice how you ask [AI] the questions.” - Fisher

Proofreaders, copy editors, and instructional content editors in Orem should expect the routine, high-volume chunks of their work - grammar checks, citation formatting, bulk proofreading and light copyedits - to be the first to feel pressure from tools such as Grammarly and large language models, which already power faster turnarounds in education pilots (see automated grading that speeds feedback and cuts faculty grading time by roughly 70%).

Yet the same research shows why humans keep the upper hand: AI routinely hallucinates, mangles document-level consistency, and struggles to preserve authorial voice and nuanced judgement, so editors who pivot toward developmental editing, coaching, editorial strategy, and tight AI-governance workflows will remain indispensable.

Practical adaptation looks like mastering prompt design and safe tool hygiene (never upload sensitive student or client material without permission), offering “AI-plus” services that combine fast, machine-assisted passes with human-led quality assurance, and marketing the bespoke judgment editors provide - tone, ethics, context, and trust - that machines cannot replicate.

Picture a stack of essays that once took a week to line-edit being preprocessed by AI in an afternoon, then refined by a human editor who preserves voice and flags integrity issues - this hybrid workflow is the most defensible path for Orem professionals who want to stay both relevant and in control of their craft; see the longer discussion on editing's limits and possibilities in The Potential Impact of AI on Editing and Proofreading and Hazel Bird's editorial manifesto on responsible AI use.

“AI tools are not yet ready to fully edit academic papers without extensive human oversight, prompt engineering, and management.”

Writers and authors in Orem - especially instructional designers who craft courses, syllabi, and multimedia learning - are seeing the routine parts of their job (first drafts, templates, quizzes, and format conversions) rapidly automated: large models can spin up lesson outlines and even a 30-minute training video in minutes, so roles that stay at the “draft-and-format” level are the most exposed.

Still, the research shows a clear playbook to adapt: treat AI as a drafting partner while owning accuracy, pedagogy, ethics, and continuous updates; invest in promptcraft, accessibility checks, and tool governance; and shift toward higher-value work like curriculum strategy, SME interviewing, assessment design, and legal/disclosure oversight (see Learning Guild analysis of instructional design and AI and i4cp recommendations on generative AI for instructional designers).

Locally, Orem pilots that use automated grading and other helpers illustrate the speed gains but also the need for human supervision, so portfolio-focused writers who can combine AI fluency with curriculum judgement and compliance will be the ones educators hire next - imagine AI producing a tidy draft in minutes and a human author turning it into an accountable, standards-aligned course by the afternoon.

See also Orem pilot studies on automated grading and AI in education.

“From smart software that can grade essays to predictive analytics that can identify at-risk students, AI is changing the landscape of K12 education.”

Customer service reps, student-services clerks, and administrative support in Orem are squarely in AI's sights because the tasks that fill their days - routine inquiries, scheduling, application processing, and first-line troubleshooting - are precisely what chatbots and virtual assistants do best: 24/7 availability, fast scalability, and consistent answers that trim hours from the workweek (see how AI chatbots deliver round-the-clock service in APU's overview of AI in customer service).

Higher-ed–focused systems are already stretching this further, using AI to recommend courses, map academic paths, and triage which students need human advising versus an automated response (Answernet's piece on AI in student services shows how advising and enrollment can be partially automated while flagging at-risk students).

In Orem that means the steady stream of routine phone and portal questions can be handled by machines, but the “so what?” is the chance to reframe these roles: staff who learn AI governance, privacy-safe handoffs, empathetic escalation, and data-interpretation skills can move into casework, retention outreach, and system oversight - work AI can't do well.

Local pilots that speed grading and admin tasks underscore the hybrid future: imagine a midnight portal answer routed instantly by a bot, and the next morning a human specialist following up on the few complex cases that require judgment and trust; that's where Orem institutions should aim their reskilling investments.

APU overview of AI in customer service and digital retail • Answernet analysis: AI in higher education student services and enrollment • Orem pilot studies on automated grading and operational efficiency in education

Interpreters and translators in Orem and across Utah face a clear bifurcation: routine, predictable tasks - menus, simple administrative translations, and low-stakes remote interpreting - are already ripe for automation, but the high-stakes, culturally nuanced work that schools, courts, and healthcare settings demand still needs human judgment; the Middlebury Institute's rundown of “AI and the Future of Translation and Interpretation” makes this plain, noting both rapid productivity gains and machine interpreting accuracy in the 90–95% range for some large events, while the American Translators Association cautions that AI remains “unfit for important meetings” without human oversight.

The practical playbook for local professionals is equally concrete: learn computer-assisted interpreting (CAI) tools and terminology management (see training approaches that embed InterpretBank), partner with developers, and build client-education routines so organizations in Utah don't mistake a plausible-sounding machine draft for a verified translation.

The vivid risk: a high-profile error - already flagged in WHO testing and advocacy reporting - can turn a routine translation into a reputational crisis, so interpreters who combine linguistic depth, ethical safeguards, and AI-fluency will be the ones local institutions keep and pay more for (not the other way around).

Middlebury Institute analysis of AI and the future of translation and interpretation • American Translators Association guidance on AI and interpreter use • Training models teaching computer-assisted interpreting tools like InterpretBank.

“Embrace ambiguity.” - Winnie Heh

Entry-level data and research assistants and junior analysts in Orem are squarely exposed because their day-to-day - data entry, cleaning spreadsheets, routine visualizations and first-pass research - matches the exact tasks AI automates fastest, and global reporting warns this is reshaping the entry-level ladder (the World Economic Forum finds many employers expect to reduce roles where AI can substitute).

Local pilots show the speed gains are real: automated pipelines and grading tools already accelerate workflows in Orem education programs, so the risk is that novice analysts become “scaffolded out” before gaining experience.

The practical defense is straightforward and immediate: shift from manual chores to oversight, interpretation, and tooling - learn SQL/Python and dashboarding, become fluent at curating AI outputs, and build AI-powered portfolios or apprenticeship projects that prove judgment beyond a model's summary.

Employers should redesign on-ramps into apprenticeship-style AI-assisted roles, while early-career hires who can translate model outputs into rigorous questions, error checks, and actionable narratives will stay in demand.

Picture a stack of CSVs that once took a week to clean being parsed in minutes by an AI agent - what separates the hireable candidate is the human who spots the subtle bias or missing context the machine missed; see VKTR roundup of jobs most at risk from AI and Nucamp AI Essentials for Work syllabus and Orem pilot information for local context.

“AI is reshaping entry-level roles by automating routine, manual tasks. Instead of drafting emails, cleaning basic data, or coordinating meeting schedules, early-career professionals have begun curating AI-enabled outputs and applying judgment.”

Orem schools and education offices should treat the next 18 months as a planning sprint: use the rigorous, data-driven framing in the 2025 AI Index to justify investment in workforce readiness, follow the U.S. Department of Education's new guidance on responsible AI adoption to align pilots with federal priorities, and move quickly to pilot hybrid workflows that pair machine speed (local studies show automated grading can cut faculty grading time by roughly 70%) with human oversight - so routine bottlenecks shrink while judgment-heavy work is preserved.

Practically, that means auditing high-volume tasks, launching targeted reskilling (promptcraft, tool governance, privacy-safe handoffs), redesigning entry-level on-ramps into apprenticeship-style roles, and embedding a readiness assessment and risk checklist before scaling.

Local leaders can also lean on public resources like the EDUCAUSE Generative AI Readiness Assessment and living risk catalogues such as the MIT AI Risk Repository while offering staff concrete training pathways - for example, Nucamp's AI Essentials for Work syllabus - to turn disruption into an opportunity to raise service quality and keep trusted, high-stakes jobs local.

“Artificial intelligence has the potential to revolutionize education and support improved outcomes for learners.” - U.S. Secretary of Education Linda McMahon