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

Salt Lake City educators are on the front lines of a statewide push that's already making Utah a national model for K–12 AI: coordinated tool vetting, a living AI framework, and large-scale professional development are helping teachers use AI to personalize lessons for students with disabilities and English learners while districts wrestle with privacy and oversight.

Local reporting highlights that Utah's centralized approach and the Utah Education Network's vetted tools give districts an edge, even as national data shows roughly one in three college-aged young adults now use ChatGPT for schoolwork - a sign that AI literacy is rapidly becoming a baseline job skill.

That mix of fast adoption and proactive policy means some administrative and grading tasks could be automated sooner than expected, so practical, workplace-focused training like Nucamp's AI Essentials for Work bootcamp or its registration page for hands-on prompt and tool training (Register for the AI Essentials for Work bootcamp) can help Salt Lake City educators adapt and stay indispensable.

“If you don't look at AI right now, you're going to lose jobs.” - InstructureCast

To rank which Salt Lake City education jobs face the most near-term disruption from AI, the methodology combined three local realities: the technical capability of explainable, high‑accuracy models, the routine nature of tasks, and Utah's strong compliance expectations.

Roles that involve repetitive, pattern‑based work (grading, attendance, standardized test scoring) scored higher for automation potential, informed by examples of XAI systems like the University of Utah explainable AI RiskPath press release that achieve 85–99% prediction accuracy while explaining how factors change over time (University of Utah explainable AI RiskPath press release).

Privacy and data‑handling risk was weighted heavily too, guided by institutional rules such as the Utah State University AI use and compliance policy (FERPA/HIPAA data restrictions) that forbids uploading FERPA/HIPAA data without agreements (Utah State University AI use and compliance policy (FERPA/HIPAA data restrictions)).

Adaptation strategies prioritized tool vetting, bias‑aware automation, and hands‑on training tied to measurable time savings (for example, automated, bias‑aware assessments and lesson‑planning efficiencies highlighted in local case studies), so districts can adopt AI as an efficiency lever without sacrificing equity or legal compliance - a practical balance that treats AI like a precise instrument, not a mysterious black box.

“Chronic, progressive diseases account for over 90% of healthcare costs and mortality. By identifying high-risk individuals before symptoms appear or early in the disease course and pinpointing which risk factors matter most at different life stages, we can develop more targeted and effective preventive strategies. Preventative healthcare is perhaps the most important aspect of healthcare right now, rather than only treating issues after they materialize.” - Nina de Lacy, MBA, MD

Office and administrative support staff in Salt Lake City schools face some of the clearest near‑term risks from AI because so many of their day‑to‑day duties are repetitive and rule‑based: inbox triage, scheduling, attendance tracking, routine data entry and standard customer‑service interactions can be streamlined or handled by automation, which helps explain why one analysis finds roughly 46% of tasks in office and administrative roles are automatable (Analysis: 46% of office and administrative tasks are automatable).

Broader labor forecasts reinforce the threat and the transition story - Goldman Sachs estimates 6–7% of U.S. jobs could be displaced if AI adoption accelerates, even as new roles emerge as the economy adjusts (Goldman Sachs research on AI's impact on the workforce).

For district leaders that means the smart play is not denial but deliberate adaptation: vet tools that save hours on repetitive work, retrain staff for human‑forward tasks like relationship management and equity oversight, and treat automation as a way to free time for higher‑value school supports - imagine a front office where routine forms and schedules are auto‑filled so staff can focus on students, not spreadsheets.

“While these trends could broaden as adoption increases, we remain skeptical that AI will lead to large employment reductions over the next decade.” - Briggs and Dong, Goldman Sachs Research

K–12 testing and grading assistants are squarely in the “high risk” column for Salt Lake City schools because the core of their work - scoring, rubric-based feedback, item generation, and trend analysis - is exactly what current AI tools do fastest and most reliably; national polling shows many teachers already lean on these tools (a Gallup/Walton poll on teachers' AI use (Fortune) found weekly users estimate they save about six hours a week, and anecdotes include a five‑page lesson plan delivered in seconds), so districts that centrally vet and deploy vetted solutions can automate low‑level scoring while preserving teacher oversight (Gallup/Walton poll on teachers' AI use - Fortune).

Research and vendor pilots also show AI can analyze patterns across student work to spotlight misconceptions and free time for coaching, but that benefit will only reach all Salt Lake City classrooms if districts pair tools with bias‑aware, OCR‑compliant workflows - exactly the kind of approach described in the local guide to automated, bias‑aware assessments for Utah schools (Automated bias-aware assessment guide for Utah schools).

The “so what?” is simple: with reliable grading automation, a teacher can trade late‑night stacks of papers for 1:1 conferences that actually move a student's learning needle, but uneven adoption risks widening gaps unless training and equitable rollout are prioritized.

“Using AI has been a game changer for me… It's helping me with lesson planning, communicating with parents and increasing student engagement.” - Ana Sepúlveda

Curriculum content creators - teachers and district instructional designers who write lesson plans - sit at a medium‑high risk crossroads: generative AI can crank out standards‑aligned sequences, differentiated worksheets, and even multimedia hooks in minutes (Edutopia shows tools like MagicSchool producing full plans with an 80/20 workflow), which many Salt Lake City schools are already piloting to shave planning time, but rigorous study shows a catch - AI‑generated lessons overwhelmingly target lower‑order thinking and rarely promote analysis or creation (a 2025 EdWeek analysis found only about 2–4% of AI lessons asked students to evaluate, analyze, or create).

That means AI is powerful as a co‑pilot - speeding routine design and alignment and freeing writers to add culturally relevant context, higher‑order tasks, and local standards alignment - but dangerous if relied on as a turnkey replacement.

Practical adaptation: use vetted tools for first drafts, apply the 80/20 review habit (AI drafts the scaffold, educators add the nuance), and validate outputs for inclusivity and rigor before rollout.

For Utah districts balancing tight budgets and equity, the decision isn't binary; it's about harnessing time savings while retaining pedagogical control so lesson‑plan authors spend fewer evenings drafting and more class periods coaching deeper student thinking.

“The teacher has to formulate their own ideas, their own plans. Then they could turn to AI, and get some additional ideas, refine [them].” - EdWeek (researchers quoted in 2025 analysis)

Learn more about practical lesson‑planning workflows in the Edutopia lesson‑planning resources (Edutopia lesson-planning resources) and read the EdWeek 2025 analysis on AI in lesson design (EdWeek 2025 AI lesson analysis).

For reported teacher time‑savings with classroom AI tools, see Structural Learning's findings (Structural Learning report on AI time savings), and find local Salt Lake City examples of lesson‑planning time savings through district pilot programs (Salt Lake City School District AI lesson-planning pilots).

School librarians and media specialists in Salt Lake City fall into a clear medium‑risk category: AI can streamline the repetitive backbone of library work - cataloging, inventory, circulation FAQs and basic discovery - without erasing the profession's core value as trusted research teachers and privacy guardians.

Tools like Follett's Destiny AI are already reshaping resource management and predictive search, freeing time that can be redirected to curriculum collaboration and student research coaching (Follett Destiny AI data assistant for school libraries), but surveys show many library professionals feel unprepared and hungry for training: an ACRL survey found only modest AI literacy and strong calls for ethics, privacy, and hands‑on upskilling (ACRL study on AI literacy in academic libraries).

Local reporting also notes opportunities and tradeoffs in semantic search and patron privacy that districts must weigh as they pilot tools (Cronkite News reporting on AI, cataloging, and semantic search in libraries).

The practical “so what?”: when routine tasks are automated, a Salt Lake City librarian can swap hours of shelving and data entry for leading a five‑minute intervention that gets a struggling student back on track - if districts prioritize vetted systems, strong privacy safeguards, and role‑focused training so librarians lead on AI literacy rather than being sidelined by it.

“If people want to know what time the library is open, a chatbot can easily answer that, which would then free me up to answer the longer questions.” - Kira Smith, Cronkite News

Substitute teachers and entry‑level tutors in Salt Lake City sit at a medium risk point: AI can rapidly fill gaps - intelligently matching substitutes to classes, auto‑scheduling, and queuing adaptive practice - so a last‑minute sub might arrive to a classroom where standards‑aligned materials and targeted exercises are already waiting, but that doesn't erase the need for human judgment, behavior management, and relationship building.

Evidence from a systematic review of intelligent tutoring systems shows AI‑driven tutors can improve K–12 learning when paired with oversight (systematic review of intelligent tutoring systems improving K–12 learning), and commercial work on substitute‑teacher platforms highlights features like profile matching, automated scheduling, and adaptive resources that smooth staffing shortages (AI-powered substitute teacher app development features: profile matching, scheduling, and adaptive resources).

The practical balance for Utah districts is clear: treat these tools as force multipliers - vet apps for privacy and bias, embed automated, bias‑aware assessment workflows, and train substitutes to use AI as a co‑pilot so tech handles routine scaffolding while humans maintain the mentorship, classroom culture, and equity oversight that AI can't replace (automated bias-aware assessment workflows for K–12 education).

Practical next steps for Utah educators and districts start with the basics already working here: lean into Utah's living P‑12 AI framework and the Utah Education Network's AI Toolkit to vet vendors, insist on data‑privacy agreements, and make professional learning the operational priority so classrooms get safer, fairer automation - not surprise rollouts.

Districts should pilot bias‑aware grading and admin automation in low‑stakes settings, fund recurring PD (layered, short modules that build to classroom practice), and create human‑in‑the‑loop workflows so teachers keep instructional judgment while routine work is delegated - think of swapping a late‑night stack of papers for an hour of targeted student coaching each week.

Use statewide coordination to capture what works, scale it, and require vendor transparency; pair pilots with clear equity checks and regular audits. For practical, job‑ready training that teaches tool use, prompt design, and workplace AI skills, consider Nucamp's hands‑on AI Essentials for Work pathway and its easy registration page (Register for AI Essentials for Work) to speed staff readiness while districts refine policy.

“If you don't look at AI right now, you're going to lose jobs.” - InstructureCast