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

Saint Paul schools are already standing at the crossroads of clear opportunity and real risk as generative AI tools - large language models that can draft lessons, summarize documents, and automate routine responses - move into classrooms and offices; Google's Generative AI beginner's guide explains the core tech and how models are tuned and grounded to reduce hallucinations, while local-focused resources like Nucamp's guide to using AI in St. Paul show practical education use cases and accessible visuals for diverse learners.

For district leaders and educators the immediate question is not if AI will arrive, but which roles (paraeducators, entry-level and substitute teachers, clerical staff, graders, and junior curriculum writers) will need new skills to adapt; targeted training such as the AI Essentials for Work bootcamp can teach prompt writing and workplace AI workflows so staff can turn automation from a threat into a productivity tool.

Read Google's Generative AI beginner's guide, Nucamp's St. Paul AI guide, or explore the AI Essentials for Work bootcamp for practical next steps. Google's Generative AI beginner's guide - Google AI Education, Nucamp's guide to using AI in St. Paul - Nucamp AI Essentials syllabus, or AI Essentials for Work bootcamp registration - Nucamp.

To identify the five Saint Paul school roles most vulnerable to automation, job descriptions and district practices were cross‑checked against official Saint Paul Public Schools guidance: board policies and procedures (to ensure alignment with Minnesota statutes and district governance), the Title I online guide (which distinguishes licensed teachers from non‑licensed support staff and sets paraprofessional requirements), the district's Technology Usage & Safety policy (Policy 520) for roles that rely heavily on routine tech tasks, and the Procedure Manual's “standard work” pages that list task-level duties - prioritizing positions whose day‑to‑day work maps neatly onto generative AI capabilities.

Roles were flagged when multiple sources converged - e.g., Title I staffing codes plus the Procedure Manual's checklists and Policy 520 acceptable‑use rules - so the selection reflects local compliance, funding rules, and operational realities in Minnesota schools rather than abstract projections; the result is a practical, district‑grounded shortlist informed by SPPS's strategic priorities in the SPPS Achieves plan.

The process was deliberate: each role's tasks were checked off against district procedures much like using a built checklist to close a loop on risk and retraining needs.

Paraeducators - those classroom support staff who run small groups, check daily work, and help implement lessons - are especially exposed as AI takes over routine feedback and basic assessment chores: platforms that auto-score and give instant comments can free teachers hours a week and scale adaptive practice, but they also risk displacing tasks paraeducators often perform in Minnesota schools unless roles shift (see national reporting on time saved and classroom uses).

Tools like Quill and similar literacy platforms are already automating grading and personalized practice in pilot programs, showing strong gains when paired with teacher oversight, while teachers nationwide report that AI can cut planning and grading time and let staff focus on higher‑value interactions.

At the same time, research warns that automated scoring can embed bias or favor formulaic responses, so the smart adaptation is not resisting technology but becoming its supervisor: upskill paraeducators in basic AI literacy, prompt‑checking, and ethical review; lead small‑group work that leverages AI‑generated practice but centers human judgment; and help translate AI feedback into culturally responsive, individualized coaching for learners.

The transition could feel like moving from the old Scantron's “rat…tat…tat” to an AI that returns comments in seconds - unless paraeducators claim the human parts of the job (relationship building, nuanced scaffolding, and fairness checks) as their core expertise and learn to run the new tools responsibly.

For concrete examples and classroom research, see coverage on AI grading ethics and literacy workshops from Education Week and InnovateUS, and national surveys on teacher time savings reported by Fortune.

“Human educators should always have the final say on evaluations of student work, even if AI is involved in the process.” - Education Week

Entry‑level and substitute teachers in Minnesota face clear exposure as tools like Gemini can rapidly draft lesson plans, generate quizzes and rubrics, and produce differentiated materials that used to take hours - routine prep and canned feedback are the easiest pieces to automate.

That doesn't mean these roles disappear; it means the job changes: substitutes who've relied on packet‑based plans may find those packets auto‑generated, and new teachers who build classroom routines in evenings could see that time reclaimed by AI. The practical response for Minnesota districts is twofold: shrink risk by owning the tools (set admin controls and age‑based access) and grow staff capability through short, focused professional learning so early‑career teachers supervise outputs, align AI drafts to state standards, and translate generic feedback into culturally responsive coaching.

School systems can adopt Gemini's teacher‑focused workflows - its lesson‑planning and differentiation features - and pair those with the no‑cost educator courses and classroom integrations that teach prompt best practices and safety checks.

The payoff is concrete: fewer weekend nights spent on basic prep and more intentional, human time for classroom relationships and formative coaching.

“With the Gemini app, my planning is so fast and easy. I can adapt my lesson plan to the needs of my students, and it can give me more ideas. I feel like I can give more attention to my students and projects using AI rather than spending my whole afternoon or weekends working on the planning.” - Natali Barretto, STEM teacher, Albuquerque Public Schools

School administrative assistants and clerical staff in Saint Paul schools face acute exposure because the exact tasks that fill their days - attendance tracking, scheduling, enrollment processing, report generation, routine parent communications - are the ones AI already automates, from chatbots that handle FAQs to systems that flag at‑risk students and auto‑produce reports; Element451's overview shows how attendance, scheduling, and automated communications cut manual work, while reporting like TomorrowDesk highlights research estimating as much as 46% of administrative tasks are now susceptible to automation and tools can shave off roughly five hours a week for staff.

That doesn't mean automatic layoffs are inevitable, but it does mean Minnesota districts should treat these tools as change management problems: pilot narrowly, require “human‑in‑the‑loop” approvals, invest in retraining so clerical staff become supervisors of workflows and data stewards, and bake in privacy and FERPA safeguards called out by education researchers.

Practical adaptations include learning to manage AI scheduling and transcription tools, interpreting predictive dashboards rather than just entering data, and redesigning roles toward family outreach, compliance oversight, and systems troubleshooting - work where human judgment and community knowledge still beat an algorithm.

For implementation playbooks and ethical checklists, see Element451's admin playbook, the coverage of school admin disruption at TomorrowDesk, and the University of Illinois summary of AI pros and cons in schools.

Assessment technicians and graders in Saint Paul schools are squarely in the crosshairs of automation because the very workflows they own - scoring quizzes, running rubrics, processing portfolio entries and producing reports - are exactly what modern tools do faster and at scale; elementary platforms like Seesaw AI-enhanced instruction and auto-grading for elementary schools promise auto‑graded, age‑appropriate questions and

8+ hours back per week

while more advanced systems such as Gradescope and Turnitin speed technical and essay grading and generate analytics that previously required hours of manual work.

The risk is real: routine scoring can be reassigned to software, shrinking the need for parcelled grading labor - but the path to opportunity is concrete and local: turn technicians into data stewards and rubric architects who spot‑check AI outputs, calibrate models, manage multimodal submissions (audio/video portfolios), and translate algorithmic flags into intervention plans that teachers actually trust.

Practical pilots, documented audit trails, and human‑in‑the‑loop review preserve fairness and allow same‑day or next‑day feedback (one tutor's workflow returned feedback in under 24 hours), so assessment staff who add calibration, privacy oversight, and interpretive coaching to their skillset move from vulnerable to indispensable.

For technology comparisons and implementation guidance, district teams can consult vendor reviews and implementation guides that map tools to assessment types and accuracy tradeoffs.

Entry‑level curriculum content developers in Minnesota schools are squarely exposed because generative AI tools can now draft course outlines, generate differentiated lessons and rubrics, and even map activities to standards - tasks that once defined a junior writer's day.

Google's Gemini for Education advertises quick lesson planning, personalized practice materials, and admin controls that let districts pilot access, while comparative research like the University of South Florida study shows AI platforms differ in speed and alignment but can sharply speed curriculum redesign; together these point to a near-term shift from raw content creation to content curation and validation.

The smart adaptation is concrete: learn to turn AI drafts into culturally responsive, standards‑aligned units by acting as an alignment specialist and rubric architect, run human‑in‑the‑loop checks for accuracy and bias, and build transparent classroom policies and scaffolded AI literacy into curriculum workflows (see practical examples and feature lists from Google Gemini and classroom pilots).

Think less “do the writing” and more “design the test and referee the output” - a shift that trades repetitive drafting for higher‑value work like differentiation, standards mapping, and educator training.

For hands‑on examples, see the Google Gemini for Education feature list and the University of South Florida comparison of AI tools for curriculum design: Google Gemini for Education features for lesson planning and personalization and University of South Florida research comparing AI tools for curriculum design.

“With the Gemini app, my planning is so fast and easy. I can adapt my lesson plan to the needs of my students, and it can give me more ideas. I feel like I can give more attention to my students and projects using AI rather than spending my whole afternoon or weekends working on the planning.” - Natali Barretto, STEM teacher, Albuquerque Public Schools

Moving from risk to opportunity in Saint Paul means pairing clear district guardrails with practical staff upskilling: follow Saint Paul Public Schools data privacy and third‑party rules (families' contact data is collected through Back to School Forms and vendor access is limited and monitored) and the Technology Usage & Safety guidance in Policy 520 to keep student data safe and access auditable, while piloting AI in narrow, monitored workflows; see the district's Data Privacy Practices and Technology Services pages for the exact permissions and opt‑out tools administrators should use.

At the same time, invest in short, job‑focused training so paraeducators, clerical staff, graders and early‑career teachers learn prompt craft, review outputs, and interpret AI reports - in other words, turn automation into a time‑saving assistant rather than a replacement (what used to be a Sunday night lesson scramble can become an afternoon coaching session).

Practical next steps: map which apps touch Infinite Campus, require approved vendor reviews, and enroll staff in applied courses such as Nucamp's AI Essentials for Work to build prompt and workflow skills that respect SPPS privacy and acceptable‑use rules.