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

Chula Vista educators should care because AI is moving from novelty to infrastructure: Stanford HAI's 2025 AI Index documents steep adoption - 78% of organizations using AI in 2024, U.S. job postings requiring AI skills rising ~20% year-over-year, and two-thirds of countries offering K–12 CS - yet fewer than half of K–12 CS teachers feel equipped to teach AI, creating an urgent local skills gap (Stanford HAI 2025 AI Index report).

Inference costs have plunged and state-level action is surging (131 state AI laws by 2024), so California districts will face affordable but regulated AI choices in curriculum, grading, and admin tools.

The practical takeaway: prioritize short, applied staff training to protect instructional quality and student equity - training that, industry surveys show, can quickly free teacher time - see a concrete course outline in the AI Essentials for Work bootcamp syllabus.

Methodology: roles were chosen by mapping California's state-level priorities for generative AI - risk analysis, procurement guidance, workforce training, and pilot sandboxes - to concrete, local signals about how AI is already entering classrooms and admin work.

First, roles where repetitive paperwork, scalable content generation, routine grading, or data-driven decisions are core duties were flagged as higher risk; second, those flags were weighed against California's executive order priorities to prioritize jobs districts will need to address in procurement and training; and third, local use-case evidence (adaptive courseware and vendor partnerships highlighted for Chula Vista) confirmed which functions are likely to be automated first, so schools can target short training sprints and procurement controls where state guidance and pilots are expected to land.

The practical payoff: the list points administrators to the five positions where aligning district procurement, short upskilling, and pilot evaluation will most quickly preserve instructional quality and equity.

Read the California AI Executive Order and Chula Vista AI education use cases here: California Governor's Executive Order on Artificial Intelligence and Chula Vista AI in Education: Top Use Cases and Prompts.

“This is a potentially transformative technology – comparable to the advent of the internet – and we're only scratching the surface of understanding what GenAI is capable of.”

High school English teachers in Chula Vista should watch the San Diego Unified example closely: teachers like Jen Roberts at Point Loma High use Writable (an HMH product powered by OpenAI) to give much faster, iterative feedback - turnaround for a 180‑student load that once took 2–3 weeks can fall to 1–2 days - letting teachers assign more drafts and focus in-person help on struggling writers, but not without tradeoffs; districts reported that Writable is “very accurate” for average students while sometimes under‑scoring top performers or over‑scoring others, so teachers still spot‑check grades and district leaders are scrambling to write policies after contracts surfaced without clear oversight.

Practical takeaway: prioritize vetted pilots, clear board-level procurement rules, and short PD so teachers can use AI for low‑stakes formative feedback while retaining human review for summative grades (see reporting from CalMatters reporting on AI grading in California and the San Diego Unified coverage at Voice of San Diego coverage of AI use at San Diego Unified).

“My students are going to live in the future. And the future is going to be very dominated by AI‑assisted everything. I don't think we're equipping students for that future if we ban it, block it prohibit it, never talk about it and tell them it doesn't exist.” - Jen Roberts, English teacher, Point Loma High (San Diego Unified)

Curriculum writers and instructional designers in Chula Vista face a fast-moving threat: major publishers are quietly bundling generative features into existing curriculum contracts, shifting lesson‑planning and differentiation work from humans to vendor platforms unless districts insist otherwise.

CalMatters documented San Diego Unified getting Writable via a broader Houghton Mifflin contract that was approved “among more than 70 other items,” with two board members later saying they didn't know an AI tool was included - an administrative blind spot that can erase designers' bargaining leverage and classroom input.

The practical risk: districts can adopt adaptive lesson generators and supplemental curricula by default, shrinking roles that create standards‑aligned units and culturally responsive materials unless contracts require disclosure, human review, and ongoing evaluation; procurement teams already use AI to draft and assess RFPs, so designers should push for plain‑English vendor explanations and pilot windows when districts solicit products (CalMatters reporting on bundled HMH contracts: CalMatters: Botched AI education deals lessons, EdWeek Market Brief on AI and district RFPs: EdWeek Market Brief: How AI is shaping district RFPs).

“It's close to impossible for districts and schools to keep up.” - Alix Gallagher, Policy Analysis for California Education (Stanford)

School administrative assistants - who handle attendance calls, enrollment paperwork, parent inquiries, and routine scheduling - are especially exposed as districts pilot chatbots and automated portals: LAUSD's high‑profile “Ed” rollout shows how quickly that exposure can turn risky when a vendor falters and oversight lags.

The district paid roughly $3 million toward a $6 million AllHere contract before deactivating Ed's chatbot on June 14 after the vendor furloughed staff, prompting parents, unions, and board members to demand transparency and an investigation into possible data exposure and procurement shortcuts; unions have also argued that future AI tools belong in collective bargaining conversations (detailed coverage at EdSource and the Los Angeles Times).

The practical consequence for Chula Vista: front‑office tasks can be automated overnight, but liability for data, service continuity, and community trust stays with the district - so administrative roles will either shift toward vendor oversight, privacy governance, and “human‑in‑the‑loop” monitoring or face elimination unless procurement, training, and clear board policies protect staff and student data.

"Ed's chatbot will return to families when the “human-in-the-loop aspect is re-established.”" - LAUSD spokesperson

District data analysts and predictive modelers in Chula Vista face outsized risk as vendors and open‑source tools make automated dropout‑prediction systems easy to deploy but hard to explain; AI models used for high‑stakes student interventions bring complexity, limited transparency, and data‑drift risks that can misdirect scarce counseling time and budget unless governed.

Recent governance work stresses concrete controls: inventory every model, tier risk, require vendor documentation, validate on historical cases, and monitor performance in production so alerts don't become false positives or biased referrals - practices detailed in Crowe's

Model Risk Meets AI

guidance and the

JGA AI governance playbook

.

The practical takeaway: mandate short pilot windows with documented validation and a human‑in‑the‑loop sign‑off before using predictions to change services, and map procurement language to recognized standards so districts can hold vendors to explainability and testing requirements (see Crowe's webinar and JGA's governance checklist linked below).

Tutoring and supplemental-instruction providers in Chula Vista face rapid disruption as generative tutors move from pilots to scalable products: randomized trials show measurable gains (Tutor CoPilot raised mastery by ~4 percentage points overall and 9 points for students of lower-rated tutors, at an estimated ~$20 per tutor), while a Turkish field experiment reported math gains of 48–127% during AI tutoring but a 17% drop after access was removed - a stark “crutch” effect that signals risk if human oversight vanishes.

Evidence also includes physics and Ghana pilots with large learning improvements, suggesting AI can extend one‑to‑one tutoring at low marginal cost but creates engagement, equity, and dependency concerns.

The practical takeaway for Chula Vista: pilot generative tutors with clear human‑in‑the‑loop rules, monitor engagement and long‑term retention, and retrain tutors to coach, validate, and personalize AI outputs rather than compete with them (see detailed reviews at Analysis of AI tutors from Education Next, implementation guidance at AI tutoring guidance from Chartered College of Teaching, and local adaptive-courseware use cases for Chula Vista at adaptive courseware for differentiated instruction in Chula Vista).

“Rule for AI tutor: Avoid providing the answer; guide students in their learning.”

Practical next steps for Chula Vista schools: form a district AI task force that mirrors CSBA's multidisciplinary approach, require any vendor contract to disclose generative features up front (the Houghton Mifflin bundling in San Diego shows why), and mandate short pilots with human‑in‑the‑loop validation before scaling tools for grading, tutoring, or predictive analytics; pair those pilots with applied staff training so front‑line educators shift into oversight, pedagogy, and vendor-evaluation roles rather than competing with automation.

Inventory every model or tool in use, tier risk, and publish board‑level procurement rules that insist on explainability and data protections; involve unions and parent stakeholders early - as LAUSD's “Ed” chatbot controversy made clear, service continuity and trust are non‑negotiable.

For rapid upskilling, prioritize short, job‑focused courses that teach tool use and prompt design (see a practical syllabus in the AI Essentials for Work bootcamp syllabus (Nucamp)), and use CSBA resources and the superintendent survey to justify funding and a formal timeline (CSBA AI Taskforce and superintendent survey) while using CalMatters reporting on bundled contracts to push for procurement transparency (CalMatters reporting on botched AI education deals).

The payoff: protect student outcomes and data, preserve educator jobs by converting roles into higher‑value oversight and coaching, and keep local control over how AI shapes classrooms.

“EAB's report confirms that teacher shortages, behavioral disruptions, worsening student mental health, and other familiar challenges are so pervasive that exploring how new technologies such as AI can help doesn't even make the ‘to‑do' list.” - Ben Court, senior director of K‑12 Research