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

Philadelphia educators should pay close attention to AI risk because the district is already piloting a cautious, equity-first approach - PASS, launched with Penn in March 2025, will test tools, craft policies, and train staff rather than rush adoption (PASS district pilot with Penn on AI in education); local reporting shows leaders balancing promise (personalized learning, time savings) with hard questions about student data, bias, and classroom integrity as teachers who once left “brand-new, shiny computers” unused now learn to guide students through AI's limits (Chalkbeat Philadelphia report on AI and teachers).

With research noting knowledge-work exposure and estimates that about 8.4% of Pennsylvania workers face AI displacement, the practical move is skill-building - programs such as Nucamp's AI Essentials for Work (15 weeks, hands-on prompt and workplace AI training) help educators turn disruption into new tools for teaching and career resilience (Nucamp AI Essentials for Work bootcamp - registration).

"If this tool is free, you are the product." - Andrew Paul Speese

To home in on the five education roles most at risk in Pennsylvania, the methodology blended task‑level AI applicability with local educational exposure: starting with the occupations Microsoft researchers flagged as highly exposed (summarized in Fortune's coverage of the 40 most affected jobs), the team cross‑checked those signal jobs against the Federal Reserve's analysis of which college majors and instructional tasks are most susceptible to generative AI, and used the AACC DataPoints guidance that explains why analytical, research and writing tasks - not hands‑on caregiving or mechanical work - drive higher AI exposure.

Where measures diverged, the approach followed the EIG / academic playbook of comparing multiple exposure metrics and “crosswalking” occupational codes so national findings map plausibly to Pennsylvania's postsecondary and district workforce.

That meant prioritizing roles whose core duties are research, grading, writing, metadata or routine curriculum production - tasks that studies show LLMs handle well - while also noting which education jobs appear on low‑risk lists to avoid false alarms (Fortune summary of Microsoft researchers' generative AI occupational exposure, Federal Reserve analysis of educational exposure to generative AI, AACC DataPoints on jobs and AI exposure and skills vulnerability), giving Philadelphia educators a defensible, task‑focused shortlist to guide upskilling and pilot choices.

“Every job will be affected, and immediately. It is unquestionable. You're not going to lose your job to an AI, but you're going to lose your job to someone who uses AI.”

Business teachers at the postsecondary level sit squarely in AI's crosshairs because so much of the role - researching cases, drafting syllabi, building rubrics, generating exam questions and feedback - maps neatly onto what large language models do well; aggregated risk estimates put the occupation in the “moderate” automation band (about 45% calculated risk) according to the Will Robots Take My Job analysis for business teachers (Will Robots Take My Job - business teachers postsecondary automation risk), and Microsoft researchers flagged business teaching among roles with high AI applicability in Fortune's roundup of exposed jobs (Microsoft Research generative AI occupational impact - Fortune coverage).

The instructional shift is already evident in faculty practice research: many instructors experiment with AI but remain unsure how to integrate it safely, and institutions are wrestling with whether to prohibit, pilot, or scaffold student use (Ithaka S+R national instructor survey on generative AI and instructional practices).

Practically, that means Philadelphia business faculty should redesign assessments toward applied, performance-based tasks and lean into roles - mentor, facilitator, bridge-builder - that AI struggles to replicate; after all, tools that can draft a case study in minutes also change what counts as meaningful student work.

“Every job will be affected, and immediately. It is unquestionable. You're not going to lose your job to an AI, but you're going to lose your job to someone who uses AI.”

Economics instructors at the postsecondary level face a particular squeeze in Philadelphia because so many of their core activities - summarizing research, generating model explanations, drafting problem sets and grading routine work - match the kinds of outputs large language models produce well, so the “so what?” is immediate: assessments and pathways that once signaled real-world analytical skill can be replicated by AI in minutes.

Local context matters: the School District's cautious PASS pilot and teacher training show a city trying to teach responsible use rather than ban tools outright (Chalkbeat Philadelphia coverage of district AI strategy and teacher training), while national signals - teen ChatGPT use doubled from 13% to 26% and analysts warn that entry‑level tasks are disappearing - underscore the risk that traditional college-level assignments will stop serving as career on‑ramps (Bellwether Institute newsletter on student AI use and labor market signals).

Add a macroeconomic warning from the Philadelphia Fed that generative AI could alter labor's share of income, and the local imperative is clear: redesign assessments toward defended, project‑based, applied or in‑person demonstrations of economic reasoning and partner with nearby research hubs to pilot resilient curricula (Philadelphia Fed analysis of generative AI and labor's share of income).

“If this tool is free, you are the product.” - Andrew Paul Speese

Library science instructors at the postsecondary level are particularly exposed because core duties - teaching cataloging, supervising metadata creation, and assessing discovery-ready collections - are precisely where descriptive AI is moving fastest, promising speed but risking bias, lost nuance, and “erasure” when names, diacritics or non‑English titles are romanized or dropped; the CRL study on metadata quality shows how missing language tags, absent values, and Western‑centric standards literally reshape who is visible in the record (CRL study: Identifying Metadata Quality Issues Across Cultures).

Practitioners at the AI metadata forum urged using AI for scale while keeping librarians in the loop to prevent automation from deskilling staff or amplifying errors, noting also ethical, IP and environmental tradeoffs that need local policy and human review (AI metadata forum insights on revolutionizing metadata management).

International guidance recommends a strategic, literate response - pilot projects, multilingual tagging, provenance documentation, and staff upskilling - so Philadelphia's library educators can teach not just cataloging rules but how to govern AI‑generated description that preserves cultural identity (IFLA guidance: Developing a library strategic response to AI).

Instructional designers and curriculum writers in Pennsylvania should treat AI as both a turbocharger and a warning light: research shows tools can handle the heavy, template-driven work - brainstorming learning objectives, drafting storyboards, generating quiz banks, even sketching a 30‑minute training video in minutes - freeing designers to focus on pedagogy and equity (University of Cincinnati: How Instructional Designers Use AI; University of San Diego: AI in Instructional Design use cases).

But that speed brings risks - hallucinations, bias, privacy and quality gaps - so local teams should pair promptcraft and literacy upskilling with governance: pilot tools, work with IT/compliance, and keep human review baked into rubrics and adaptive flows, as i4cp recommends for L&D leaders (i4cp: How Generative AI Will Change Instructional Designers' Roles).

In Philadelphia classrooms a practical next step is pragmatic: use AI to produce rapid formative items (a local rapid formative assessment generator can create OCR‑compliant MCQs aligned to standards) but require an educator to validate cultural relevance and accessibility before anything goes live - because tools can scale reach, but humans must safeguard learning quality.

Assessment technicians and grading staff in Philadelphia are squarely in the path of scalable automated scoring: industry tools have already scored “hundreds of millions of responses,” offering speed, consistency and near‑real‑time feedback that districts prize for faster instruction cycles (Pearson automated scoring solutions for K–12).

That same research shows best practice: a hybrid model (Continuous Flow) routes tricky answers to humans while machines handle routine scoring, so the practical shift for Philadelphia is clear - technicians will move from bulk scoring to quality assurance, discrepancy review, and managing machine‑human workflows.

Districts can pilot rapid formative tools to offload MCQs and short responses (a rapid formative assessment generator can produce OCR‑compliant items aligned to standards), but must pair automation with human validation, bias checks, and clear governance so speed doesn't come at the cost of accuracy or equity (rapid formative assessment generator for Philadelphia education).

“The process gives students immediate, detailed feedback - and it allows teachers to do more teaching.”

Philadelphia's path forward is practical and local: lean into the PASS tiered pilot and Penn GSE's scaffolded approach to build governance, require human review on any AI-generated work, and redesign assessments toward defended, project-based demonstrations and in-person tasks that reveal real student thinking - steps local reporting and the district's pilot emphasize as essential (Penn GSE PASS program details; Chalkbeat report on PASS in Philadelphia).

Upskilling is equally concrete: short, applied training that teaches promptcraft and workplace AI workflows turns automation from a threat into a classroom assistant - Nucamp's 15‑week AI Essentials for Work is one such option to learn how to vet outputs, write safe prompts, and integrate tools without sacrificing equity (Nucamp AI Essentials for Work - registration).

Start with small pilots, lock down student data and consent, move routine scoring into hybrid workflows with human QA, and remember the image that keeps this policy grounded: those “brand‑new, shiny computers” are only useful when teachers are trained to put them to work for learning.

“If this tool is free, you are the product.” - Andrew Paul Speese