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

Joliet Public Schools leaders are already talking about how AI will reshape classrooms and budgets - District 86 officials emphasized planning and adaptation in a recent Shaw Local transcript of the Joliet District 86 AI discussion - and statewide research from the University of Illinois warns that automation will shift who's at risk across Illinois' workforce (University of Illinois report: AI and the Future of Work in Illinois).

That combination - local leaders preparing for change plus a statewide risk assessment - means Joliet educators and support staff should prioritize practical AI skills now; one concrete option is a focused 15‑week program that teaches workplace AI tools, prompt writing, and role-specific applications (see the AI Essentials for Work syllabus and course details), a short, career-oriented path that can move a staff member from vulnerability to applied competence in a single semester.

The top‑five list for Joliet was built by cross‑checking statewide vulnerability maps and policy analysis with region‑level robot exposure and task‑level risk: first, Illinois' Labor Education Program report on “AI and the Future of Work in Illinois” identified occupations and county priorities for reskilling, which narrowed candidates to education roles common in Joliet schools (University of Illinois LEP report on AI and the Future of Work in Illinois); second, metro‑area robot intensity and measured displacement effects from Century Foundation research signaled which routine, repeatable tasks are most likely to be automated in the Midwest (the ENC region shows the highest robot intensity and, in manufacturing, each extra robot per 1,000 workers corresponded to about a 3.5 percentage‑point drop in employment for young, less‑educated workers) (Century Foundation report on robots and worker wages); third, national policy reviews and automation forecasts - used to set timelines and urgency - helped prioritize short‑term risks (for example, large McKinsey estimates of job displacement framed a 2030 planning horizon) (Chicago Policy Review analysis of automation, job loss, and education policy).

The method therefore blends local occupational profiles, Illinois‑specific risk assessment, and empirical robot‑exposure metrics to flag roles where immediate reskilling or process redesign in Joliet schools will have the biggest payoff.

Adjunct and entry‑level instructors in Joliet are uniquely exposed because routine, high‑volume tasks - auto‑grading, basic Q&A, and templated lesson generation - are exactly what generative systems can perform, which both frees time and concentrates risk in large, gateway sections where fewer “master instructors” could oversee AI tutors; research shows adjuncts benefit from AI for lesson planning and personalized feedback while also being the cohort most likely to see task reshuffling in the next decade (research on generative AI aiding adjunct professors, Pyrrhic Press) and that faculty in high‑enrollment survey courses face uneven displacement pressure (study on AI impact on college jobs, ETC Journal).

Policy choices matter: bans or blunt restrictions can simply shift grading and feedback back onto already stretched instructors, increasing workload rather than protecting jobs (see analysis of AI in community colleges and instructor safeguards).

So what to do: prioritize short, practical upskilling - prompt literacy, validated AI‑assisted grading rubrics, and bias checks - which pilots have shown can raise instructor productivity by noticeable margins and preserve the human strengths of mentoring and high‑stakes assessment.

“Finding ways to use it as a tool are things we have discussed with our faculty.” - Packback survey respondent

K–12 test graders and standardized‑test administrators in Joliet face immediate pressure because automated scoring delivers real gains - studies report automated systems cut grading time by roughly 73–76% and can free about 5–7 hours per instructor each week - yet those same tools struggle with high‑variance responses, partial‑credit items, and edge cases common on state assessments (see research on the automated grading benefits and challenges study: Automated grading benefits and challenges for K–12 education).

Large language model work suggests gains in scoring open responses but also flags limits on difficult items and partial‑credit scoring that matter for Illinois‑aligned math and ELA items (NAEP R&D Hub study on LLMs and automated scoring).

Crucially, explainability studies show that transparent explanations alone don't automatically raise student trust - students react most to the grade outcome itself - so Joliet districts should pair automated scoring with clear appeal routes and human review for disputed or subjective items (Study on explanations, trust, and motivation in learning analytics).

The practical takeaway: deploy hybrid, human‑in‑the‑loop workflows, validate systems on partial‑credit items, and lock down FERPA‑compliant data practices so time saved by automation truly converts into targeted interventions for struggling students rather than unexamined score shifts.

Curriculum content producers and educational copy editors in Joliet face rapid task‑level disruption because generative systems can draft lesson text, quizzes, and worksheets at scale - tools that

"draft, summarize, and correct grammar"

while also commonly inventing citations or factual errors - so the highest‑risk work is routine copy and unverified sourcing rather than pedagogic judgment (Ohio State University guidance on AI in teaching and learning; IEEE article on generative AI in education).

The practical response for Illinois editors is concrete: master prompt design, build rapid source‑verification checklists, and require FERPA‑safe workflows when materials draw on student data - moves that convert vulnerability into advantage by shifting roles from rote copyediting to high‑value tasks like curating verified content, ensuring accessibility, and designing AI‑transparent assignments that faculty can adopt (see institutional strategies and integrity templates in the Nucamp AI Essentials for Work academic-integrity strategies guide).

One memorable, practical metric: if an editor adds a two‑step verification (source check + model confidence review) to every AI draft, many districts can keep fast content production while preventing a single fabricated citation from undermining a lesson or assessment.

Registrars, schedulers, and data‑entry clerks in Joliet are on the front line of automation risk because their work is rule‑based and high‑volume: enrollment processing, transcript assembly, timetable changes, attendance logs, payroll updates and routine email triage can all be handled by Robotic Process Automation (RPA) and bots that move data between systems with fewer errors and faster throughput.

See detailed RPA use cases in education at AIMultiple RPA in Education use cases and practical school implementations in Agile Automations RPA education implementation.

“so what”

The result: real time savings - one reported government bot cut email handling from 2.5 days to about four minutes on average - but also a clear concern: without FERPA‑safe controls and human oversight, those efficiency gains can create privacy exposures and shift staff into precarious exception‑handling roles rather than meaningful, student‑facing tasks.

Joliet districts should pilot human‑in‑the‑loop automations for high‑volume workflows, require audit trails and escalation rules, and pair each rollout with role‑aligned reskilling.

For guidance on addressing privacy and FERPA concerns for local AI rollout, see Privacy and FERPA considerations for AI in education implementation.

Front‑desk staff and basic advisors in Joliet - the people who answer eligibility questions, schedule appointments, and triage student concerns - face outsized exposure because AI chatbots already handle many of those repetitive touchpoints: 24/7 answers about scholarships, forms, and simple course questions reduce phone queues and shorten wait times (examples and use cases in higher education are documented in How AI chatbots are transforming student services in higher education: AI chatbots for student services).

Well‑designed bots also provide proactive nudges, deadline reminders, and registration help, and institutions that pair bots with human handoffs can scale support without losing care; practical guides show pilots should start small, integrate with SIS data, and preserve clear escalation paths to people (Guide to integrating AI chatbots in academic advising: academic advising chatbot guide).

So what: real campus pilots show measurable student behavior change - a targeted advising bot (Georgia State's Pounce) reached 90% of freshmen opt‑ins and yielded higher re‑enrollment and on‑time registration - meaning Joliet can convert routine front‑desk workloads into more proactive student outreach if districts pair AI with FERPA‑safe data practices and explicit human‑in‑the‑loop review.

“AI isn't just a trend; it's a new way of listening to learners at scale.” - Lauren Gomez, Vice President of Technology and Innovation, Boundless Learning

Practical next steps for Joliet districts: treat AI preparedness as a coordinated, short‑term plan that combines policy, pilots, and people: monitor the Illinois Task Force and LTC guidance as they formalize school rules and pilot expectations, then launch small human‑in‑the‑loop pilots for grading, front‑desk chatbots, and RPA-backed scheduling so time savings convert to targeted student supports rather than privacy risk (LTC recommends piloting and iterative rollout - see their anticipating guidance Anticipating Illinois Guidance on AI in Schools (LTC)).

Invest in role‑specific AI literacy (prompt writing, bias checks, FERPA‑safe workflows) and give exposed staff a career‑ready option: a semester‑length 15‑week course that teaches workplace AI tools and prompt craft (AI Essentials for Work syllabus (15‑week course), early bird $3,582) can move a staff member quickly from vulnerability to applied competence - pair that training with clear audit trails and escalation rules before scaling.

Finally, include teachers, registrars, and frontline advisors in policy design so pilots reflect classroom realities and protect student data (AI Essentials for Work syllabus (15‑week course)).

“For Illinois teachers, this legislation prioritizes the importance of digital literacy and ethical AI in the classroom. It is the first step in helping Illinois educators to navigate the complexities of AI technologies, so educators and students can be equipped with the knowledge to use these tools responsibly.” - Teach Plus Illinois