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

Topeka educators are already feeling the ripple effects of AI: KU's Center for Reimagining Education is coaching Kansas districts to use AI for personalized learning and to reclaim teachers' time, while a national scan by CRPE warns that Kansas has not issued statewide AI guidance, leaving districts to make tough calls locally - sometimes by blocking tools, sometimes by experimenting cautiously in places like Wichita and Andover.

This local-national mix means jobs in Topeka's schools will change fast, not disappear overnight, and practical upskilling matters; tools like automated grading can save teachers hours per week and new roles will reward AI literacy.

For Kansas education workers wanting job-ready skills, consider targeted training such as the AI Essentials for Work bootcamp to learn workplace AI tools and prompt-writing.

Expect a future where smart classroom tech augments instruction and the best-prepared educators shape how it's used in schools and communities.

“AI is not going to replace the human. It is going to supplement the human.”

This brief used the same, transparent approach Microsoft Research applied in its July 2025 analysis - anchoring a local Topeka lens on a national dataset of 200,000 anonymized Bing Copilot conversations - by mapping those chats to O*NET work activities (844 intermediate activities across 104 professions) and computing AI-applicability scores based on task completion rates, user thumbs‑up feedback, and scope of impact; occupations whose daily work centers on information gathering, writing, teaching, and advising ranked highest, which is why postsecondary teaching roles and historians surfaced on the at‑risk list.

The methodology favors activity-level evidence over headlines: it identifies which classroom tasks (for example, rubric‑aligned grading and explanatory feedback) overlap with LLM strengths and which hands‑on duties remain outside their scope, then applies those patterns to Topeka's education job titles and local practice.

For readers who want the source methods, see the Microsoft Research Copilot conversations study and a practical note on how automated grading can reclaim hours each week for teachers.

“Our data do not indicate that AI is performing all of the work activities of any one occupation.”

Business teachers at Topeka-area colleges are squarely in the path of practical automation: industry analysis cited in

“up to 70% of business operations” could be automated by generative AI

, and higher‑ed pilots show that AI excels at routine workflows - everything from RPA data pulls to chatbot answers and preliminary grading - so instructors who spend hours on repetitive assignment scoring will feel the pressure first; yet that same automation can be a win if programs teach students when to delegate and when to apply human judgment, moving class time from clerical work to strategy, ethics, and critical thinking.

Practical examples - from virtual TAs and 24/7 campus chatbots to AI agents that format lesson plans - prove institutions can free faculty time for high‑value coaching, but only if syllabi, assessment, and faculty development evolve together.

For Kansas postsecondary business educators, the clear play is to build AI fluency into coursework (prompt engineering, ML basics, ethics) and partner with IT to pilot safe tools so courses model real‑world AI use rather than ban it; see William & Mary's take on AI in business education and AWS's higher‑ed case studies for concrete institutional examples.

Economics faculty at Topeka's colleges will feel the same de‑skilling pressure Microsoft and labor researchers flag for knowledge workers: routine tasks like data summarization, formulaic problem sets, rubric scoring, and literature synthesis are prime candidates for AI assistance, which means instructors could lose time spent on clerical work but gain capacity for higher‑value activities - policy debate, model interpretation, and mentoring students on judgment calls that machines can't make.

AEI's “De‑Skilling the Knowledge Economy” frames this as an “up or out” moment for mid‑career knowledge workers and argues that pairing AI literacy with strong noncognitive skills will be crucial; the MIT synthesis on AI's economics also notes that a bounded set of white‑collar tasks is most exposed and that complementary uses of AI may yield modest productivity gains for institutions.

For practical pilots in Topeka classrooms, start with automated grading and rubric‑aligned feedback that “can save teachers hours per week” and design assignments that require students to critique or supervise AI outputs rather than simply reproduce them - so class time becomes applied judgment training, not just content delivery (see AEI's report, MIT's overview, and a Nucamp checklist on automated grading for local pilots).

“In the long run, we're all dead.”

Library science teachers at Topeka's colleges face a clear double-edged moment: the same generative AI tools that can

reduce backlogs of materials by creating brief records

, as the OCLC Research Library Partnership has explored, also threaten routine cataloging and batch metadata work that long formed a big part of course and job training for LIS students.

Metadata analysis - data cleaning, text normalization, transforming CSVs into MARC-ready fields, and designing repeatable ETL workflows - is now a core competency (see the practical breakdown of core knowledge areas and tools like MarcEdit, OpenRefine, and Python in this metadata analysis primer), and programs that keep teaching only traditional card‑catalog skills risk graduating people into shrinking task lists.

The smarter play for Kansas programs is to lean into embedded roles - librarians as partners in course design and information‑literacy instruction - so faculty teach students how to supervise, validate, and document AI‑assisted metadata pipelines rather than just perform them; the result is a classroom that turns messy exports into consistent, reusable records and a career that stays centered on judgment, ethics, and systems thinking (Code4Lib metadata guide: practical metadata workflows for librarians, OCLC Research Library Partnership report on AI for metadata workflows, and models of integrated librarian workflows to enhance course design and development).

Historians working in educational contexts - whether teaching undergraduates, leading public programs, or consulting on local heritage projects - do the slow, indispensable work of preserving and interpreting the past, from digging through letters and diaries to turning archival fragments into classroom case studies that sharpen students' judgment rather than just feeding them tidy stories; resources like the Career Fit guide on what historians do highlight this mix of research, writing, teaching, and public engagement.

At the same time, the profession's future in places like Topeka depends on stronger archival literacy: scholars and archivists argue university programs must teach hands‑on skills for requesting, evaluating, and contextualizing primary sources so students can “open the hood” on how narratives are constructed rather than passively accept them (see the ActiveHistory review of archival literacy and Shane Twaddell's piece on narrative in the classroom).

Practical steps for Kansas programs include embedding primary‑source assignments, partnering with campus archives for real research projects, and teaching students to document and validate digitized records - so a dusty box of letters becomes a detective story that trains critical thinking, not just a footnote in a lecture.

“History is the narrative one weaves; history instruction might have once been the sharing of an agreed upon narrative at the national level, but today is more akin to historical methods and skills within the context of illustrative examples and case studies. It's not our job as teachers to impart a grand narrative. It's our job to open the hood and have a look underneath the narratives.”

Farm and Home Management educators in Kansas face a practical crossroads: generative AI can scale routine advisories and farm troubleshooting, but the technology's strengths - and its pitfalls - are already well documented, from Farmer.Chat's successes in giving extension agents quick answers to the hard lesson that farming is hyperlocal

microclimates, soil and drainage can shift even within a mile

and so machine drafts need local vetting and human oversight (O'Reilly case study on Farmer.Chat and FarmStack).

Research warns that retrieval‑augmented outputs must pass human review to avoid hallucinations, and that trustworthy systems rely on opt‑in, auditable data sharing and feedback loops - design features Kansas programs can teach and test with community partners (IFPRI analysis on AI for agricultural extension advisories).

At the local level, the curriculum pivot is clear: train students to validate model outputs against local records, document data provenance, manage consent and privacy, and run low‑tech contingencies so communities aren't stranded by a failed algorithm.

Urban and equity concerns - risk of overreliance, cost barriers, and uneven benefits - mean classes should also cover access, workforce pathways, and how to deploy AI tools in ways that preserve farmer trust and local decision‑making (CBCF capstone on urban farming and AI solutions for food insecurity), turning extension training into a lab for safe, community‑centered automation rather than an exercise in simply replacing routine tasks.

Topeka educators can treat AI not as an existential threat but as a set of practical changes to manage: upskill through local offerings (Washburn University's new AI literacy certificate is designed to teach job‑market AI basics and ethics and is open this fall) and hands‑on bootcamps like Nucamp's AI Essentials for Work that teach promptcraft and workplace AI skills; advocate within districts for clear, student‑centered policies and pilots (Project Topeka and Topeka Public Schools' early classroom work show how tools can be positioned as partners, grading assistants, or substitutes); and reframe roles so routine tasks are automated and human time is redeployed to coaching, equity, and critical thinking - reclaiming hours per week for high‑value student interaction rather than paperwork.

Start small: a certificate course, a district pilot, and an institute‑level workshop can build credibility, protect academic integrity, and make AI literacy part of professional practice rather than a controversial add‑on - so a planning hour once eaten by grading becomes dedicated mentoring time.

Learn more about local options at Washburn and KU's AIDL institute, and explore practical upskilling through Nucamp's short, workforce‑focused course.

"It's an introduction to kind of learn the AI techniques that you might need in the job market and the basics of it,"