EXPOSED
The bulk of this job — building syllabi for cataloging and reference courses, recording lecture modules, grading discussion posts and research papers, drafting literature reviews for LIS journals — is text-and-screen work that current models handle at usable quality, and MLIS programs are already among the most online-delivered graduate degrees, which strips away the physical-classroom moat. What persists is thesis and practicum advising, accreditation-facing program design, and the mentorship that pushes students into actual library and archives careers. The bigger threat is demand-side: this is a 3,630-person occupation whose enrollment pipeline depends on library and information-work hiring, and AI pressure on search, cataloging, and reference roles feeds back into program size.
Most of this decline happened after 2021 — it is not the pandemic dip.
Median pay $71,410 → $80,340 -10.0% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+3%
Percentage only. The projection counts a different population from the 3,630 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +3% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~400 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorCollege ProfessorLibrary ProfessorLibrary InstructorAssistant ProfessorAssociate ProfessorCollege Faculty MemberClassification InstructorLibrary Science ProfessorUniversity Faculty MemberInformation Science ProfessorLibrary Technology InstructorFilm and Media Program InstructorMedical Record Librarians TeacherMedical Records Library Professor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Lecturing on MARC/RDA cataloging rules, building reference-interview exercises, and marking annotated bibliographies are all things a model drafts at passable quality, which is why this sits at 9 rather than mid-teens — the residue that genuinely resists is supervising a student's practicum placement in a live special-collections reading room and chairing capstone committees where you judge whether a project is defensible, not whether it is correct.
Some physical or field component A 5 reflects the thin physical layer that still exists: hands-on instruction in archival processing, preservation handling, and physical-collection organization, plus site visits to partner libraries for practicum oversight — but the ALA-accredited MLIS is overwhelmingly delivered online, so most of your teaching week is a screen and a Zoom window.
No licence, no signature requirement There is no license to teach library science — the MLIS itself is the credential your students seek, not one you must hold to be personally liable, and ALA program accreditation attaches to the institution's self-study and site visit rather than to you as an individual, so no statute names you when a course goes wrong.
Meaningful discretion A 10 rather than mid-teens: you exercise genuine discretion on curriculum mapping to ALA core competencies, admissions decisions, and whether a thesis is ready to defend, but the consequences land on a student's timeline and a program review cycle, not on anyone's safety, liberty, or money.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (9/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (3/20) is whether the law requires a licensed human to sign. Trust premium (13/20) is whether buyers specifically pay for a person. Judgment and accountability (10/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 26 of this occupation's 40 points (65%).
Embodiment (5/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 56/100, still EXPOSED.
If faculty of record are made individually accountable for academic-integrity determinations on AI-assisted student work (the pattern spreading through university honor-code revisions, where an AI-detection flag cannot itself constitute a finding and a named instructor must make the call), plus signing off on practicum placements and site supervisor evaluations, the role owns more consequential, contestable, appealable calls than it does when grading is the core.
This occupation does have two genuine tiers: the lecture/rubric/discussion-grading tier and the thesis-and-practicum supervision, accreditation self-study, and IRB-facing research-methods tier. If the routine tier is absorbed and headcount is cut rather than backfilled with more sections, the residual job is disproportionately the supervision-and-self-study tier, which current models cannot carry — writing an ALA self-study that a visiting committee accepts requires standing behind institutional claims.
ALA Committee on Accreditation's 2025 revision of the Standards for Accreditation of Master's Programs already ties program approval to a named, qualified full-time faculty core (Standard III) and to faculty responsibility for curriculum. If the COA adds explicit language that AI-generated or AI-delivered instruction does not count toward required faculty FTE, and that a named faculty member of record must attest to course design and assessment for each required course, the licensed-human-must-sign element becomes real rather than nominal — most state and municipal librarian civil-service lines require the ALA-accredited degree, so the accreditation signature has teeth.
Narrow but real: employers of archivists and special-collections staff (SAA's Academy of Certified Archivists, and the digital-preservation and rare-books tracks) hire on named-mentor reputation and cohort networks. If iSchools compete on identified faculty and placement pipelines rather than credit-hour price — the way top MLIS programs already market Simmons or UNC faculty by name — students pay for the human relationship, not the content.
The limit. Every lever here raises resistance per remaining job without touching the binding constraint, which is demand-side. A 3,630-person occupation sized by MLIS enrollment, which is sized by library and cataloging hiring, can shrink 30% while each surviving position scores higher. Accreditation moats also protect the program, not the individual line: ALA standards specify faculty adequacy in aggregate, so a department can meet them with fewer, more senior faculty.
| Chicago-Naperville-Elgin, IL-IN | 220 | $78,060 -3% |
| New York-Newark-Jersey City, NY-NJ | 170 | $101,920 +27% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 90 | $99,720 +24% |
| Seattle-Tacoma-Bellevue, WA | 80 | $126,820 +58% |
| Los Angeles-Long Beach-Anaheim, CA | 70 | $109,250 +36% |
| Atlanta-Sandy Springs-Roswell, GA | 60 | $62,450 -22% |
| Greensboro-High Point, NC | 60 | $78,200 -3% |
| Knoxville, TN | 60 | $76,940 -4% |
| Riverside-San Bernardino-Ontario, CA | 40 | $156,310 +95% |
| San Diego-Chula Vista-Carlsbad, CA | 30 | $129,150 +61% |
| San Francisco-Oakland-Fremont, CA | 50 | $129,120 +61% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 40. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.