Role Definition
| Field | Value |
|---|---|
| Job Title | Biological Science Teachers, Postsecondary (SOC 25-1042) |
| Seniority Level | Mid-level (Assistant/Associate Professor, 5-12 years) |
| Primary Function | Teaches courses in biological sciences — molecular biology, genetics, ecology, evolution, microbiology, cell biology, physiology, biochemistry — at colleges and universities. Combines classroom lectures with hands-on laboratory instruction where students perform wet-lab experiments (dissections, microscopy, PCR, gel electrophoresis, cell culture, specimen collection). Conducts original biological research, publishes in peer-reviewed journals, mentors undergraduate and graduate students through thesis and dissertation research, develops curricula aligned with departmental and accreditation standards. Unlike K-12 science teachers, requires a terminal degree (PhD in a biological science) and an active research programme. Unlike health specialties faculty, does not supervise students performing procedures on live human patients. |
| What This Role Is NOT | NOT a K-12 biology teacher (different regulatory framework, younger students). NOT a health specialties teacher (no patient care supervision, no clinical liability). NOT a biological technician (no independent research mandate). NOT an online-only biology instructor (removes lab supervision protection). NOT a postdoctoral researcher (no primary teaching duties). |
| Typical Experience | 5-12 years post-doctoral. PhD in a biological science required (molecular biology, ecology, genetics, microbiology, etc.). Postdoctoral research experience typical. Emerging to established research/publication record. Active grant-seeking. May supervise graduate student research. |
Seniority note: Full professors with tenure score similarly — the core work is identical with stronger structural protection. Adjuncts and part-time lecturers without tenure, research mandates, or lab supervision duties would score lower, likely Yellow, due to weaker barriers and primary exposure through lecture-only courses.
Protective Principles + AI Growth Correlation
| Principle | Score (0-3) | Rationale |
|---|---|---|
| Embodied Physicality | 1 | Lab instruction requires physical presence — supervising students handling specimens, operating microscopes, running gel electrophoresis, managing chemical reagents and biological hazards. Fieldwork in ecology/environmental biology adds outdoor physical work. But labs are structured, controlled environments and lectures are desk-based. Minor physical component overall. |
| Deep Interpersonal Connection | 1 | Mentors graduate students through multi-year research projects and dissertation work. Builds relationships with undergraduates during lab sessions and office hours. Important but more transactional than therapeutic or pastoral — primarily professional academic mentoring. |
| Goal-Setting & Moral Judgment | 2 | Designs research programmes, sets intellectual direction for lab groups, makes gatekeeping decisions about graduate student readiness, directs curriculum content reflecting evolving biological knowledge, navigates research ethics (biosafety, IRB, animal care protocols). Significant judgment in shaping what students learn and whether they progress. |
| Protective Total | 4/9 | |
| AI Growth Correlation | 0 | AI adoption does not create or destroy demand for biology professors. Demand driven by university enrolments, STEM education policy, research funding cycles, and faculty retirements. AI tools augment teaching and research but don't drive new faculty hiring. Neutral. |
Quick screen result: Protective 4/9 with neutral growth = likely Green Zone boundary, similar to Education Teachers Postsecondary. Proceed to confirm with task decomposition and evidence.
Task Decomposition (Agentic AI Scoring)
| Task | Time % | Score (1-5) | Weighted | Aug/Disp | Rationale |
|---|---|---|---|---|---|
| Classroom & lecture teaching — delivering lectures on molecular biology, genetics, ecology, evolution, physiology; leading discussions; facilitating problem-based learning | 25% | 2 | 0.50 | AUGMENTATION | AI generates lecture slides, creates diagrams, produces practice problems, and drafts explanations. But the professor delivers content drawing on research expertise, adapts to student questions, explains complex biological mechanisms through real research examples, and models scientific thinking. Human-led, AI-accelerated. |
| Laboratory instruction & supervision — supervising wet labs (dissection, microscopy, PCR, gel electrophoresis, cell culture), demonstrating techniques, ensuring biosafety compliance | 20% | 2 | 0.40 | NOT INVOLVED | Faculty must physically supervise students handling biological specimens, hazardous reagents, and lab equipment. A student pipetting incorrectly, contaminating a culture, or mishandling a specimen requires immediate in-person correction. Biosafety protocols demand a qualified human present. AI cannot physically demonstrate sterile technique or intervene when a student cuts incorrectly during dissection. |
| Research & publication — conducting original biological research, writing papers, applying for grants, presenting at conferences, peer review | 15% | 2 | 0.30 | AUGMENTATION | AI accelerates literature review, data analysis (genomic, proteomic, statistical), and draft generation. But original research questions, experimental design, wet-lab execution, fieldwork, interpreting unexpected results, and navigating peer review require human scientific judgment. Much biological research involves physical benchwork and field observation that AI cannot perform. |
| Curriculum development & course design — developing and updating biology courses, incorporating new discoveries, selecting textbooks, designing lab exercises | 10% | 3 | 0.30 | AUGMENTATION | AI generates draft syllabi, creates learning materials, and suggests course structures. Faculty direct content decisions, ensure scientific accuracy against current research, design lab exercises that teach both technique and scientific reasoning, and align curricula with department and accreditation standards. AI produces; faculty curate and validate. |
| Student assessment & grading — grading lab reports, exams, research papers; evaluating lab competence; designing assessments | 10% | 3 | 0.30 | AUGMENTATION | AI can grade multiple-choice exams, analyse performance patterns, and provide preliminary feedback on written work. But evaluating lab report quality — whether a student correctly interpreted gel electrophoresis results, whether their experimental controls were appropriate, whether they demonstrated scientific reasoning — requires expert judgment. Faculty assess scientific thinking, not just correct answers. |
| Student mentoring & advising — advising undergrad/graduate students, supervising thesis/dissertation research, career guidance, recommendation letters | 10% | 1 | 0.10 | NOT INVOLVED | Personal mentoring through the challenges of biological research — guiding students through failed experiments, helping them develop research questions, navigating graduate school applications, writing recommendation letters. Multi-year research mentorship relationships are deeply human. |
| Service & committee work — departmental committees, programme review, peer review of manuscripts, professional society leadership, tenure reviews | 5% | 2 | 0.10 | AUGMENTATION | AI assists with report drafting, data compilation, and scheduling. But faculty governance decisions, tenure evaluations, programme strategic direction, and professional society leadership require human judgment and institutional knowledge. |
| Fieldwork supervision — directing student field research in ecology, environmental biology, marine biology; supervising data collection in natural settings | 5% | 1 | 0.05 | NOT INVOLVED | Fieldwork in forests, streams, coastlines, and field stations requires physical presence in unstructured outdoor environments. Faculty supervise students collecting specimens, operating field equipment, navigating terrain, and making real-time decisions about sampling methodology. Irreducibly physical and human. |
| Total | 100% | 2.05 |
Task Resistance Score: 6.00 - 2.05 = 3.95/5.0
Displacement/Augmentation split: 0% displacement, 65% augmentation, 35% not involved.
Reinstatement check (Acemoglu): AI creates new tasks: integrating AI tools into biology curricula (teaching students to use AI for bioinformatics, protein structure prediction, genomic analysis), evaluating AI-generated scientific content for accuracy, supervising students using AI in research projects, conducting research on AI applications in biological sciences, and teaching scientific integrity in an AI era. Biology professors gain oversight and integration responsibilities as AI enters biological research and education.
Evidence Score
| Dimension | Score (-2 to 2) | Evidence |
|---|---|---|
| Job Posting Trends | +1 | BLS projects 7% growth for postsecondary teachers 2024-2034 (faster than average). Biology faculty positions steady — 66,000 employed (BLS 2024). STEM education policy continues to prioritize biological sciences. Not an acute shortage like health specialties or K-12, but consistent demand driven by replacement needs and enrolment stability. |
| Company Actions | 0 | No universities cutting biology faculty citing AI. No surge in hiring either. Institutions integrating AI into biology programmes as augmentative tools, not as faculty replacements. Virtual labs supplement but do not replace wet-lab instruction. MasteringBiology, Labster, and AI-enhanced platforms deployed to support — not replace — faculty. |
| Wage Trends | 0 | BLS median salary for biological science teachers postsecondary: $88,130. Growing nominally but tracking inflation. No significant premium or decline signals. Competitive with industry for ecology/organismal biology but below industry for molecular biology/biotech PhDs — similar to the health faculty clinical salary gap at a smaller scale. |
| AI Tool Maturity | 0 | Production tools in use: Labster (virtual lab simulations), MasteringBiology (adaptive learning), Gradescope (grading), ChatGPT/Claude (content generation), AlphaFold (protein structure). All augmentative — virtual labs supplement but cannot replace handling real specimens, running real PCR, or conducting real dissections. No viable AI alternative for wet-lab supervision. |
| Expert Consensus | +1 | Brookings/McKinsey: education among lowest automation potential (<20% of tasks). MyJobVsAI estimates ~35% of tasks may be automated by 2028 — consistent with augmentation, not displacement. WEF: 78% of education experts say AI augments, not replaces. Biology teaching adds physical lab and fieldwork protection beyond generic postsecondary teaching. Consensus: transformation of lecture/assessment layers, persistence of lab/research/mentoring core. |
| Total | 2 |
Barrier Assessment
Reframed question: What prevents AI execution even when programmatically possible?
| Barrier | Score (0-2) | Rationale |
|---|---|---|
| Regulatory/Licensing | 1 | PhD in a biological science typically required. Regional accreditation bodies and disciplinary standards (ASM for microbiology curricula, AAAS for biology education) establish faculty qualification expectations. But no state licensure required for the professor role itself — unlike K-12 teachers or healthcare practitioners. Accreditation meaningful but less rigid than medical/nursing accreditation. |
| Physical Presence | 1 | Wet-lab instruction requires physical presence — supervising students with microscopes, reagents, specimens, and biological hazards. Fieldwork requires outdoor presence in natural environments. But lectures and office hours operate effectively online/hybrid. Semi-structured environments. The lab component provides real but partial physical presence protection. |
| Union/Collective Bargaining | 1 | Faculty unions (AAUP, AFT, NEA) at many public universities. Tenure system provides structural job protection at research institutions. Not universal — many biology faculty are contingent, non-tenure-track, or at institutions without collective bargaining. Moderate protection where it exists. |
| Liability/Accountability | 1 | Faculty bear responsibility for laboratory safety — students working with biological hazards, chemical reagents, sharp instruments, and potentially pathogenic organisms. Biosafety compliance (IBC oversight for recombinant DNA, BSL-2 protocols) requires qualified human supervision. Research ethics (IACUC for animal research, IRB for human subjects) require faculty accountability. Lower stakes than patient care liability but meaningful. |
| Cultural/Ethical | 1 | Strong expectation that scientists are trained by experienced researchers who have done real benchwork and fieldwork. The credibility of biological science education depends on faculty with authentic research experience. Students and parents expect human instruction in laboratory settings where safety is a concern. Cultural preference operating within professional norms. |
| Total | 5/10 |
AI Growth Correlation Check
Confirmed at 0 (Neutral). AI adoption does not create or destroy demand for biology professors. The driver is university enrolment patterns, STEM education policy, research funding (NIH, NSF), and faculty retirement/replacement cycles. AI tools that reduce grading and content-creation burden may improve faculty productivity and job satisfaction. The growing integration of AI into biological research (AlphaFold, AI-driven drug discovery, computational genomics) creates new curriculum content to teach — but this is absorbed into existing faculty roles rather than creating new positions. If anything, AI makes the research component more productive, not redundant.
JobZone Composite Score (AIJRI)
| Input | Value |
|---|---|
| Task Resistance Score | 3.95/5.0 |
| Evidence Modifier | 1.0 + (2 × 0.04) = 1.08 |
| Barrier Modifier | 1.0 + (5 × 0.02) = 1.10 |
| Growth Modifier | 1.0 + (0 × 0.05) = 1.00 |
Raw: 3.95 × 1.08 × 1.10 × 1.00 = 4.6926
JobZone Score: (4.6926 - 0.54) / 7.93 × 100 = 52.4/100
Zone: GREEN (Green >= 48, Yellow 25-47, Red <25)
Sub-Label Determination
| Metric | Value |
|---|---|
| % of task time scoring 3+ | 20% |
| AI Growth Correlation | 0 |
| Sub-label | Green (Transforming) — >= 20% task time scores 3+, Growth != 2 |
Assessor override: None — formula score accepted. The 52.4 positions this role correctly below Health Specialties Teacher (70.9 — clinical patient supervision + acute faculty shortage) and Education Teachers Postsecondary (53.9 — student teacher supervision in K-12 classrooms). The 1.5-point gap from Education Teachers Postsecondary is appropriate: biology professors have wet-lab protection (20% of time at score 2 in NOT INVOLVED) but weaker mentoring protection (academic rather than pastoral) and more codifiable subject matter (biological facts/mechanisms vs pedagogy). Higher than Business Teachers Postsecondary (33.0 — fully codifiable subject, 0% NOT INVOLVED). The lab component is the key differentiator that holds this role in Green rather than slipping toward Yellow.
Assessor Commentary
Score vs Reality Check
The Green (Transforming) label at 52.4 is honest but sits close to the zone boundary (48) — 4.4 points above Yellow. This proximity warrants flagging but not overriding. The score is not barrier-dependent: stripping barriers entirely, task resistance alone (3.95) with neutral modifiers would yield a raw score well above the Green threshold. The 35% of time in NOT INVOLVED tasks (lab supervision, mentoring, fieldwork) provides genuine structural protection. The modest evidence (+2) and moderate barriers (5/10) are realistic — there is no acute biology faculty shortage, no state licensure requirement, and AI tools are meaningfully augmenting lecture and assessment work. The score accurately captures a role that is safe but transforming.
What the Numbers Don't Capture
- Bimodal by sub-discipline. Molecular biology and genetics faculty who run wet labs with expensive equipment and hazardous materials have strong physical presence protection. Ecology and field biology faculty who supervise fieldwork in natural environments have even stronger physical protection. But anatomy, physiology, or general biology lecturers who teach large auditorium courses without hands-on lab components are significantly more exposed — closer to Yellow.
- Bimodal by employment type. Tenured research faculty at R1 universities have strong structural protection — tenure, research mandates, grant funding, lab facilities. Adjunct and part-time lecturers at teaching-focused community colleges who teach introductory biology without research mandates face genuine displacement risk as AI enables more scalable lecture delivery.
- Virtual labs are supplements, not replacements — for now. Labster and similar platforms provide valuable simulations, but accreditation bodies, students, and employers overwhelmingly expect biology graduates to have hands-on wet-lab experience. If accreditation standards shifted to accept virtual-only lab instruction, the physical presence protection would erode. This has not happened and faces strong institutional resistance.
- Biology subject matter is more codifiable than clinical judgment. Unlike health specialties faculty who must evaluate whether a student is safe to practice on patients, biology faculty evaluate whether students understand scientific concepts and techniques. AI can articulate biological mechanisms clearly. The protection comes from the physical lab and fieldwork, not from subject matter complexity.
Who Should Worry (and Who Shouldn't)
Shouldn't worry: Faculty who combine active research programmes with hands-on laboratory instruction — the associate professor who runs a molecular biology research lab, supervises graduate students at the bench, teaches upper-division lab courses with real specimens and equipment, and takes ecology students into the field. The more time you spend in wet labs and field sites with students, the safer you are.
Should worry: Faculty whose role is primarily lecture-based with minimal lab supervision — large introductory biology lecturers in auditorium settings without a lab component, online-only biology instructors, and adjunct lecturers teaching foundational courses at multiple institutions without research or lab duties. Also at risk: faculty at institutions considering replacing wet labs with virtual-only alternatives to cut costs.
The single biggest separator: Whether your teaching involves supervising students in physical laboratories or field sites. Biology professors who own the wet-lab experience — where biosafety requires a qualified human in the room and real specimen handling cannot be simulated — are well protected. Faculty who primarily lecture about biology without that physical anchor face steeper transformation pressure.
What This Means
The role in 2028: Biology professors use AI to generate lecture materials, create practice problems, automate multiple-choice grading, produce adaptive learning modules, and accelerate literature reviews. Virtual lab platforms supplement instruction for concepts that benefit from simulation. AlphaFold and AI-driven analysis tools become standard in research and upper-division curricula. But the core job — supervising a student performing their first dissection, teaching sterile technique at the bench, guiding a graduate student through a failed experiment, conducting original biological research in the lab or field, mentoring students through the demands of scientific training — remains entirely human. The lecture layer transforms; the lab and research layers persist.
Survival strategy:
- Lean into wet-lab and field instruction — hands-on laboratory teaching is the irreducible human core. Maintain and expand your lab teaching load; resist institutional pressure to replace wet labs with virtual alternatives
- Integrate AI tools into biology curricula — teach students to use AI for bioinformatics, protein structure prediction, and data analysis. Become the faculty member who bridges AI capability and biological science, making yourself essential to the evolving programme
- Build a research programme that requires physical benchwork or fieldwork — experimental biology requiring lab execution, specimen collection, or field observation is harder to automate than purely computational or review-based research
Timeline: 10+ years for core responsibilities (lab instruction, research, mentoring, fieldwork). Lecture delivery and assessment layers transform within 2-5 years. Driven by the impossibility of automating wet-lab supervision, accreditation expectations for hands-on training, and the enduring need for physical biological research.
