Will AI Replace Biological Science Teachers, Postsecondary?

AI will not replace biological science teachers in postsecondary education, though it will significantly reshape how they work. The profession carries a low overall risk score of 42 out of 100, reflecting the deeply human elements at the core of university teaching. While AI can assist with lecture preparation, assessment creation, and even some grading tasks, the essential work of mentoring graduate students, designing original research, facilitating laboratory experiences, and fostering critical scientific thinking remains fundamentally human.

The profession's resilience stems from its multidimensional nature. Biological science professors don't simply transmit information, they model scientific reasoning, guide hands-on experimentation, provide career mentorship, and push the boundaries of knowledge through original research. These activities require judgment, empathy, physical presence, and creative problem-solving that AI cannot replicate. In 2026, we're seeing AI tools augment teaching workflows rather than replace the teachers themselves.

Employment data supports this stability. With 53,250 professionals currently employed and average job growth projected through 2033, the field shows no signs of contraction due to automation. The role is evolving toward greater integration with AI tools, but the human biological science teacher remains central to postsecondary education.

In 2026, AI has become a practical teaching assistant rather than a replacement for biological science professors. Faculty are using AI tools to streamline lecture preparation, generate practice problems, create initial assessment drafts, and provide students with 24/7 tutoring support for foundational concepts. Research from EDUCAUSE shows that higher education institutions are rapidly integrating AI into teaching workflows, with faculty experimenting with tools that can explain complex biological processes, generate visualizations of molecular structures, and even suggest experimental designs.

The most impactful applications focus on tasks with high repetitiveness and clear right answers. Our analysis indicates that lecture preparation and assessment creation show potential for 55% time savings when AI assists with initial drafts and routine elements. However, professors still provide the critical oversight, context, and refinement that transforms AI-generated content into pedagogically sound materials. AI can draft a quiz on cellular respiration, but the professor determines whether it aligns with learning objectives, adjusts difficulty for the specific student cohort, and ensures it integrates with laboratory work.

Laboratory instruction remains largely untouched by automation. While AI can help design experiments or analyze data patterns, the hands-on mentorship of students learning sterile technique, microscopy, or field sampling requires physical presence and real-time adaptation. The technology augments administrative and preparatory work, freeing professors to focus more on the irreplaceable human elements of teaching and research.

Biological science professors should focus on developing AI literacy alongside their scientific expertise. This means understanding how large language models work, recognizing their limitations in scientific accuracy, and learning to prompt AI tools effectively for teaching and research tasks. The goal is not to become a programmer, but to become a sophisticated user who can leverage AI for routine tasks while maintaining rigorous scientific standards. Professors who can critically evaluate AI-generated content for factual accuracy and pedagogical appropriateness will have a significant advantage.

Equally important is strengthening the distinctly human skills that AI cannot replicate. This includes advanced mentorship capabilities, the ability to design authentic learning experiences that develop scientific reasoning, and expertise in facilitating collaborative research. As AI handles more routine explanation and assessment, the professor's role shifts toward higher-order teaching: asking provocative questions, designing complex laboratory experiences, guiding students through ambiguous research problems, and modeling the scientific process. These skills have always been valuable but become even more central as AI automates the more mechanical aspects of teaching.

Finally, professors should develop competence in teaching students how to use AI responsibly in biological science. This includes addressing academic integrity concerns, helping students understand when AI-generated information about biological systems may be unreliable, and preparing future scientists to work in AI-augmented research environments. The professors who thrive will be those who view AI as a tool that expands their capacity rather than a threat to their expertise.

The transformation is already underway in 2026, but the pace of change varies dramatically across institutions and individual faculty members. Early adopters are currently using AI to streamline lecture preparation, generate practice materials, and provide supplemental tutoring to students. However, widespread institutional integration is still 3 to 5 years away, as universities grapple with questions about academic integrity, pedagogical effectiveness, and equitable access to AI tools. The change is happening incrementally rather than as a sudden disruption.

The next major shift will likely occur as AI tools become more sophisticated at understanding and explaining complex biological concepts with greater accuracy. Current AI systems sometimes generate plausible-sounding but scientifically incorrect information about biological processes, which limits their utility in advanced courses. As these reliability issues improve over the next few years, we'll see broader adoption for more sophisticated teaching tasks. However, the core structure of postsecondary biological science education, centered on laboratory work, research mentorship, and critical thinking development, will remain largely intact.

The timeline for change is also constrained by the nature of academic institutions themselves. Universities move deliberately when adopting new pedagogical approaches, requiring evidence of effectiveness and careful consideration of unintended consequences. This measured pace actually protects the profession, allowing biological science teachers to adapt gradually rather than facing sudden obsolescence. The role will continue evolving throughout the 2020s, but the human professor will remain central to the educational experience.

AI's impact will indeed vary by institution type, but not in the direction many expect. Teaching-focused institutions might see more immediate integration of AI tools for routine instructional tasks, as these colleges prioritize pedagogical efficiency and student support. AI tutoring systems, automated grading for large introductory courses, and AI-assisted course design could become standard more quickly at teaching colleges. However, this doesn't translate to job losses, as these institutions still require human faculty to mentor students, facilitate discussions, and provide the personal attention that defines their educational model.

Research universities face a different dynamic. Here, AI is becoming a research tool as much as a teaching tool, with applications in genomic analysis, protein structure prediction, and ecological modeling. Biological science professors at research institutions are increasingly expected to integrate AI into their research programs, which actually increases demand for faculty who can bridge traditional biological science and computational methods. The teaching component remains important but represents a smaller portion of the role compared to research productivity and grant acquisition.

Across both settings, the profession shows stable employment prospects. The BLS projects average growth through 2033, with no indication that AI will reduce the need for qualified faculty. The bigger challenge may be evolving institutional expectations, where professors are expected to maintain research productivity, teach effectively, and now also demonstrate competence with AI tools, all without additional compensation or reduced workload. The jobs will remain, but the demands within those jobs are intensifying.

AI is transforming biological research more rapidly than teaching, creating both opportunities and pressures for postsecondary faculty. In 2026, AI tools are already accelerating literature reviews, identifying patterns in genomic data, predicting protein structures, and even suggesting experimental designs. Our analysis indicates that research design, execution, and dissemination tasks show potential for 40% time savings when AI assists with data analysis, literature synthesis, and manuscript preparation. This could theoretically free professors to pursue more ambitious research questions or mentor more students.

However, the reality is more complex. As AI makes certain research tasks more efficient, it also raises the bar for what constitutes publishable work. Competitors using AI tools can produce results faster, increasing pressure on all researchers to maintain productivity. Additionally, AI-generated insights still require expert interpretation, validation through experimentation, and integration into theoretical frameworks, all of which demand deep biological knowledge. The professors who thrive will be those who use AI to enhance their research capacity while maintaining the critical thinking and experimental rigor that defines good science.

The research landscape is also creating new expectations. Funding agencies and universities increasingly expect biological science faculty to incorporate computational methods and AI tools into their research programs. This doesn't replace traditional wet-lab biology but adds another dimension to the skill set required for success. Junior faculty entering the profession in 2026 are expected to be conversant with both traditional biological methods and emerging AI-assisted research approaches, reflecting the hybrid nature of modern biological science.

AI will likely reduce reliance on graduate teaching assistants for certain routine tasks, but this creates a complex situation rather than simple job displacement. AI tutoring systems can now handle many of the basic questions that students previously brought to TA office hours, and automated grading tools can assess multiple-choice exams and even some short-answer questions. This might reduce the number of TA hours needed for large introductory courses, potentially affecting graduate student funding packages that depend on teaching assistantships.

However, the laboratory and discussion sections that TAs typically lead remain difficult to automate. These sessions require real-time troubleshooting of experimental techniques, facilitation of group discussions, and the kind of responsive teaching that adapts to student confusion in the moment. AI cannot demonstrate proper pipetting technique, help students understand why their gel electrophoresis failed, or facilitate a nuanced discussion about experimental ethics. These high-touch educational experiences still require human TAs, and many institutions view TA positions as essential training for future faculty rather than merely a source of inexpensive labor.

The more significant concern is how AI might reshape graduate education itself. If AI reduces the teaching burden on faculty, departments might reduce TA positions while expecting professors to handle more teaching directly. Alternatively, institutions might redirect TA effort toward developing AI-assisted learning materials or providing more intensive mentorship to smaller groups of students. The TA role will evolve rather than disappear, but graduate students should be prepared for a teaching landscape where AI handles routine support and human TAs focus on higher-order educational interactions.

AI's impact on compensation is likely to be indirect and varied rather than uniformly positive or negative. Professors who develop expertise in AI-assisted teaching and research may command premium salaries, particularly at institutions competing for faculty who can bridge traditional biological science and computational methods. We're already seeing some universities create new positions or offer retention packages for faculty with these hybrid skill sets. However, this premium is more about expanding expertise than AI directly increasing base salaries across the profession.

The more concerning possibility is that AI could be used to justify increased teaching loads or larger class sizes. If administrators believe AI tools make teaching more efficient, they might expect professors to teach more students or more courses without additional compensation. This productivity pressure could effectively reduce hourly compensation even if nominal salaries remain stable. The profession's reliance on adjunct and contingent faculty, who are often paid per course, makes this particularly problematic, as institutions might use AI to avoid hiring additional full-time faculty.

Long-term compensation trends will likely depend more on broader higher education economics than on AI specifically. Enrollment patterns, state funding for public universities, and the ongoing debate about the value of postsecondary education will shape salary trajectories more than automation. AI is one factor among many, and its effect on compensation will vary significantly based on institution type, geographic location, and individual negotiating power. Professors with strong research programs and demonstrated teaching excellence will continue to have the strongest compensation prospects regardless of AI developments.

Laboratory instruction stands as the most automation-resistant aspect of biological science teaching. The hands-on work of teaching students proper sterile technique, microscopy skills, dissection methods, or field sampling requires physical presence and real-time adaptation that AI cannot provide. When a student's bacterial culture becomes contaminated, when a microscope needs adjustment, or when field conditions change unexpectedly, a human instructor must diagnose the problem and guide the student through troubleshooting. These tactile, situated learning experiences form the core of biological science education and show minimal automation potential.

Equally resistant is the mentorship dimension of the professor's role. Guiding a graduate student through the emotional challenges of failed experiments, helping an undergraduate decide between medical school and research careers, or providing the nuanced feedback that shapes a developing scientist, these require empathy, judgment, and relationship-building that AI cannot replicate. Our analysis shows that human interaction requirements contribute significantly to the profession's low automation risk. The professor-student relationship, built over months or years of collaboration, creates the trust and understanding necessary for effective mentorship.

Finally, the creative and strategic aspects of the role remain distinctly human. Designing a new course that integrates emerging research, developing an original research program that fills gaps in scientific knowledge, or synthesizing disparate findings into a coherent theoretical framework, these require the kind of creative insight and strategic thinking that current AI systems cannot match. While AI can assist with components of these tasks, the overarching vision and scientific judgment come from human expertise developed over years of immersion in the field.

Aspiring professors should view AI as a factor that reshapes the profession rather than eliminates it. The path to becoming a biological science professor remains viable, with stable employment projected through 2033 and no indication of AI-driven job losses. However, the expectations for new faculty are evolving. In 2026, competitive candidates are expected to demonstrate not only traditional research productivity and teaching effectiveness but also competence with AI tools and computational methods. This doesn't mean abandoning wet-lab biology, but rather adding digital literacy to an already demanding skill set.

The bigger challenge for aspiring professors is the same one that has existed for years: an oversupply of PhDs relative to tenure-track positions. AI doesn't worsen this fundamental imbalance, but it also doesn't solve it. What AI does change is the nature of the work once you secure a position. New faculty should expect to integrate AI tools into their teaching and research workflows, to navigate questions about academic integrity in an AI-enabled classroom, and to help students learn to use these tools responsibly. These are new competencies layered onto traditional expectations.

The most successful aspiring professors will be those who develop a hybrid skill set: deep expertise in a biological subdiscipline combined with comfort using AI as a research and teaching tool. This might mean taking coursework in bioinformatics, learning to code in Python or R, or experimenting with AI tools during graduate school. The goal is not to become a computer scientist but to become a biologist who can leverage computational tools effectively. The career remains viable and rewarding, but it requires adaptability and a willingness to engage with technology as an integral part of modern biological science.