Robots Can’t Replace a Nursing Professor: Evidence for the Irreplaceable Human Role in Nursing Education
The explosive growth of artificial intelligence (AI), robotics, and simulation technology has changed nursing education. Virtual simulations, auto-grading systems, and AI-enabled tutor systems tout efficiencies and scalability. The use of these and other tools in nursing education has raised increasing speculation that technology could replace nursing professors. Robots cannot replace nursing professors, as evidenced by nursing education scholarship, workforce data, and learning science research. Technology can support education, but it can’t replace human judgment, mentorship, ethical reasoning, and professional socialization.
Nursing Education Is More Than Content Delivery
Empirical research about effective nursing education points to a consistent set of findings that inform core curricular and instructional elements: faculty–student interaction, reflective learning, and guided clinical reasoning rather than the transfer of information (NLN, 2022). Nursing education is a social process (NLN, 2022). Faculty are not only tasked with the transfer of knowledge but also with complex tasks such as mentorship, role modeling, and the shaping of professional identity (NLN, 2022). An AI can read a lecture or demonstrate a procedure, but it cannot “read the room” and adapt to students’ emotional states. AI cannot account for complex cognitive states like anxiety or distress and it cannot provide professional confidence.
Benner’s Novice to Expert theoretical framework shows that expert nursing practice is not automatized but rather a product of complex experiential learning with expert clinicians and nurse educators (Benner, 1984). In other words, expert practice is the result of both complex knowledge and complex socialization through faculty coaching of uncertainty
Clinical Judgment and Ethical Reasoning Cannot Be Automated
Clinical judgment is an expected outcome of nursing programs. It is one of the essential markers of a patient safety culture. According to the National Council of State Boards of Nursing (NCSBN), clinical judgment is a cognitive human function that involves synthesis of knowledge, experience, ethics, and an understanding of the patient and situation (NCSBN, 2019). Artificial intelligence (AI) can make inferences from datasets; however, there is no machine reasoning, moral deliberation, or accountability in AI care.
Evidence-based studies report that students who are prompted by the faculty and engage in self-questioning, reflection, self-correction, and feedback during clinical or simulation learning experiences exhibit stronger clinical judgment (Lasater, 2007; Tanner, 2006). Questioning, reflection, and feedback are human functions that cannot be replaced by machine reasoning.
Simulation Technology Requires Skilled Nursing Faculty
One of the arguments for high-fidelity simulation is that it is evidence that technology will replace educators. The evidence in simulation research points to the opposite conclusion. It is not the technology, but the quality of faculty facilitation, prebriefing, and debriefing that determines the success of simulation.
The INACSL Healthcare Simulation Standards of Best Practice: Simulation-Integrated Knowledge Application in Healthcare (INACSL, 2021) indicate that psychological safety, learning transfer, and achievement of outcomes rely on having trained simulation educators (INACSL, 2021). In a systematic review, Cant and Cooper (2017) concluded that when no expert debriefing was used, the resultant learning from the simulation was significantly lower. Robots may be able to run simulators, but they cannot replace faculty expertise in reflective debriefing, emotional processing, and professional guidance.
Faculty Shortages Highlight the Need for Investment, Not Replacement
At a time when a severe nursing professor shortage is discussed, it may sound like a paradox to consider the question of professor replacement. As per the AACN, US nursing schools had to reject 65,000 qualified applicants for 2023 primarily due to the shortage of faculty rather than a lack of advanced equipment (AACN, 2024). The shortage of faculty was caused by lower academic salaries, which are a much lower incentive than the salaries of similar-level workers in clinical positions, heavy workloads, and a lack of support from the institutions.
Replacing nursing professors with robots would not solve the problem because the faculty shortage is a contributing factor. Research evidence indicates that faculty salary growth, an increase in the faculty’s comfort at work, and investment in professional development would be much more effective (Fang & Kesten, 2017).
Professional Socialization Is a Human Process
Nursing faculty are essential to the professional socialization of students. This process of professional socialization includes a way of thinking, valuing, and behaving as a professional, including characteristics such as compassion, advocacy, cultural humility, and moral accountability (Beauchamp & Childress, 2009). Social learning theory indicates that students learn professional norms by watching and interacting with a role model (Bandura, 1977).
An AI system could never be a moral exemplar in modeling courage or compassion at the end of life or advocacy in the face of an ethical dilemma. The power of these lessons rests in a relationship with a caring faculty member.
Conclusion
Evidence is mounting—and it all points in the same direction: robots can’t and won’t replace nursing professors. Research in nursing education, regulatory agencies, and workforce data all point to a shared reality: technology is a force multiplier that can and should be used in nursing education to improve simulation, assessment, and access to learning. Technology can scale certain aspects of education, but it cannot replicate human clinical judgment, ethical decision-making, mentorship, or professional socialization. Nursing education is a deeply human enterprise that is both relational and experiential. It requires educators who can guide reflection and learning, model professional values, and support learners as they navigate complex clinical and emotional situations. The future of nursing education will not be defined by replacing professors with machines, but by investing in, empowering, and valuing nursing faculty while responsibly leveraging technology to augment—not replace—human-centered education.
References:
Key References (Evidence-Based)
- American Association of Colleges of Nursing (AACN). (2024). Nursing faculty shortage fact sheet.
- Bandura, A. (1977). Social learning theory. Prentice Hall.
- Benner, P. (1984). From novice to expert: Excellence and power in clinical nursing practice. Addison-Wesley.
- Cant, R. P., & Cooper, S. J. (2017). Use of simulation-based learning in undergraduate nurse education: A systematic review. Nurse Education Today, 49, 63–71.
- INACSL. (2021). Healthcare Simulation Standards of Best Practice.
- Lasater, K. (2007). Clinical judgment development: Using simulation to create an assessment rubric. Journal of Nursing Education, 46(11), 496–503.
- National Council of State Boards of Nursing (NCSBN). (2019). Clinical judgment measurement model.
- National League for Nursing (NLN). (2022). Core competencies for nurse educators.
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