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Nursing Academia and AI Governance: Why Human Rights Must Guide the Future of Healthcare Technology


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Cleofas, J. V. (2026). Towards a framework for human rights-based stakeholdership of nursing academia in the healthcare artificial intelligence governance ecosystem: A discussion paper. Nurse Education in Practice, 92(104737), 104737. https://doi.org/10.1016/j.nepr.2026.104737

What this paper is about

Artificial intelligence, or AI, is rapidly entering healthcare. It is now being used in patient assessment, decision support, imaging, health records, telehealth, robotics, nursing care planning, virtual assistants, and even education. These technologies may help improve care, reduce workload, and support clinical decision-making.

But AI also brings risks. It can reproduce bias, threaten privacy, give unsafe recommendations, widen health inequalities, weaken human-centered care, and create unclear lines of accountability when harm happens.

This paper asks: What should nursing academia do in this changing healthcare AI landscape?

The author argues that nursing schools, colleges, departments, and research centers should not only teach students how to use AI. They should also become active stakeholders in AI governance. This means helping shape the rules, values, policies, research, training, and accountability systems that guide how AI is used in healthcare.

The paper’s main contribution is a proposed framework: the Human Rights-Based Stakeholdership Framework for Nursing Academia in the Healthcare AI Governance Ecosystem.

In simple terms, the framework says: nursing academia should help ensure that healthcare AI protects dignity, equity, safety, autonomy, privacy, and the right to health.

Why this matters

AI in healthcare is not only a technical issue. It is also a nursing issue, an education issue, a public health issue, and a human rights issue.

If an AI system misreads patient data, who is responsible?
If an algorithm gives poorer recommendations for marginalized groups, who notices and corrects it?
If nurses are expected to use AI tools without proper training, who protects them?
If patients do not understand how AI is involved in their care, how can they give informed consent?
If nursing students graduate without AI literacy, how prepared are they for future practice?

These questions show why nursing academia matters. Nursing education institutions shape future nurses. They produce research. They influence policy. They serve communities. They can therefore help ensure that AI is not adopted only because it is new, efficient, or profitable, but because it genuinely supports equitable, ethical, and human-centered healthcare.

The key idea: nursing stakeholdership

The paper uses the idea of stakeholdership. This means active involvement by people or organizations that can influence, or be influenced by, a system.

In this article, nursing academic stakeholdership refers to the actions, expressions, involvements, and resources of nursing academic institutions as they participate in healthcare AI governance.

This is important because nursing academia is often treated as a training provider or research producer. The paper gives it a stronger role. It says nursing academia should be understood as a governance actor.

That means nursing schools and research centers can help:

  • define healthcare AI problems;
  • assess risks and benefits;
  • evaluate AI tools and policies;
  • manage harms;
  • educate nurses and students;
  • conduct justice-oriented research;
  • partner with communities;
  • advocate for patient and worker rights;
  • hold powerful actors accountable.

The framework: three axes of nursing academia’s role

The framework has three major axes: domains, modalities, and human rights considerations.

1) Domains: What areas of AI governance can nursing academia influence?

The first axis identifies four governance domains.

Framing

Framing means helping define the problem. Nursing academia can ask: What healthcare problems should AI solve? Whose needs are being prioritized? What risks are being ignored? Which communities are missing from the conversation?

This matters because the way a problem is framed shapes the solutions that follow.

Risk/benefit assessment

Nursing academia can help assess the possible benefits and harms of AI tools. For example, an AI triage tool may improve efficiency, but it may also reproduce bias if trained on unequal datasets. A staffing algorithm may help with scheduling, but it may also intensify workload or reduce professional judgment.

Evaluation

Evaluation means studying whether AI actually works as promised. Nursing researchers can examine whether AI improves care, reduces disparities, supports nurses, protects privacy, and remains acceptable to patients and communities.

Risk management

Risk management means creating safeguards. Nursing academia can help design policies, training, protocols, ethical guidelines, and accountability systems to reduce harm.

2) Modalities: How can nursing academia act?

The second axis is based on the three major functions of academia: instruction, research, and service/extension.

Instruction

Through teaching, nursing academia can prepare students and faculty to understand AI. This includes AI literacy, ethics, bias, privacy, explainability, patient rights, and safe use in clinical practice.

Instruction should not only teach “how to use AI.” It should also teach students how to question AI.

Research

Through research, nursing academia can study how AI affects patients, nurses, communities, and health systems. Nursing research can examine bias, access, clinical safety, human-centered care, professional autonomy, workload, data protection, and health equity.

Research can also generate evidence that informs AI policy and regulation.

Service/extension

Through service and extension, nursing academia can work with hospitals, communities, civil society groups, policymakers, professional organizations, and technology developers. This can include public forums, community education, policy consultations, advocacy campaigns, and partnerships for ethical AI implementation.

This is where nursing academia moves beyond the classroom and becomes a public actor.

3) Human rights: For whom and toward what end?

The third axis is the most important ethical anchor of the paper. The framework uses a Human Rights-Based Approach, or HRBA.

This approach asks: Who has rights? Who has duties? What systems are needed to protect dignity, equality, autonomy, privacy, participation, and accountability?

The paper highlights several human rights issues in AI-enabled healthcare:

  • autonomy and informed consent;
  • privacy and data protection;
  • equality and non-discrimination;
  • accountability and redress;
  • availability, accessibility, acceptability, and quality of health services.

This means nursing academia should not only ask whether AI is effective. It should ask whether AI is just.

Rights claimants and duty bearers

A major strength of the framework is its distinction between rights claimants and duty bearers.

Rights claimants

Rights claimants are people and groups whose rights must be protected. In AI-enabled healthcare, these include:

  • patients;
  • marginalized communities;
  • citizens;
  • frontline healthcare workers;
  • nursing students;
  • nurse educators;
  • civil society organizations.

Patients have rights to safe, equitable, respectful, and understandable care. Nurses and healthcare workers also have rights: fair working conditions, professional autonomy, participation in AI-related decisions, and protection from technologies that threaten their integrity or security.

Students also have rights. They deserve quality education that prepares them for AI-enabled healthcare.

Duty bearers

Duty bearers are institutions or actors responsible for protecting and fulfilling rights. These include:

  • governments;
  • healthcare institutions;
  • AI developers;
  • medical technology companies;
  • regulatory bodies;
  • international organizations.

The framework argues that nursing academia has two linked roles.

First, it can empower rights claimants. This means helping patients, nurses, students, and communities understand their rights and participate in decision-making.

Second, it can capacitate and hold accountable duty bearers. This means helping governments, institutions, and technology developers design better policies, improve training, reduce risk, and respond when harm occurs.

What makes this framework useful?

The framework is useful because it connects nursing academia’s everyday functions to bigger questions of AI governance.

A nursing school can ask:

  • Are we teaching AI ethics and human rights?
  • Are students learning how AI may reproduce bias?
  • Are faculty trained to evaluate AI tools critically?
  • Are we researching how AI affects nurses and patients?
  • Are we working with communities affected by digital health inequalities?
  • Are we helping policymakers understand nursing perspectives?
  • Are we advocating for accountability when AI causes harm?

The framework helps nursing academia move from passive adaptation to active governance participation.

In simple terms: nursing academia should not only prepare nurses for AI. It should help shape the kind of AI healthcare deserves.

Bottom line

This paper argues that nursing academia has a major role to play in the future of healthcare AI.

AI will not automatically make healthcare more ethical, equitable, safe, or human-centered. It must be governed. And because nurses are close to patients, communities, care systems, and health inequalities, nursing academia brings a perspective that AI governance urgently needs.

The core message is clear: healthcare AI governance must be human rights-based, and nursing academia should be one of its active stakeholders.

Policy/practice recommendations

  1. Integrate AI ethics into nursing curricula
    Nursing programs should teach AI literacy, algorithmic bias, data privacy, explainability, patient rights, informed consent, and accountability.
  2. Treat AI as a human rights issue
    AI should not be taught only as a technical tool. It should be discussed in relation to autonomy, equality, non-discrimination, privacy, safety, and the right to health.
  3. Prepare nurse educators first
    Faculty development is essential. Nurse educators need training so they can teach, supervise, and research AI critically and responsibly.
  4. Support nurse-led AI research
    Nursing research should examine how AI affects care quality, equity, workload, patient experience, clinical judgment, and professional autonomy.
  5. Create partnerships beyond nursing
    Nursing academia should collaborate with computer scientists, ethicists, lawyers, policymakers, patient groups, civil society, hospitals, and technology developers.
  6. Include patients and communities in AI governance
    AI policies should not be designed only by experts and corporations. Patients, marginalized communities, and frontline workers must have meaningful participation.
  7. Develop AI risk assessment tools for nursing practice
    Nursing schools and research centers can help create tools that assess whether AI systems are safe, fair, explainable, culturally acceptable, and rights-protective.
  8. Advocate for policy and accreditation standards
    Regulators and accreditation bodies should include AI ethics, digital health equity, and human rights-based competencies in nursing education standards.
  9. Protect nurses from unsupported AI implementation
    Healthcare institutions should not expect nurses to use AI tools without training, time, legal clarity, and organizational support.
  10. Hold duty bearers accountable
    Governments, technology companies, and healthcare institutions must be answerable when AI systems cause harm, deepen inequity, or violate rights.

Glossary of key terms

  • Artificial intelligence / AI — Computer systems that can perform tasks usually associated with human intelligence, such as learning from data, recognizing patterns, generating text, or supporting decisions.
  • Healthcare AI — AI systems used in health settings, including diagnosis, monitoring, care planning, triage, telehealth, education, documentation, and management.
  • AI governance — The rules, policies, structures, and processes that guide how AI is designed, used, monitored, and held accountable.
  • Healthcare AI governance ecosystem — The network of technologies, risks, stakeholders, policies, and mechanisms that shape AI use in healthcare.
  • Nursing academia — Nursing schools, colleges, departments, universities, research centers, and other higher education institutions involved in nursing education, research, and service.
  • Stakeholdership — Active involvement by individuals or organizations that influence, or are influenced by, a system.
  • Nursing academic stakeholdership — The participation of nursing academic institutions in shaping healthcare AI governance through teaching, research, service, advocacy, and collaboration.
  • Human Rights-Based Approach / HRBA — An approach that centers human dignity, equality, participation, accountability, and the protection of rights.
  • Rights claimants — People and groups whose rights must be respected and protected, such as patients, nurses, students, communities, and citizens.
  • Duty bearers — Institutions or actors responsible for respecting, protecting, and fulfilling rights, such as governments, healthcare institutions, regulators, and technology developers.
  • Right to health — The right of every person to the highest attainable standard of physical and mental health.
  • Autonomy — A person’s right to make informed decisions about their own care.
  • Informed consent — Agreement to care or procedures based on clear, understandable, and sufficient information.
  • Data privacy — Protection of personal information, including sensitive health data.
  • Algorithmic bias — Unfair or discriminatory outcomes produced by AI systems, often because of biased data or design.
  • Explainability — The ability to understand how an AI system reaches a recommendation or decision.
  • Non-discrimination — The principle that people should not be treated unfairly based on identity, status, condition, or social position.
  • Accountability — The responsibility of individuals or institutions to explain decisions, correct harm, and answer for failures.
  • Redress — Processes through which people can seek remedy when harm or rights violations occur.
  • Instruction — The teaching function of academia, including curriculum, training, simulation, and faculty development.
  • Research — The knowledge-generating function of academia, including studies on AI safety, ethics, equity, and impact.
  • Service/extension — Community engagement, advocacy, consultation, and partnership work done by academic institutions.
  • Framing — Defining the problem, priorities, and values that guide AI governance.
  • Risk/benefit assessment — Examining potential harms and benefits of AI systems.
  • Evaluation — Studying whether AI systems and policies work as intended.
  • Risk management — Creating safeguards to prevent, reduce, and respond to AI-related harm.
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