Artificial intelligence in a multi-disciplinary university: From adoption to stewardship

WHAT DO YOU MEAN BY AI STEWARDSHIP?

AI stewardship refers to responsible guardianship, ensuring that AI systems align with institutional values, ethical standards, and societal responsibility.^[2,3]

WHAT IS THE NEED FOR AI STEWARDSHIP?

The rapid diffusion of generative AI has created both opportunity and disruption. Without structured oversight, adoption risks becoming fragmented and ethically inconsistent. The WHO in 2021 and the Indian Council of Medical Research (ICMR) in 2023 provided the core principles that guide AI in healthcare. The principles focus on protecting human autonomy, promoting human well-being, safety, and public interest, trustworthiness, ensuring transparency, “explainability” and intelligibility, fostering responsibility and accountability, ensuring inclusiveness and equity, ensuring data privacy, and optimizing data quality, and collaboratively promoting AI that is responsive and sustainable.^[4,5] Hence, the need for AI stewardship.

WHAT DOES AI STEWARDSHIP MEAN IN A MULTIDISCIPLINARY UNIVERSITY?

The AI stewardship in a multidisciplinary university means recognizing the growing role of AI in education and inculcating proper training and ethical awareness among faculty and students. Literature shows that the educators and educational institutions must guide their students in understanding the benefits and limitations of AI technologies.^[6]

AI STEWARDSHIP AND RESPONSIBILITIES IN ENGINEERING AND BIOMEDICAL SCIENCES

In engineering, outcomes depend on algorithms, validation, and transparency. In biomedical sciences, the AI should align with the ethical principles of beneficence, non-maleficence, autonomy, justice, and explicability. AI systems design requires shared responsibility between engineering and biomedical scientists while preserving their core principles, guided by AI stewardship that ensures responsible use, bias mitigation, interdisciplinary collaboration^[7] while promoting human well-being and safety, respecting their rights, ensuring fairness, and providing explanation on the AI decisions.^[8]

AI STEWARDSHIP IN HEALTH SCIENCES EDUCATION

AI is transforming health sciences education by improving learning methods, clinical training, and access to information for students and educators. AI technologies such as intelligent tutoring systems, predictive analytics, and generative AI tools help personalize learning and support students in understanding complex medical concepts. Adaptive learning platforms, automated assessments, and simulation-based training allow students to practice clinical decision-making in safe environment and help them develop critical thinking and diagnostic skills before interacting with real patients.^[6]AI can assist healthcare professionals by analyzing complex datasets, enabling students to better understand patterns in diagnosis and treatment.

However, uncritical reliance may weaken clinical reasoning. Human judgment, contextual reasoning, and patient-centered ethics remain irreplaceable.^[9,10] The role of AI stewardship should ensure that AI augments but does not replace professional responsibility. Educational institutions must guide the responsible and ethical use of AI in education and patient care.

ARE THE CURRENT CURRICULA IN THE UNIVERSITIES SUFFICIENT TO TRAIN IN AI?

Exposure to AI tools or sporadic discussions on AI topics may cause sensitization to the presence of AI, but does not build competence in AI. The curricula of the programs offered by many universities are not completely designed to address the rapid development and impact of AI. A comprehensive framework integrating AI competencies such as AI ethics, governance, and responsible use is the need of the hour.

United Nations educational, scientific and cultural organization emphasizes that education systems should integrate ethics, human rights, and social responsibility into AI-related learning. It also advises that the Universities should ensure that students understand not only how AI works but also its ethical implications, potential biases, and societal consequences.^[2] The AI Act, by the European Union, recommends that educational programs should integrate both technical AI skills and Ethical governance into their curricula. Hence, there is a need for a complete AI competency framework and a curriculum that integrates it.^[11]

HOW SHOULD SCHOLARLY ASSESSMENT EVOLVE?

The growing use of generative AI tools that are easily accessible poses a big challenge to traditional assessment methods. Studies have shown that ChatGPT performed at a level comparable to passing scores on parts of the United States Medical Licensing Examination medical examination.^[12] This finding suggests that assessments based only on factual knowledge may no longer accurately measure a student’s competence. Thus, assessment strategies should include clearer guidelines on AI use and methods that encourage original thinking and ethical practice. Clinical simulations, oral examinations, problem-based learning assessments, and reflective tasks are some approaches that focus on clinical reasoning, communication, and judgment, which AI tools cannot easily replicate.

The other major concern that studies highlight is about academic integrity when students use generative AI tools without proper guidance.^[13] AI detectors to assess assignments to evaluate integrity might be necessary and commonplace in the future.

WHAT RESEARCH AGENDA SHOULD GUIDE AN UNIVERSITY?

Research on AI-assisted learning outcomes, validation of AI tools in clinical education, bias detection in biomedical datasets, interdisciplinary AI collaborations, and faculty development models for AI literacy^[14] should be encouraged. Recent scholarship calls for structured AI competency frameworks in health professions education. Research should examine how AI can support medical education and learning outcomes.

AI stewardship should also promote a strong research agenda prioritizing AI-assisted education, ethical and responsible AI use, innovative learning technologies, and healthcare applications of AI, ensuring that universities contribute to both academic advancement and societal benefit.

WHAT GOVERNANCE STRUCTURES ARE REQUIRED?

AI stewardship requires updated ethics review mechanisms, data governance frameworks, and institutional policy clarity. AI Act proposes a risk-based governance approach, where AI systems are classified according to their level of risk. Systems considered high-risk, such as those used in healthcare, must comply with strict requirements, including quality data management, risk assessment, transparency, and human oversight. Governance must remain adaptive and anticipatory rather than reactive. Another important governance structure is the establishment of regulatory and supervisory bodies that monitor AI development and ensure compliance with legal standards. These bodies help enforce regulations, evaluate AI systems before deployment, and investigate potential risks or misuse.^[11]

The Act also emphasizes accountability and transparency. Organizations developing or deploying AI must clearly document how their systems work, maintain records of data usage, and ensure that users are informed when interacting with AI systems. This helps build trust and allows stakeholders to understand how AI decisions are made.^[11]Overall, the governance structures required include risk-based regulation, strong oversight institutions, transparency mechanisms, accountability frameworks, and mandatory human supervision of high-risk AI systems. These measures aim to ensure that AI technologies are developed and used in ways that protect individuals, maintain safety, and promote public trust.

CONCLUSION

AI is rapidly transforming health sciences education, research, and healthcare practice, creating both opportunities and challenges for universities. To address these challenges, universities must update their curricula, assessment methods, and governance structures. AI Stewardship is a strategy that guides academic integrity, ethical use, and responsible integration of AI into education systems.