New opportunity

Assistant/Associate Professor in AI for Medical Education

Duke-NUS Medical School · Singapore

About this role

Duke-NUS Medical School seeks a full-time Assistant/Associate Professor in AI for Medical Education in its Office of Education at Outram Campus. The role combines AI-powered educational development, research and teaching to improve medical-student learning and prepare future physicians for AI-enhanced healthcare. Faculty rank will reflect experience, achievement and recognition.

Responsibilities

Partner with MD curriculum teams to design, implement and evaluate intelligent tutoring systems, adaptive learning platforms, virtual patient simulations and personalized learning applications.
Support AI-driven assessments that provide automated feedback, performance analytics and competency evaluation. Collaborate with Education and IT teams on learning analytics and educational data management.
Research the effectiveness of AI in medical education, publish in peer-reviewed journals, present at conferences and secure internal and external funding for educational innovation.
Develop and deliver an AI literacy curriculum for medical students; train faculty to use and teach about AI; mentor students, healthcare educators and investigators. Contribute to teaching, service and administration, and collaborate across the SingHealth Duke-NUS Academic Medical Centre.

Requirements

Doctoral degree in Computer Science, Biomedical Informatics, Data Science, Artificial Intelligence, Machine Learning, Educational Technology or a closely related field; at least 3 years of experience developing and applying AI/ML in education, healthcare or another relevant domain.
Demonstrated AI/ML expertise; strong programming skills in Python, R or similar languages; experience developing, training and deploying models; knowledge of NLP, computer vision and/or multimodal learning; and experience with learning analytics, educational data mining, human-centred design and user experience research.
Evidence of research and scholarship; understanding of AI bias, fairness and privacy; strong communication, organization, collaboration and mentoring skills, including the ability to explain AI concepts to non-technical audiences and handle confidential information discreetly.

Desirable: Postdoctoral training or a prior faculty appointment in a relevant area. Familiarity with learning management systems or digital learning platforms is advantageous. Applications must include a letter of interest and curriculum vitae submitted online.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Artificial IntelligenceMachine LearningPythonRNatural Language ProcessingComputer VisionMultimodal LearningLearning AnalyticsEducational Data MiningData ManagementHuman-Centred DesignUser Experience ResearchAI Model DevelopmentAI Model TrainingAI Model DeploymentEducational TechnologyIntelligent Tutoring SystemsAdaptive LearningVirtual Patient SimulationAI-Driven AssessmentAI EthicsResearchGrant WritingTeachingMentoring

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Integrate AI tools into the MD curriculum
The role partners with curriculum teams to design, implement and evaluate tutoring systems, adaptive learning platforms, virtual patient simulations and personalized learning applications. Prepare to discuss how educational goals would guide tool design and evaluation.
Improve assessment and learning analytics
The post supports AI-driven feedback, performance analytics and competency evaluation, alongside Education and IT teams’ work on educational data. Prepare to explain how assessment outputs could be checked for usefulness and reliability.
Build a research and funding programme
Responsibilities include original research on AI’s effectiveness in medical education, peer-reviewed publications, conference presentations and securing internal and external funding. Prepare to articulate a focused research agenda and how its outcomes could be measured.
Teach AI literacy and develop others
The faculty member will help deliver AI literacy teaching for medical students, train faculty and mentor students, educators and investigators. Prepare to explain technical concepts clearly for audiences with different levels of AI experience.
Make adoption collaborative and responsible
The role works across Duke-NUS and the SingHealth Duke-NUS Academic Medical Centre. The posting also calls for human-centred design experience and attention to bias, fairness, privacy and confidentiality. Prepare to describe how these considerations would shape collaboration and deployment.

Likely challenges to think through

Personalization versus educational validity
Possible challenge, not a confirmed employer problem: an adaptive tool may respond to individual learning patterns without improving learning. Prepare to discuss how you would define learning objectives, involve users and evaluate educational outcomes before expanding use.
Fast feedback versus trustworthy assessment
Possible challenge, not a confirmed employer problem: automated feedback and competency analytics may be useful at scale but sensitive to bias, errors and student-data privacy. Prepare to discuss validation, fairness checks, appropriate human review and data safeguards.
Delivery demands versus scholarly output
Possible challenge, not a confirmed employer problem: curriculum development, teaching, mentoring and service may compete for time with research and funding work. Prepare to explain how you would prioritize projects and connect implementation work to rigorous, publishable evaluation.

Practice your answers

Technical practice

How would you design and evaluate an intelligent tutoring or adaptive learning tool for the MD curriculum?

What to coverSuggested preparation: Explain how you would identify a learning need with curriculum teams, choose what the tool adapts to, and test whether adaptation improves learning. Discuss the trade-off between personalization and keeping the learning experience understandable to students and educators.

What would you consider when developing a virtual patient simulation?

What to coverSuggested preparation: Describe how you would work with clinician educators to define the intended learning outcomes and review the simulation’s clinical and educational quality. Explain how you would assess its usefulness before wider integration into teaching.

How would you assess the quality of AI-generated feedback and competency evaluations?

What to coverSuggested preparation: Address alignment with the competencies being assessed, the accuracy and usefulness of feedback, and when educator review is needed. Distinguish a system that gives plausible feedback from one that supports defensible assessment decisions.

How would you use learning analytics to identify learning needs while managing educational data responsibly?

What to coverSuggested preparation: Explain what data and outcomes you would examine, how you would check for misleading patterns, and how insights might inform curriculum decisions. Include data quality, access and privacy considerations when collaborating with Education and IT teams.

How would you choose, train and deploy an AI model for a medical-education application?

What to coverSuggested preparation: Compare a relevant approach, such as NLP or multimodal learning, against the task and available data rather than assuming one method fits every application. Cover validation, deployment monitoring and the practical trade-off between model capability and maintainability.

How would you evaluate whether an AI educational tool improves medical-student learning?

What to coverSuggested preparation: Define an educational outcome and an appropriate comparison, then discuss how you would account for confounding factors and unintended effects. Explain how the findings could support peer-reviewed publication without overstating what the study establishes.

How would human-centred design and UX research shape an AI learning tool?

What to coverSuggested preparation: Describe how you would gather input from students and educators, test a prototype, and revise it in response to observed use. Address a trade-off between technical sophistication and a tool people can use effectively in the curriculum.

How would you investigate bias, fairness and privacy risks in an AI-driven assessment?

What to coverSuggested preparation: Identify whose outcomes could differ, what evidence you would examine, and how you would respond if performance varied across student groups. Include safeguards for sensitive information and limits on what automated results should be used to decide.

What would you include in AI literacy teaching for medical students and faculty?

What to coverSuggested preparation: Outline learning goals that connect AI concepts with responsible use in medical education and AI-enhanced healthcare. Explain how you would adapt examples and activities for students versus faculty learning to use or teach about AI.

How would you develop a fundable research proposal on AI in medical education?

What to coverSuggested preparation: Frame a specific educational question, a feasible study and a plan for measuring results. Discuss how collaborators, anticipated limitations and dissemination through publications or conferences would strengthen the proposal.

Behavioural practice

Tell me about a time you worked across technical and educational or clinical disciplines on a shared initiative.

What to coverSuggested preparation: Use a real situation, your actions to reconcile different priorities, and the result. Make clear how you built a working relationship rather than describing only your technical contribution.

Describe a time you explained a complex AI or data concept to a non-technical audience.

What to coverSuggested preparation: Give the situation, how you adjusted your explanation or presentation, and what the audience was able to do afterward. Note how you checked understanding without obscuring important limitations.

Give an example of mentoring someone through an AI-related research or learning challenge.

What to coverSuggested preparation: Describe the person’s goal and starting point, the guidance you provided, and the result. Distinguish what you contributed from what the mentee accomplished.

Tell me about a time you managed competing project deadlines alongside teaching, research or service commitments.

What to coverSuggested preparation: Set out the situation, how you prioritized and communicated trade-offs, and the outcome. Focus on specific organizational decisions rather than a general claim that you handle pressure well.

Describe a situation in which you had to protect confidential information while collaborating with others.

What to coverSuggested preparation: Explain the situation without disclosing the sensitive information itself, the boundaries you set on access or sharing, and the result. Show how you supported the work while exercising discretion.

Sharpen your knowledge

Topics to prepare

AI-supported medical learning
Compare intelligent tutoring, adaptive learning, virtual patient simulation and personalized learning: for each, identify the learner need, how the tool would respond and how its educational value could be evaluated. These are examples named in the role.
Assessment and learning analytics
Map how automated feedback, performance analytics and competency evaluation differ, then identify what educational data each would need and where interpretation could go wrong. The posting calls for AI-driven assessment and learning analytics.
Relevant AI methods
Review when NLP, computer vision or multimodal learning would be appropriate for an educational task, and explain the limits of each choice. The posting asks for knowledge of these domains, not expertise in every one.
Bias, fairness, privacy and confidentiality
Critique a hypothetical student-facing AI assessment for uneven performance, privacy risks and inappropriate disclosure; outline safeguards to investigate. These concerns are explicit competencies in the posting.
Evidence for educational effectiveness
Sketch a research question, outcome measures and plausible comparison for an AI application in medical education; note threats to interpreting the results. The role includes original research, publication and conference presentation.
AI literacy for future physicians
Outline the concepts a medical student would need to understand to discuss an AI tool's uses and limitations in healthcare. The role explicitly includes developing and delivering an AI literacy curriculum.

Skills to revise

Develop, train and deploy a model
Using Python, R or another language named by the posting, rehearse explaining a small model workflow from data preparation and training through evaluation and deployment. Model development, training, deployment and programming are explicit requirements.
Analyze educational data
With fabricated learner records, calculate a performance pattern, check for missing or misleading data, and produce an interpretable summary. This practices the stated learning analytics, educational data mining and data-management work without using confidential information.
Conduct user-centred inquiry
Draft interview questions for students and faculty about a proposed AI learning tool, then turn hypothetical feedback into a revised design and usability check. The posting explicitly seeks human-centred design and user experience research.
Plan a publishable study and funding case
Draft a one-page study outline with a research question, evaluation plan, expected contribution and funding rationale. This rehearses the role's research, dissemination and internal or external funding responsibilities; it is a preparation exercise, not a specified application requirement.
Teach AI to non-technical audiences
Practice a brief explanation of an AI concept for medical students, then adapt it into a faculty workshop activity and invite questions. This reflects the stated AI literacy teaching, faculty training and communication duties.

Your preparation checklist

Map your evidence to the qualifications
Prepare a one-page evidence map covering the doctoral degree, at least three years of relevant AI/ML application, programming, model development and deployment, learning analytics, educational data mining, human-centred design, and scholarship. Mark gaps rather than implying experience you do not have.
Build a technical project walkthrough
Choose a relevant project, if available, and prepare a concise account of the problem, users, data, model choices, training, deployment, evaluation and your contribution. Be ready to explain the work to both technical and non-technical audiences.
Practice a medical-education design exercise
Sketch a proposed intelligent tutor, adaptive learning tool or virtual patient simulation for the MD curriculum. Label it as a proposal, and show how you would work with curriculum teams and students to test usability and learning value.
Prepare an assessment and data plan
Outline how a proposed AI-driven assessment could provide feedback or competency insights, how you would validate its outputs, and how Education and IT teams might support learning analytics and data management. Include safeguards for bias, fairness, privacy and confidentiality.
Frame a research and funding agenda
Draft two research questions about the effectiveness of AI in medical education, with feasible evaluation measures and potential publication outputs. Prepare accurate examples of your own scholarship and funding contributions, distinguishing lead roles from supporting roles.
Outline teaching and mentoring examples
Prepare a short AI-literacy lesson idea for medical students and a faculty-workshop outline on using or teaching about AI. Identify real examples, if any, of mentoring students, educators or investigators.
Prepare collaboration questions
Write questions about MD curriculum priorities, how AI tools are evaluated, Education–IT collaboration, and opportunities to work across the SingHealth Duke-NUS Academic Medical Centre. Use the answers to refine your proposed work, not to assume existing processes.
Tailor the application materials
Prepare the requested letter of interest and curriculum vitae for online submission. Connect documented experience to educational delivery, assessment, research and teaching; distinguish demonstrated accomplishments from proposed initiatives.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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