Artificial Intelligence for Human Factors Training in Clinical Education: Designing Data-Driven Feedback and Simulation

Project details

First supervisor: Dr Jonathan San Diego

Duration: 3 years 

Mode of study: Full-time 

Funding: Self-funded.  Students will need to self-fund or secure sponsorship for non-clinical PhD annual fees shown at this page 

Eligibility: Home and Overseas applicants

Application deadline: Open until a suitable candidate is found

Reference number: 2026/JSD/Ai

Project description

Non-technical skills, including communication, professionalism, situational awareness, and teamwork, are central to safe and effective clinical practice. However, students’ training, development and assessment in these domains remain challenging, often relying on subjective, episodic observation that limits consistency and scalability.

This PhD project aims to design and evaluate an artificial intelligence AI-driven framework to enhance the assessment and training of human factors in clinical education. Grounded in Situated Learning Theory and Self-Regulated Learning (SRL), the project reconceptualises learner performance not as isolated skills, but as participation in socially and contextually embedded clinical interactions.Using recordings of authentic and simulated clinical encounters, the research will apply machine learning (ML) and natural language processing (NLP) to analyse how learners engage in clinical practice. The system will identify behavioural markers such as turn-taking, responsiveness to patient concerns, role positioning, and teamwork dynamics. In doing so, it will determine where learners sit along a continuum from peripheral participation (novice) to full participation (competent practitioner).Building on this situated perspective, the project will develop AI-generated feedback designed to support self-regulated learning. By making behavioural patterns visible, the system will enable learners to monitor, evaluate, and adapt their performance. Feedback will be aligned with the SRL cycle (supporting planning - forethought, performance monitoring, and reflective evaluation) thereby promoting deeper metacognitive awareness and sustained improvement.

A mixed-methods approach will be used to validate AI outputs quantitatively and explore learner and educator experiences qualitatively. The research will also examine how AI-informed feedback can enable adaptive, personalised learning pathways and inform targeted simulation-based interventions.Ethical considerations, including data privacy, bias, transparency, and responsible AI deployment, will be integral to the study design.This project offers a novel contribution by integrating context-sensitive analysis of clinical participation (Situated Learning) with AI-supported metacognitive development (Self-Regulated Learning). The outcomes will inform the design of intelligent educational systems with implications for clinical training, simulation, and patient safety.

Research training

• Exposure to clinical workflows and simulation-based education
• Artificial intelligence and machine learning skills (classification, model development, evaluation)
• Natural language processing for analysing clinical communication and interaction
• Learning analytics and educational data science
• Application of learning theory (Situated Learning, Self-Regulated Learning, human factors)
• Mixed-methods research design (quantitative validation and qualitative inquiry)
• Behavioural analysis of clinical interaction (video/audio coding, discourse analysis)
• Design of AI-driven feedback systems for education
• Simulation-based education design and evaluation
• Ethical AI and governance (bias, fairness, transparency, consent)
• Academic writing, publication, and interdisciplinary dissemination

These skills will position the candidate for careers in academia, clinical education leadership, health AI innovation, and digital health.

Person specification

Any applicants with interest in Dental Education and Learning Technology research. To view entry requirements and further information, see the Dental and Health Sciences Research MPhil/PhD. 

Next steps

Applicants are strongly encouraged to discuss the project with the first supervisor prior to submitting an application. 

Please apply online at apply.kcl.ac.uk following these steps:

1. Register a new account/login
2. Once logged in, select Create a new application
3. Enter ‘Dental and Health Sciences Research MPhil/PhD (Full-time)' under Choose a programme. 
4. Select your preferred start date.
5. Include the project reference number 2026/JSD/Ai in the 'Research proposal' section of the application.

Contact for further information

Dr Jonathan San Diego (jonathan.p.san_diego@kcl.ac.uk)

For further information on education research, see Centre for Oral, Clinical & Translational Sciences

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