environment that enables students to explore molecular and structural biology in realistic 3D contexts, guided by AI feedback and gamified challenges.

Using Unity or Unreal Engine, the student will create immersive models of key molecular processes, such as receptor–ligand binding, enzyme catalysis, protein folding, and oncogenic mutation, based on the Protein Data Bank. AI technologies, including natural-language feedback agents, adaptive reinforcement learning, and learning analytics, will personalise the learner’s pathway and provide real-time formative assessment. Gamification principles such as narrative framing, level progression, and reward systems will sustain engagement and foster problem-solving.

Educational design will be grounded in constructivist learning theory, cognitive load management, and self-determination theory to ensure that technological innovation enhances learning. The evaluation strategy will use a mixed-methods approach, combining pre- and post-tests, behavioural analytics, and interviews to measure conceptual gains, spatial reasoning, and engagement. Comparisons will be made with conventional teaching and non-adaptive digital approaches to assess impact and scalability.

Expected outcomes include a functional prototype of an AI-driven immersive gamified platform, empirical data on its educational value, and design principles for curriculum integration. The successful candidate will develop interdisciplinary expertise spanning molecular biology, educational technology, and AI, gaining experience in software development, learning design and pedagogical research. The project offers opportunities for collaboration with biomedical educators and technologists, and for publication and conference presentation.

environment that enables students to explore molecular and structural biology in realistic 3D contexts, guided by AI feedback and gamified challenges.

Using Unity or Unreal Engine, the student will create immersive models of key molecular processes, such as receptor–ligand binding, enzyme catalysis, protein folding, and oncogenic mutation, based on the Protein Data Bank. AI technologies, including natural-language feedback agents, adaptive reinforcement learning, and learning analytics, will personalise the learner’s pathway and provide real-time formative assessment. Gamification principles such as narrative framing, level progression, and reward systems will sustain engagement and foster problem-solving.

Educational design will be grounded in constructivist learning theory, cognitive load management, and self-determination theory to ensure that technological innovation enhances learning. The evaluation strategy will use a mixed-methods approach, combining pre- and post-tests, behavioural analytics, and interviews to measure conceptual gains, spatial reasoning, and engagement. Comparisons will be made with conventional teaching and non-adaptive digital approaches to assess impact and scalability.

Expected outcomes include a functional prototype of an AI-driven immersive gamified platform, empirical data on its educational value, and design principles for curriculum integration. The successful candidate will develop interdisciplinary expertise spanning molecular biology, educational technology, and AI, gaining experience in software development, learning design and pedagogical research. The project offers opportunities for collaboration with biomedical educators and technologists, and for publication and conference presentation.