My name is Prashant Bikram Shah, and I am a Lecturer at York St John University, London Campus, as well as an Associate Fellow of the Higher Education Academy (AFHEA). My background spans both academia and industry. I hold an MSc in Computer Science and previously worked as a Senior Computer Engineer, where I gained hands-on experience with biometric identity systems and the implementation of national digital ID projects. This combination of experience helps me stay grounded in real-world delivery while focusing on student learning, academic quality, and research that has practical impact.
In my current role, I am actively involved in postgraduate teaching and student academic support, covering modules such as Machine Learning, Cloud Computing, Big Data, Cybersecurity, and Blockchain. I am passionate about bridging theory and practice in higher education through inclusive pedagogy, cross-disciplinary collaboration, and technology-enhanced learning, ensuring students are well-prepared for both academic and professional success. I contribute to module delivery and coordination, assessment design and marking, seminar and workshop leadership, and dissertation or project supervision. I also focus on creating accessible learning experiences for large cohorts, using clear structure, applied examples, and hands-on activities that build confidence and employability. Alongside this, I’ve been developing practical ways for academics to embed GenAI into curriculum design and classroom activities in a responsible, learning-outcome-led way.
My academic and research interests lie at the intersection of artificial intelligence and healthcare, particularly in Generative Adversarial Networks (GANs), Explainable AI for medical imaging, and interdisciplinary AI applications. I have published research in IEEE conferences and peer-reviewed journals, with contributions in breast cancer diagnostics, sentiment analysis, and retrieval-augmented generation for intelligent systems. Building on this, I’m also developing a broader research direction around agentic and explainable AI for healthcare, including work linked to neurodegenerative disease modelling (for example, Parkinson’s disease) using multimodal signals and clinically meaningful interpretation. I’m interested in research that is technically strong, transparent, and usable in real settings, especially where reliability, ethics, and trust matter as much as performance.
Alongside my teaching and research, I contribute to the wider academic and professional life of the University through quality assurance, student support, and curriculum development work. I regularly take part in assessment setting, marking, moderation, and academic standards processes, and I support module delivery through careful planning, consistent communication, and structured guidance for students. I supervise student projects and dissertations and provide pastoral and academic support where needed, particularly for postgraduate cohorts.
I engage in continuing professional development to strengthen my practice and stay current, including training linked to assessment and feedback, managing students in distress, inclusion and race equity, and academic quality processes. I also take an active interest in responsible uses of generative AI in higher education. In practice, this means supporting colleagues with practical approaches to designing AI-assisted learning activities that align with learning outcomes and academic integrity expectations.
Beyond internal university activity, I maintain a strong connection with industry-relevant skills and applied technology, drawing on my prior engineering background and continuing to develop technical projects that support teaching and research. This includes work with AI-enabled tools (such as retrieval-augmented systems), cloud-based development, and emerging technologies that can enhance student learning and academic delivery.
