Jacek Robert Kszczot

Jacek Robert Kszczot is a PhD researcher in the School of Computing and Creative Industries at Leeds Trinity University, supervised by Associate Professor Dr Xin Lu and Professor Dr Martin Barwood.

He holds an MSc in Data Science and Artificial Intelligence, awarded with Distinction, and a BA (Hons) in Media Arts and Communication, both from Leeds Trinity University.

His doctoral research investigates how long-document Transformer models, such as Longformer and LED, can be adapted to evaluate AI policy effectiveness in higher education. The study addresses a recognised gap in the field: whilst universities increasingly develop AI governance frameworks, systematic methods for assessing whether these policies achieve their intended outcomes remain limited.

His work combines computational approaches with qualitative insights, drawing on pedagogical, ethical, and governance perspectives to build an evidence-based evaluation framework.

This research direction emerged from his MSc thesis project, PolicyCraft, a full-stack web platform that uses natural language processing and machine learning to analyse university AI policy documents. The tool categorises policies against established frameworks from UNESCO, JISC, and BERA, generating actionable recommendations for institutional improvement.

PolicyCraft demonstrated that computational methods could bridge the gap between policy design and practical implementation-a finding that shaped his commitment to pursuing doctoral study.

Beyond his academic work, Jacek contributes to multimodal AI development as an AI Trainer, where he provides expert human feedback to improve reasoning, safety, and cross-modal capabilities in large language models. This practical engagement with frontier AI systems informs his research perspective and keeps his work grounded in real-world applications.

His broader research interests include AI ethics and governance, the impact of artificial intelligence on university education, and the relationship between nutrition and learning outcomes among young people.

• AI policy evaluation in higher education
• Natural language processing and deep learning (Transformer architectures)
• AI ethics and governance
• Impact of AI on university education
• Nutrition and its influence on learning and development