Mark Grattan

Profile

I am a full-time postgraduate researcher in the School of Education at the University of Leeds, having successfully transferred to full doctoral status in September 2026. My PhD examines how undergraduate music production students experience generative artificial intelligence (GenAI) in assessment, including decisions to use or not use it, questions around disclosure, and uncertainty about what is expected. I am particularly interested in how students explain their creative process and demonstrate what they have learned, and how that learning is recognised within formal assessment.

I bring over three decades of professional experience as an internationally recognised electronic music artist, producer and music educator. This background has given me a close understanding of music production practice and how people develop creative and technical skills through both formal education and independent learning.

My academic pathway has been non-traditional. After returning to higher education as a mature student, I completed a BA (Hons) in Teaching and Education (First Class Honours). My undergraduate dissertation explored how music practitioners support children's learning and engagement, particularly through informal approaches, and the barriers created by unequal access to music education. I subsequently completed an MA Education (Distinction), with a dissertation examining the democratising potential of AI for music education in England, with a particular interest in social justice, access and participation.

Alongside my academic work, I founded Safety in Music CIC, using music-based approaches to support vulnerable groups. My professional and educational experiences continue to shape my research interests, particularly around how students’ knowledge, creative practice and contributions are understood and valued in education.

Research interests

My doctoral research focuses on how learning is evidenced and recognised in higher music production assessment, particularly where generative AI (GenAI) raises questions about authorship, creative process and what counts as legitimate evidence of learning.

The study examines one Level 6 undergraduate music production assessment within a UK higher education provider. It explores how students interpret assessment expectations, make decisions about GenAI use or non-use, and explain their own creative and technical contributions. It also considers how uncertainty around disclosure, authorship and assessment criteria may affect their experiences.

Epistemic injustice provides the main analytical framework for the study. I am interested in whether students’ explanations of their learning and creative decisions are given appropriate credibility and can be understood within assessment. This is particularly relevant to music production, where learning often develops through experimentation, critical listening, digital production work and decisions that may not be fully visible in a finished track.

The research also draws on assessment judgement and feedback literacy, recognition-oriented social justice, and informal-formal music learning. Together, these perspectives help examine how students understand what they are expected to demonstrate and whether assessment allows their learning to be recognised fairly.

Using a qualitative case-study approach, I will examine students’ experiences through semi-structured interviews, supported by assessment documents and contextual interviews with teaching staff.

The research aims to inform clearer assessment guidance around GenAI, authorship and evidence of learning. It also develops the idea of a recognition gap as a working concept for examining where students’ learning may not become visible, understood or accepted as legitimate evidence within assessment.

Areas of expertise

Generative AI in higher education; music production education; assessment and feedback literacy; epistemic injustice; social justice in education; recognition of learning; creative practice and authorship; informal and formal music learning

Student education

Alongside my doctoral research, I work as a teaching assistant in the School of Education at the University of Leeds, supporting teaching and student learning at both undergraduate and masters level.

My teaching interests include AI and education, assessment, social justice, digital education, and creative approaches to learning. I also bring extensive experience of teaching and facilitating learning in professional and community music education settings.

Qualifications

• MA Education (Distinction)
• BA (Hons) Teaching and Education (First Class)

Research groups and institutes

• Postgraduate research
• Imaginative Curriculum Centre