Nick Huxley

Research interests

Investigating how AI tutors shape mathematical reasoning, dialogue, and equity in primary education.

Nick Huxley is a PhD researcher in Education within the University of Brighton’s Doctoral College and the School of Education, Sport and Health. His research examines how large language model (LLM)-mediated tutoring is reshaping teaching and learning in primary mathematics.

His current doctoral work focuses on a question at the heart of AI in education: who is doing the thinking? More specifically, he investigates how AI tutors may influence the dialogic conditions through which children develop mathematical reasoning. Rather than asking only whether AI 'improves outcomes', his research explores whether these systems sustain explanation, justification, and productive struggle, or whether they risk closing down reasoning too quickly through overly fluent or authoritative support.

The project brings together dialogic learning theory, mathematics education, postdigital perspectives, and concerns around equity and digital justice. It pays particular attention to disadvantaged learners and to the possibility that AI may widen inequalities if some pupils are better positioned than others to use it productively. Across this work, Nick is interested in how AI can be designed and implemented to support active, teacher-centred, equitable learning in real classrooms rather than passive task completion.

More broadly, Nick’s research interests include:

• AI in education, especially LLM-mediated tutoring
• Dialogic pedagogy and dialogic space
• Mathematical reasoning and explanation in primary classrooms
• Human–AI interaction in teaching and learning
• Cognitive offloading, cognitive extension, and learner agency
• Digital equity, educational inequality, and the AI divide
• Evidence-informed design and evaluation of educational technology

Nick’s knowledge exchange work aims to build dialogue between schools, researchers, EdTech developers, and policy communities around the educational implications of AI.

He is particularly interested in how AI tools can be developed and evaluated in ways that align with classroom pedagogy rather than simply with efficiency or automation. His work therefore focuses on the conditions under which AI might support teacher-led, dialogically rich learning, especially in primary mathematics, and the possible risks that arise when AI systems offer answers too quickly or narrow the space for reasoning.

Working with schools and wider partners, Nick seeks to contribute to conversations about the ethical, pedagogical, and practical integration of AI in education, including issues of access, safeguarding, data protection, infrastructure, and teacher agency. His previous 12-year career in telecommunications and technology supports this work by bringing additional expertise in systems design, networking, security, reliability, and implementation realities.

He is especially keen to support knowledge exchange that connects classroom practice with wider debates about AI policy, digital inclusion, and the future of learning.