Amplifying Student Voice through AI: Textual Analysis of Student Discourse in UK Higher Education
Jan 01, 2025
Stuart Grey
Chapter 15 in: Artificial Intelligence Applications in Higher Education: Theories, Ethics, and Case Studies for Universities
Edited by Helen Crompton and Diane Burke
Routledge, New York, 2025, pp. 247–274
ISBN: 978-1-032-57386-1 (hbk) · 978-1-032-57614-5 (pbk) · 978-1-003-44017-8 (ebk)
This study investigated distinct themes of student "voice" in UK higher education student comments using artificial intelligence (AI). Machine learning models, trained on expert-annotated data, were used to classify large volumes of student feedback—more than 500,000 comments—into these themes. Frequency and sentiment analysis, made possible using machine learning, discerned the relative prominence of each theme and comparative demographic analysis highlighted variations among different student groups.
Identified Themes
- Voice-as-strategy — emphasising the tactical employment of voice
- Voice-as-participation — highlighting students as active contributors to educational discourse
- Voice-as-difference — acknowledging the diverse student experience
- Voice-as-right — underscoring the importance of students' inherent rights in academic conversations
Contribution
This innovative AI-driven approach not only offered a nuanced understanding of student voice but also showcased the transformative potential of AI in academic research within UK higher education. The study demonstrates how machine learning can be applied at scale to understand the complexities of student feedback, providing institutions with actionable insights that would be impossible to derive through manual analysis alone.