Dr Brian Ballsun-Stanton studies how people and organisations build an effective and ethical relationship with artificial intelligence. He leads the Faculty of Arts’ work on generative AI at Macquarie University. His research program asks whether people give AI control over their work or keep that control themselves, and how they build the judgement to use it well. He calls that question the AI-Locus of Control.
The question grows out of his PhD, which he took in philosophy on the nature of data. His thesis showed that what counts as data is situated in a community rather than fixed for everyone. He is now building the Prompt Grimoire, a tool that teaches close reading and prompting as a single act of judgement.
He wrote Macquarie University’s guidance note on generative AI in research. It now sits inside the university’s ethics and data management processes. He also built a framework that trains researchers to keep AI under active scholarly oversight. That framework won him the 2025 Vice-Chancellor’s Excellence in Research Integrity Award.
With colleagues across his faculty he has changed how more than 2,000 students work with AI in the humanities. They learn that they control what a prompt produces, through the close reading and critical judgement their subjects already ask of them. With Jodie Torrington he built a 26-hour Coursera course that teaches educators to work with generative AI.
In 2025 he and Jodie Torrington, with colleagues in the Faculty of Arts, were Highly Commended in the Vice-Chancellor’s Award for Excellence in Education. In 2024 he was one of ten colleagues named Educational Leader in the Faculty of Arts Learning and Teaching Awards.
The same question reaches well beyond the university, because regulators and professional bodies each face it in their own setting. He has advised UK financial regulators through the Digital Regulation Cooperation Forum. He trained the research ethics committee of the Royal Australian and New Zealand College of Ophthalmologists. More than 1,500 researchers have taken his introduction to large language models, across more than 40 workshops.
He brings more than fifteen years of experience as a data scientist and educator. His technical work runs to natural language processing and social network analysis, including a study of violent extremism drawing on tens of millions of social media posts. He has held chief investigator and equivalent roles on 19 funded grants and projects.
Selected publications
Wang, H.-C., Lai, J., Ballsun-Stanton, B., Van Bergen, P., Waked, L., Green, G., Colenbrander, D., Jones, T., & Robinson-Jones, C. (2026, July 17).
AI-enabled Academic Vocabulary Learning for Multilingual Children. EdArXiv.
https://doi.org/10.35542/osf.io/p8mab_v2
Preprint
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Vocabulary knowledge is fundamental to academic achievement, yet multilingual learners with English as an Additional Language or Dialect (EAL/D) consistently demonstrate vocabulary gaps compared to monolingual peers. Despite evidence supporting explicit vocabulary instruction, scalable and personalised interventions remain limited. This study evaluated an AI-enabled vocabulary learning web application designed to pre-teach academic vocabulary to EAL/D students and support curriculum content learning. The application utilises multimodal large language models (LLMs) for conversational instruction, automatic speech recognition, response evaluation, and text-to-speech production. Thirty-one multilingual learners in Grades 2–4 were recruited in Sydney, Australia. Using a counterbalanced within-subjects design, 24 target science vocabulary words were assessed at pre-test. Participants then received AI teaching for 12 of these words over six at-home sessions across 2 weeks (the remaining 12 served as untrained control), followed by a matched post-test. Over the following 2 weeks, all participants received content lessons on four science topics, two containing pre-trained vocabulary and two that did not. Comprehension was assessed via topic-specific questions. Linear mixed-effects models showed significantly AI-training effect (d = 1.45), compared to the untrained words, and significantly higher comprehension accuracy for lessons paired with trained vocabulary (d = 0.21). Students and parents viewed the application positively, particularly its personalised, multilingual feedback, while highlighting technical reliability as an area for improvement. These findings provide initial evidence that AI-enabled, curriculum-aligned vocabulary pre-teaching can support EAL/D students’ academic vocabulary development and content learning, warranting evaluation at scale in school settings.
