Ballsun-Stanton, B. (2026, August 11). The Ethics of Investigating Digital Worlds [Lecture]. INTS1302 Navigating Digital Worlds, Macquarie University. https://techethicsai.au/course/INTS1302-week03/week03.html#/
Presentation
Techniques and Ethics of AI
Dr Brian Ballsun-Stanton and Dr Jodie Torrington teach teachers, students, and researchers to work with generative AI without handing it their judgement. Who directs the outcome when a person works with AI, the person or the tool? That question runs through their research programs, and they call it the AI-Locus of Control. They show that a person can steer what a tool produces, sentence by sentence, and keep the result as their own.
Brian Ballsun-Stanton took his doctorate in philosophy, on what makes something count as data. He now leads generative AI work for the Faculty of Arts at Macquarie University, and he wrote the guidance its researchers use. Jodie Torrington researches how people learn to direct AI and judge what it gives them. She brings more than two decades in primary classrooms to that work, and she now builds professional learning for teachers. Together their work reaches from a university’s research policy to the classroom, and they support teachers, researchers, and anyone who works with AI.
My AI Journey - The Drivers’ Choice. Jodie Torrington and Brian Ballsun-Stanton on choosing to drive the tool instead of riding along. 4 minutes 42 seconds.
Research
AI-Locus of Control
When a person works with AI, who directs the outcome, the person or the tool? We study where people place that control, and how teaching moves it back to the person. We have watched people shift from accepting whatever the tool produced to steering it, and we are now following that change over time.
AI Prompting Techniques
How does a person get from a blank chat box to work they can stand behind? We build ways of prompting that begin with whether AI suits the task and end with whether the result reflects the person’s own knowledge. We have seen that the value lies in the judgements a person makes, even when the AI is wrong, and we now teach that to educators.
The Future of Education
If AI can produce an assignment, what should that assignment assess? We argue for teaching that rewards the process a student works through, with room to try something and learn when it fails. We have seen a subject succeed by dwelling on process, and we are turning that into assessment practice others can use.
AI in Research: Methods and Policy
How can researchers use AI in work that others can check? We set out what to record about the AI’s part in the work, so the researcher stays accountable for every decision. Our guidance is now part of Macquarie University’s ethics processes, and we are developing methods that keep the evidence beside every result.
Teaching with and about AI
How do people teach with a tool whose limits keep changing? We design teaching that gives people room to experiment, then asks them to explain how they judged what came back. We have found that a failed attempt still teaches when assessment asks what the person learned, and we build that into whole subjects.
About Us
Courses & Professional Learning
Ballsun-Stanton, B. (2026, August 4). AI Multiplies Judgement: Using AI ethically and well in research practice. https://doi.org/10.5281/ZENODO.21781864
Presentation
Read abstract
AI multiplies a researcher's judgement without caring about the sign, so the discipline built around the model matters more than the model. These are the slides from a one-hour lecture to the South Australian Research and Development Institute on using generative AI well in research practice. It opens on workslop, Niederhoffer and colleagues' term for AI output that looks polished while lacking the substance to advance the task (Harvard Business Review, 2025), and asks why capable people send it, since the reason they give is being stretched too thin. From there it sets out the prompting mechanisms that make a model interrogate your thinking rather than flatter it, and closes the first half on a live test of Macquarie's enterprise Copilot, which critiqued a bounded document competently and bluffed on an unbounded question whose sources it never looked at.
The second half works through Macquarie University's 2023 Guidance Note on generative AI in research and the 2025 disclosure and documentation Checklist, covering what each was written to protect, how disclosure scales from a footnote to an appendix, and why AI use is acknowledged rather than cited, since a model cannot be fired and so cannot take the blame. A registered report written under both supplies the worked example, with a use-of-AI declaration spanning roughly 120 sessions of drafting, simulation coding, and citation checking, and with the survey responses sealed from the models on the same terms as from the authors. The lecture ends on what makes the practice ordinary in a team, which is annotated conversations, shared prompts, and leaders going first.
Ballsun-Stanton, B. (2026, May 26). Week 12: Civilization Under the Influence of AI, Five Possible Futures [Lecture]. INTS1301 Technology and Society, Macquarie University. https://techethicsai.au/course/INTS1301-week12/
Presentation
Practical Resources
Ballsun-Stanton, B., Torrington, J., & Waked, L. (2026, September 4). AI Prompting Framework [Poster]. https://doi.org/10.5281/ZENODO.22290781
Poster
Read abstract
The AI Prompting Framework is a structured, six-stage guide (numbered 0 to 5) for working with generative AI in a way that keeps the user's knowledge, expertise, and judgment at the centre of the process. Rather than treating AI as a source of ready-made answers, it positions the tool as a collaborator whose output the user directs, scrutinises, and ultimately owns.
The framework moves from an initial decision about whether AI is appropriate for the task at hand (Step 0), through the preparation of a clear, well-contextualised first input with a built-in self-check (Step 1), to reviewing that first response (Step 2), interacting iteratively while retaining control of the exchange (Step 3), reflecting on whether the final output genuinely reflects the user's own knowledge (Step 4), and finally documenting effective prompts and being transparent about AI use (Step 5). Each stage is paired with a reflective question, such as "Have I provided enough context?" and "Am I happy to put my name to this?", that prompts users to pause and exercise judgment throughout.
Designed to be general-purpose, the framework applies across disciplines and professional contexts wherever people want to use generative AI deliberately, critically, and accountably.
Ballsun-Stanton, B., & Torrington, J. (2026, September 4). AI-Locus of Control Continuum [Graphic]. Zenodo. https://doi.org/10.5281/zenodo.17823627
Diagram
Read abstract
Our proposed AI-Locus of Control framework has emerged from our recent longitudinal study on the impact of explicit teaching about AI literacy on students' agency and control beliefs in human-AI interactions (https://osf.io/preprints/edarxiv/6mke5_v3). From our analysis, we identified four key dimensions or tensions in AI-Locus of Control:
AGENCY: Does the AI have agency, or does the human retain it? Students with external LOC felt they had to either accept AI output or reject the entire idea of using AI. Students with internal LOC realised they could steer, adjust, and control output on a sentence-by-sentence basis.
EXPERTISE: Is AI better than human, or an input to human judgement? Some students initially treated AI as an expert that couldn't be questioned. Through the unit, they learned that their own prompting and setup was crucial to producing output they felt comfortable claiming as their own.
EMBODIMENT: Is there a mind behind the mirror, or is it a tool to be used? Early in the semester, students engaged with AI as if there was an intelligence behind it. By the end, they understood they held sole responsibility for populating the context window with facts and scaffolding.
PRAXIS: Automation versus augmentation? Some students used AI to automate their assignments entirely. Others learned to dip in and out, using it for partial ideation, sense-checking, and editing - augmenting rather than automating their process.
These four tensions give us a richer understanding of what AI-LOC means in practice.
Ballsun-Stanton, B., Johnston, V. H., Jensen, H. S., Kjeldsen, C. K., & Thøgersen, J. (2026, April 15). LLM Document Discovery (Version Container) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.19591245
Software
Read abstract
LLM Document Discovery v0.2.0 — Apptainer Container Pipeline
Reproducible pipeline for classifying historical web documents (1996–2005) using large language models. Extracts linguistic and structural features from children's web
content archived by the Internet Archive, producing a structured SQLite database of classifications with supporting blockquote evidence.
This release adds a containerised execution pipeline using Apptainer/Singularity, enabling reproducible deployment on both local GPUs and HPC clusters (NCI Gadi).
What's new in v0.2.0:
Apptainer container: wrapping vLLM v0.19.0 + llm-discovery for fully offline, reproducible execution on HPC compute nodes
Single-command deployment: deploy assembles the data directory, syncs to HPC, and submits the PBS job
Multi-model support: tested with google/gemma-4-E4B-it (local RTX 4090), google/gemma-4-31B-it (Gadi V100), and openai/gpt-oss-120b (Gadi H200)
Prompt engineering: rationale-first instruction format achieving 98% structured output compliance (up from <1%)
Crash-safe resumability: killed containers resume from the last completed document–category pair
CLI commands: build, init, download-model, deploy, status –watch, retrieve, for end-to-end HPC lifecycle management
Offline tokeniser support: tiktoken vocab files baked into the container for OpenAI gpt-oss model family
Associated paper:
Johnston, V.H., Ballsun-Stanton, B., Jensen, H.S., Kjelsen, C.K., & Thøgersen, J. (2026). Exploring the Archived Web through AI-Assisted Document Discovery. https://github.com/WEB-CHILD/exploring-the-archived-web-through-ai-assisted-document-discovery
Work with us
Get in touch to discuss an AI workshop or professional learning for your school or organisation. You can also contact us about research collaboration or AI policy.