Techniques and Ethics of AI
  • Home
  • Research
    • Research overview
    • AI-Locus of Control
    • AI Prompting Techniques
    • The Future of Education
    • AI in Research: Methods and Policy
    • Teaching with and about AI
  • Courses & Professional Learning
  • Practical Resources
  • All Work
  • About Us

Practical Resources

Ballsun-Stanton, B., Torrington, J., & Waked, L. (2026, September 4). AI Prompting Framework [Poster]. https://doi.org/10.5281/ZENODO.22290781

Poster

Why it matters

Use six steps to decide whether AI suits the task, give it useful context, stay in control of the conversation, check the result against your expertise, and keep a transparent record.

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

Why it matters

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.

Source: EXPERTISE

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

Why it matters

Our method lets historians and other researchers work from a problem-oriented approach where the research questions lead the search for relevant source material.

Source: article notebook, Markdown cell 8

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

Source notebook
Ballsun-Stanton, B., & Torrington, J. (2026, February 6). Generative AI for Educators: AI Literacy & Prompting Skills. Coursera. https://www.coursera.org/learn/mqfoa-generative-ai-literacy-and-prompting-skills-for-educators/

Course

Why it matters

Think of a large language model as the engine. An AI platform adds the controls and dashboard that let teachers and students use it.

University of Giessen, ZAD, Ballsun-Stanton, B., & Uhl, C. (2025, October 23). AI Summer Camp 2025 - Basic Course. Zenodo. https://doi.org/10.5281/ZENODO.17422422

Course

Why it matters

Shape AI’s style without mistaking confidence for accuracy. You practise checking its quotations against the original source.

Course site
Ballsun-Stanton, B., & Torrington, J. (2025, October 17). Effective AI Prompting Strategies from the Humanities [Poster]. https://doi.org/10.5281/ZENODO.17373463

Poster

Why it matters

Use examples. AI is effective at imitation, and if you give it a few examples of what you want, it can copy their form.

Source: p. 1

Read abstract

Poster to support successful prompting by teachers and researchers.

Good AI prompts are essential to predictably and consistently getting a desired output.There are no single phrases that work across all situations - AI will simply amplify the user’s capabilities, creativity, judgement and discernment.

These effective prompting strategies will help develop your own skills and strategies with AI prompting.

Text:

Start with a goal: what is the single thing you are trying to achieve?

Add context and details: requirements, instructions and parameters. AI needs clear scaffolding so it’s set up for success. Don’t ask it to search for facts - it’s not a search engine.

State your role, so it knows what register to use. The use of words influences its sample and response. Tell it what you want and who the output will be for.

Work as thinking partners: Tell the AI to “Ask me one critical question at a time until we achieve our goal”

Check for clarity: Include a final sentence for the AI to scaffold the task to make sure what you said is what you meant. “To begin, please functionally decompose the task.”

Use examples. AI is effective at imitation, and if you give it a few examples of what you want, it can copy their form.

Longer prompts, rather than shorter: Make sure that you provide all the details it needs. Edit your prompts if errors occur, or start over.

Clear expectations: At the end of each prompt, end with a single, specific task.

Have a real conversation: Go back and forth a few times before you reach your answer. Asking it to ask you for clarification highlights blank spots in your thinking.(Also, you can prompt it to help you design prompts…)

Avoid “AI-slop” - Would you be comfortable putting your name to this? Have you interacted using some effort? Make sure you know enough to be able to validate the output.

Ballsun-Stanton, B., Torrington, J., Laurence, R., Khalid, M., & Atkin, A. (2025, June 30). Exploring Student and Faculty use of Generative AI and Large Language Models in Classrooms and Research, ARTS3500 Classroom Transcripts and Assessments Semester 2 2024 (Version 1.0) [Dataset]. ADA Dataverse. https://doi.org/10.26193/CDCZA7

Dataset

Why it matters

Deidentified classroom transcripts and student work allow researchers to examine how students used and understood generative AI across three disciplines and over a semester.

Read abstract

This dataset contains educational materials from an AI-focused course, including: - Student assessments from AI History, AI Philosophy, and AI Politics streams - Class transcripts from various sessions - Course materials, schedules, and rubrics

Torrington, J., & Ballsun-Stanton, B. (2025, June 10). Generative AI for Teacher Planning and Resources. https://techethicsai.au/course/GenAI-TeachersLearningHub-2025/

Course

Why it matters

Learn where AI can save preparation time, then use a six-stage process to turn the AI's first draft into a resource you can stand behind.

Superseded by Generative AI for Educators: AI Literacy & Prompting Skills
Ballsun-Stanton, B., & Smith, S. (2025, April 30). Generative AI in Research: Disclosure & Documentation Guidance / Checklist. Macquarie University. https://doi.org/10.5281/ZENODO.15307492

Guidance

Why it matters

Keep dated records of your prompts, outputs and later edits so other researchers can verify what you did. Match the disclosure to how extensively AI contributed.

Read abstract

The use of generative AI in research needs to align with the principles and responsibilities outlined in the Australian Code for the Responsible Conduct of Research 2018. Guidelines about the appropriate disclosure and documentation to accompany the use of Generative AI in research also need to recognise the practical considerations of current or innovative research practices. An approach combining these elements is detailed in this document for Macquarie University researchers.

Ballsun-Stanton, B., Smith, S. M., & White, K. (with Anson, D., Connor, M., Cornish, J., D’Arcens, L., Kim, J., Singh, A., & Todd, V.). (2023, October 22). Guidance Note: Using Generative Artificial Intelligence in Research. Macquarie University. https://doi.org/10.5281/ZENODO.10851623

Guidance

Read abstract

This is the university policy/guidance note for the use of Generative AI and LLMs at Macquarie University, Australia. The original source is https://policies.mq.edu.au/download.php?associated=1&id=768&version=1

Here is the summary:

Generative AI (Artificial Intelligence, Large Language Models) offers the unprecedented ability tomanipulate and generate text and media in response to arbitrary instructions. These new capabilities offeropportunities and risks to researchers. This document will discuss responsible use, risk mitigation, andappropriate use of these tools. The technological landscape changes quickly and new tools are releasedalmost weekly – this guide offers general advice which should be applied thoughtfully.

Regardless of any tools or technologies used, now or in the future, everyone at Macquarie University isresponsible for ensuring their research meets the expectations of the Australian Code for the ResponsibleConduct of Research and the Macquarie University Code for the Responsible Conduct of Research(2018).

Generative AI must be used with caution, and its use is currently inappropriate in some researchprocesses because Generative AI services, including ChatGPT:• cannot meet the requirements for authorship• can create authoritative-sounding outputs that may be incorrect, incomplete, or biased• could inappropriately capture sensitive data (including, but not limited, to personal information).

Researchers must not use Generative AI:• to perform peer review activities• to generate substantive content of research outputs, including HDR theses• for writing the critical components of human ethics, animal ethics, or biosafety applications.

Researchers must exercise care in the use of Generative AI in other aspects of their research and should:i. only do so with the written agreement of their research collaborators (& HDR supervisors)ii. review and consider the terms of service/license of the platforms used and any models usediii. consider the current issues and understandings around copyright and intellectual propertyiv. mitigate risks around the insecure storage or unauthorised re-use of sensitive datav. exert oversight and control when using the technologyvi. carefully and critically review the output and results created by Generative AIvii. take responsibility for the integrity of the content altered or created using Generative AIviii. disclose the use of Generative AI to potential publishers and in disseminated research outputsix. read and follow the policies of publishers and funders regarding the use of Generative AI.

Ballsun-Stanton, B. (2023, June 6). Large Language Models Workshop. OSF. https://doi.org/10.17605/OSF.IO/RD24Y

Workshop

Why it matters

Prompting is a skill, and there's no mind behind the mirror. Develop your own rules of thumb.

Source: Open Discussion and further Demos

Read abstract

This two-hour workshop provides a comprehensive introduction to the world of Large Language Models (LLMs), focusing on the recent advancements in Generative AI. Participants will gain insights into the development and functionality of prominent LLMs such as Bing Chat and ChatGPT. The workshop will delve into the concept of LLMs as "calculators for words," highlighting their potential to revolutionize ways of working and learning.

The session will explore the principles of Prompt Engineering and Transactional Prompting, demonstrating how consistent prompts can yield reliable and reproducible results. Participants will also learn about the practical applications of LLMs, including editing and proofreading papers, generating technical documentation, recipe ideation, and more.

The workshop emphasizes the importance of understanding the terms of use and the responsibilities that come with using these powerful AI tools. By the end of the session, participants will be equipped with the knowledge and skills to effectively use LLMs in various contexts, guided by the mantra that a LLM is "Always confident and usually correct."

A recording of one workshop is available on Youtube: https://www.youtube.com/watch?v=c3_P7fVjiK8

 
  • Contact

  • RSS

  • Privacy