Teaching with and about AI

Teaching approaches that help students experiment with AI, judge its output, and learn when an attempt fails.

How can students learn with AI when the technology and its limits keep changing? We develop teaching approaches in which students experiment with AI and explain how they judged its output.

Our Teaching the Unknown framework grew from a thirteen-week undergraduate unit with 23 students across three subject areas. We analysed student reflections and classroom observations. The study also used AI interaction logs. The framework makes room for students to try approaches that may fail. Assessment asks them to explain what they tried and learned, so an unsuccessful AI result can still support learning.

We apply these ideas in workshops and lectures. Our teaching resources give students practice with AI output as material for interpretation. Students interpret the output and explain which parts they accept or change. Teachers can use this approach to make experimentation safer while keeping responsibility for judgement with the learner.

Ballsun-Stanton, B. (2026, May 19). Week 11: Words Gooding, The Triumph of the Humanities [Lecture]. INTS1301 Technology and Society, Macquarie University. https://techethicsai.au/course/INTS1301-week11/

Presentation

Why it matters

The AI cannot judge whether what it produced is any good. That capacity is what your humanities training has been cultivating, slowly, for years before this lecture ever happened.

Source: Slide 10

Recording

Ballsun-Stanton, B. (2026, April 21). Week 7: A History of AI Since the 1950s [Lecture]. INTS1301 Technology and Society, Macquarie University. https://techethicsai.au/course/INTS1301-week07/

Presentation

Why it matters

AI that replaces the people it serves tends to get rejected; AI that augments them tends to persist. Who benefits from a working system, and who loses, decides which AI survives deployment.

Source: The Navy shelved it

Recording

Ballsun-Stanton, B., & Torrington, J. (2025, June 3). Teaching the Unknown: A Pedagogical Framework for Teaching With and About AI. Zenodo. https://doi.org/10.5281/ZENODO.15583013

Preprint

Why it matters

Make it safe for students to take risks and play with AI. When assessment values reflection over successful output, failure becomes part of learning.

Read abstract

Generative artificial intelligence (AI) has disrupted education systems worldwide. This disruption necessitates pedagogical approaches that embrace uncertainty while developing student agency. We examined how decoupling task success from assessment outcomes created environments where students developed critical AI literacy through structured risk-taking. Drawing on Transformative Learning Theory and Rumsfeld's epistemological matrix as interpretive frameworks, we analysed an experimental undergraduate AI unit across three disciplinary streams: Ancient History, Philosophy, and Politics and International Relations ($N=23$). Data included student reflections, classroom observations, and AI interaction logs collected over a 13-week semester. Our pedagogical framework operationalised four interconnected pillars: risk-embracing assessment structures, intentional classroom culture development, systematic navigation of technological uncertainty, and facilitation of transformative learning experiences. This paper presents the implications of these pillars for 1) educational theory, where productive failure serves as an effective pedagogical strategy; 2) educator praxis, viewing AI as a textual technology that extends the capabilities of the humanities; and 3) implications for the university teaching context, where AI-enabled teaching should focus on reflection and process rather than demonstratable competencies.

Prompts

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