Good Teaching Stories

Episode 2: The Scratchies Project

David Christie

Led by David Christie (Department of Economics), the Scratchies Project flips traditional tutorials into lively, group‑based learning spaces. Vulnerable online multiple‑choice quizzes—easily gamed by generative AI—were replaced with in‑tutorial scratch‑card quizzes (instant feedback assessment technique). Students work collaboratively, scratching answers until they reach the correct step, which supports multi‑step algebraic reasoning, peer feedback and tutor facilitation rather than didactic delivery.

Early results are promising: stronger student engagement, higher end‑of‑semester satisfaction and a condensed grade distribution with fewer fails and marginal passes. Implementation is underpinned by a tutor observation programme and a scalable question‑bank workflow that leverages generative AI to draft items, converts them to LaTeX and publishes via Overleaf. While the approach demanded significant design effort, it has revitalised tutorials, reduced bimodal outcomes and sharpened collaborative problem‑solving—offering a practical, sustainable model for enhancing formative assessment across economics teaching.

Scratchie Example

If students find the star – full 4 points; if the first scratched area reveals no star, they keep scratching until they find it. For each ‘mis-scratch’ they will lose a point. If they have to reveal all until they find the star, typically this equals 0 points. Thus: 1 scratch = 4 points. 2 scratch = 3 points. 3 scratch = 2 points. 4 scratch = 0 points.

Card identification code

This ‘code’ indicates the answer key for this particular card
i.e. 1-A, 2-C, 3-D, 4-A, etc.

Episode 1: Inquiry‑based AI & Design Thinking in the BUSA30000 Capstone

Eugene Skewes and A/Prof Andre Sammartino

Discover how Business Judgement turned the AI debate into a practical learning advantage. Students used industry tools, notably Retrieval-Augmented Generation (RAG) via NotebookLM, to analyse simulated organisational data, run design-thinking sprints and interrogate AI outputs. Rather than banning AI, the subject reframed assessment. AI-generated case contexts, in-class workshops and an oral interview required teams to explain their choices and critically evaluate the reliability of the tools and their outputs.

Industry consultations helped shape the project’s approach, including the selection of appropriate tools and learning objectives. Industry representatives emphasised the value of graduates gaining relevant experience with AI tools while learning to think critically about their outputs. Evaluation through pre- and post-surveys, engagement metrics and interviews showed increased student confidence, sharper critical appraisal of AI and improved engagement. The initiative demystified AI, encouraged reflective use and developed employable skills valued by students and employers.

Curious about the classroom mechanics or assessment design? The project provides activity guides, assessment exemplars and evaluation findings to support broader curricular adoption across commerce subjects.

Links to the various resources used in the project:

Student tools

• NotebookLM: RAG learning
• Statista AI: Industry RAG, credible industry research.
• Copilot: Prompt development, credible macro research
• Miro AI:  Synthesizing problem statements, concept and prototyping.
• Figma AI: Prototyping

Tools for resource development

• Gama: Website, slides and images
• Midjourney: Images
• HeyGen: Avatar videos
• Mockaroo: Synthetic Data (Very Impressive!)
• ChatGPT: Content creation and prompt generation