| Activity | Square | Discipline |
|---|
| Build a personal AI learning roadmap with goals, tools to try, methods to track success, and reflection checkpoints. | Create | STEM |
| Compile a reflective portfolio that includes AI-assisted writing, self-analysis of process, and ethical stance on using AI in scholarly work. | Create | Humanities |
| Plan and create a multimedia project (e.g., music video or animation) using at least three AI tools in the workflow. | Design | Arts |
| Design a simple concept for an AI system that addresses a specific campus or social need (e.g., sustainability dashboard). | Assemble | STEM |
| Students create self-assessments reflecting on their AI-assisted research habits and evaluate changes they’d make. | Reflect | Humanities |
| Analyze the differences in process and output when using two different prompt strategies for the same coding task in the same AI tool. | Judge | STEM |
| Debate whether deep learning should be used in high-stakes fields like medicine, using AI-generated arguments for and against. | Determine | STEM |
| Students keep a debugging log with AI assistant support and reflect on decision points. | Deconstruct | STEM |
| Analyze own AI usage patterns across tools (text, image, audio) and explain when and why they shift strategy. | Deconstruct | Arts |
| Compare model structures (decision tree vs. neural net) by visualizing workflows and dissecting their logic. | Differentiate | STEM |
| Use multiple AI summarizers on the same article and critique which tool captures nuance best. | Select | Humanities |
| Students create a personalized "AI Toolbox" listing tools best suited to their learning or research style and explain their rationale. | Use | STEM |
| Use two different AI tools with the same prompt for a creative output in your chosen modality and document your experience, thought processes, and preferences during each stage of AI output. | Use | Arts |
| Build a basic predictive model using Google AutoML or a no-code AI tool and test it with a small data set. | Carry Out | STEM |
| Use a transcription AI tool (like Otter.ai) to convert and organize interview audio, then compare it with a human-transcribed version. | Carry Out | Humanities |
| Given different types of AI algorithms, students match them with appropriate real-world problems and justify their choices. | Provide | STEM |
| Students identify AI tools suited for specific data sets and justify their tool choice by referencing AI technical documentation. | Respond | STEM |
| Students track the types of prompts they tend to use across tools and reflect on how that shapes their outcomes. | Predict | Arts |
| Students annotate a diagram of a machine learning workflow generated by an AI diagramming tool (like diagrams.net + ChatGPT). | Summarize | STEM |
| Students complete a guided reflection form on past AI tools they’ve used, what worked well, what confused them, and what they want to improve. | Identify | STEM |
| Students fill in a digital journal after exploring 3 AI tools for creativity (e.g., Midjourney, ChatGPT, Lumen5), noting personal ease/difficulty. | Identify | Arts |
| Students reflect on how their research habits have changed after using AI tools, using a Google Doc worksheet shared for peer feedback. | Identify | Humanities |
| Students record a screen-capture tutorial using AI assistants (like GitHub Copilot or Scribehow) to demonstrate the steps of a workflow relating to their course or intended career field. | Recall | STEM |
| In small groups, students use ChatGPT to look up and compile definitions for types of machine learning (supervised, unsupervised, reinforcement) and create a mini-poster. | Recognize | STEM |
| Students create a mind map in Canva with AI tool integrations, classifying AI tools into categories like generative, assistive, or curatorial. | Recognize | Arts |
| In a timeline-building activity, students label key developments in AI history | List | Humanities |