| Activity | Square | AI Literacy Tags |
|---|
| Build a personal AI learning roadmap with goals, tools to try, methods to track success, and reflection checkpoints. | Create | Self-Knowledge, Career Competencies |
| Students build a functioning low-code AI prototype using tools like Peltarion, Zapier, or Scratch + Teachable Machine. | Design | Hardware & Software, Problem Solving |
| Design a simple concept for an AI system that addresses a specific campus or social need (e.g., sustainability dashboard). | Assemble | Problem Solving, Career Competencies |
| Build a slide deck or infographic explaining how an AI system like ChatGPT works, using both AI and peer-reviewed info. | Generate | Hardware & Software, Communication & Collaboration |
| Students complete an AI learning journal that includes entries reflecting on tool use, decision-making strategies, and peer feedback. | Reflect | Self-Knowledge, Problem Solving |
| Analyze the differences in process and output when using two different prompt strategies for the same coding task in the same AI tool. | Judge | Problem Solving, Career Competencies |
| Debate whether deep learning should be used in high-stakes fields like medicine, using AI-generated arguments for and against. | Determine | Safety, Career Competencies |
| Students critique the accuracy of an AI-generated explanation of a scientific process by cross-referencing with textbooks and databases. | Check | Problem Solving, Information & Data Literacy |
| Students keep a debugging log with AI assistant support and reflect on decision points. | Deconstruct | Problem Solving, Career Competencies |
| Students experiment with prompt engineering to improve model output and document what worked best. | Integrate | Information & Data Literacy, Problem Solving |
| Compare model structures (decision tree vs. neural net) by visualizing workflows and dissecting their logic. | Differentiate | Hardware & Software, Career Competencies |
| Given AI-generated data outputs, students identify incorrect or hallucinated data and mark discrepancies. | Select | Problem Solving, Information & Data Literacy |
| Students create a personalized "AI Toolbox" listing tools best suited to their learning or research style and explain their rationale. | Use | Hardware & Software, Career Competencies |
| Build a basic predictive model using Google AutoML or a no-code AI tool and test it with a small data set. | Carry Out | Hardware & Software, Career Competencies |
| Given different types of AI algorithms, students match them with appropriate real-world problems and justify their choices. | Provide | Problem Solving, Career Competencies |
| Students identify AI tools suited for specific data sets and justify their tool choice by referencing AI technical documentation. | Respond | Hardware & Software, Career Competencies |
| After using different AI tools, students reflect on when they felt most in control and least in control, and chart their confidence over time. | Predict | Safety, Problem Solving |
| Students analyze steps taken by an AI assistant to solve a math or data science problem, then narrate those steps as a screencast. | Clarify | Problem Solving, Communication & Collaboration |
| Given several AI outputs, students work in pairs to group them based on the type of learning used (e.g., classification vs. clustering). | Classify | Information & Data Literacy, Problem Solving |
| Students annotate a diagram of a machine learning workflow generated by an AI diagramming tool (like diagrams.net + ChatGPT). | Summarize | Hardware & Software, Career Competencies |
| 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 | Career Competencies, Problem Solving |
| 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 | Career Competencies, Communication & Collaboration |
| 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 | Career Competencies, Communication & Collaboration |