Generative AI Tools and Data Practices

This unit introduces practical coding tools, foundational data analytics workflows, and synthetic data techniques used in generative AI for instruction. Students will practice tool selection and use, prepare and analyze datasets, and learn methods to create and validate synthetic data while considering privacy and bias, preparing them to design safe, data-informed AI instructional applications in the final unit.

Learning Objectives

• Analyze the capabilities, trade-offs, and classroom suitability of coding tools and development environments (e.g., notebooks, APIs, IDEs) used for building and deploying generative AI models
• Apply data analytics techniques—including data cleaning, exploratory analysis, visualization, and basic statistical summaries—to prepare and evaluate datasets for instructional AI tasks
• Demonstrate methods for generating, validating, and documenting synthetic data (including privacy-preserving approaches and quality checks) for use in instructional design and model training
• Evaluate ethical, legal, and instructional implications of tool choice, data handling, and synthetic data use, and propose strategies to mitigate bias and protect student privacy