AI core technology instructor for recording. Not coding.

Instructor to record following skill areas: Content will be provided. To teach this introductory, non-technical AI course, an instructor needs a blend of foundational technical knowledge, practical browser-based tool fluency, and educational/assessment skills: 1. Core Subject Knowledge * AI & Machine Learning Concepts: Understanding fundamental principles like supervised vs. unsupervised learning, neural networks, generative AI vs. discriminative models, and LLM mechanics (tokens, context windows, next-token prediction). * Prompting & Workflow Techniques: Mastery of prompt anatomy, role framing, grounding, few-shot examples, prompt chaining, and task augmentation. * Responsible AI Practices: Knowledge of data quality dimensions, bias/harm classification (representational vs. allocative), privacy/data minimization, prompt injection risks, and human-in-the-loop oversight. 2. Technical & Practical Skills * Browser-Based Tooling: Ability to guide students using built-in OS text editors (e.g., Notepad, TextEdit), managing plain-text/CSV files, and navigating browser-based environments like Hugging Face or supplied HTML explorers without installing software or command-line tools. * Troubleshooting: Skill in guiding students through blocked-site fallbacks (using captured reply sets) and debugging basic plain-text formatting issues. 3. Pedagogy & Assessment * Addressing Misconceptions: Ability to directly target and correct beginner misconceptions (e.g., explaining that AI doesn't "think" like a human or that models do not simply retrieve stored answers). * Non-Deterministic Evaluation: Proficiency in evaluating non-deterministic model outputs by grading process, reasoning, and evidence using qualitative rubrics rather than expecting a single "correct" answer. Here is the lesson structure: we will provide content. Lesson 1: What Artificial Intelligence Is - And What It Is Not  Lesson 2: Setting Up Your Browser-Based AI Workspace  Lesson 3: How Machine Learning Learns From Data  Lesson 4: Data - The Raw Material Behind Every Model  Lesson 5: Neural Networks and Deep Learning Explained  Lesson 6: The Rise of Generative AI  Lesson 7: Inside Large Language Models  Lesson 8: Beyond Text - Images, Audio, and Multimodal Systems  Lesson 9: Prompting Fundamentals - The Anatomy of a Clear Prompt  Lesson 10: Giving Context, Role, and Format  Lesson 11: Few-Shot and Multi-Step Prompting  Lesson 12: Evaluating and Iterating on AI Output  Lesson 13: Capabilities, Limitations, and Hallucination  Lesson 14: Deciding When AI Is the Right Tool  Lesson 15: Augmenting Everyday Work Tasks  Lesson 16: Keeping the Human in the Loop  Lesson 17: Bias, Fairness, and Harm in AI Systems  Lesson 18: Privacy, Security, and Responsible Data Handling  Lesson 19: Staying Current as AI Evolves  Lesson 20: Capstone Completion and Course Close-Out