What is Prompt Engineering for teens?

Prompt Engineering is the art and science of designing precise, structured instructions (prompts) to guide Large Language Models (LLMs like ChatGPT, Claude, and Gemini) to produce accurate, controlled, and high-quality outputs.

What is the AI Explorer course at TeacherColab?

AI Explorer is a 25-hour live online course for teens aged 15–17 covering prompt engineering frameworks (PCTF), Large Language Models, Generative AI tools, study automation, AI ethics, and hallucination verification.

Does my child need coding skills to take Prompt Engineering?

No coding or programming experience is required! Prompt Engineering uses natural English language instructions to communicate with AI systems.

What AI tools will my child learn to use?

Students learn to effectively use industry-leading AI models: Anthropic Claude, OpenAI ChatGPT, Google Gemini, Microsoft Copilot, and image generators like DALL-E and Adobe Firefly.

What is the PCTF Prompting Framework?

PCTF stands for Persona, Context, Task, and Format. It is a professional 4-step framework for structuring prompts to get reliable, high-performing AI outputs every time.

What are LLM hallucinations and how do students catch them?

Hallucination occurs when an AI model confidently generates false or fabricated information. We teach students the SIFT fact-checking method (Stop, Investigate, Find, Trace) to critically verify AI responses against primary sources.

What real-world projects will students build?

Students build an AI Interaction Audit Report, a Prompt Engineering Toolkit of reusable templates, an AI Productivity Portfolio, and a complete multi-prompt AI System Capstone.

What techniques are taught in the course?

Students master Role-Based Prompting, Chain-of-Thought reasoning ('think step by step'), Context Injection, Output Formatting (JSON, Tables, Markdown), Zero-Shot, One-Shot, and Few-Shot prompting.

How long is the Prompt Engineering & AI Tools course?

The course consists of 25 hours of live interactive instruction across 4 structured modules, ending with a final prompt system design showcase.

Are the classes live or pre-recorded?

All TeacherColab classes are 100% live, interactive online sessions led by expert AI mentors.

How does AI help high school students with studying and schoolwork?

AI can generate custom revision quizzes, create flashcards, summarize complex documents (ELI5), explain difficult coding snippets, and assist with writing while maintaining academic integrity.

What topics are covered regarding AI ethics and responsible AI?

Students discuss academic integrity, copyright laws, AI bias, data privacy, deepfakes, job displacement, and environmental costs of training large models.

Will my child earn a certificate of completion?

Yes! Every student who completes the course and capstone prompt design challenge receives a verified digital Certificate of Completion from TeacherColab.

What is the batch size for AI classes?

We maintain small batch sizes of 3 to 5 students to ensure interactive prompt testing and live teacher feedback.

What software and hardware are needed?

Any computer (Windows, Mac, Chromebook) with a web browser and internet connection. Students access free-tier AI tools (Claude, ChatGPT, Gemini) directly in Chrome.

Is prompt engineering a recognized career skill?

Yes! Prompt engineering and AI fluency are among the fastest-growing job skills across technology, marketing, finance, research, and software engineering.

Is teacher support available outside class hours?

Yes! TeacherColab provides 24/7 mentor assistance via email and chat to answer student questions.

Can we reschedule if my child misses a class?

Yes, free makeup sessions are arranged with at least 24 hours prior notice.

Are parent progress reports provided?

Yes, comprehensive progress reports are sent at the end of each module detailing prompt projects built, critical evaluation scores, and milestone progress.

How can parents book a free demo class?

Click any 'Book Free Demo' button, fill out the form, and pick a time slot that works best for your schedule.

Back to Programs

💬Course Curriculum

Prompt Engineering & AI Tools

AI & Machine Learning· Intermediate· Ages 15–17· 25 Hours

Course At a Glance

Program Outcomes

By the end of this course, students will be able to:

1
Understand how AI systems (particularly Large Language Models) interpret and respond to prompts.
2
Design effective, structured prompts using professional techniques to generate useful, controlled outputs.
3
Apply AI tools responsibly and effectively for productivity, creativity, research, and learning.
4
Develop a prompt-based project demonstrating practical AI interaction skills and critical evaluation of AI outputs.

Module 1

Introduction to Artificial Intelligence & Prompting

Students learn how LLMs work, what a prompt is, the major types of prompts, an overview of leading AI tools, responsible AI use, and critical evaluation of AI-generated content.

Approx. 6–7 hrs

#	Lesson Title	What Students Learn	Activity / Project	Key Concepts / Syntax
1.1	What is Artificial Intelligence?	Define AI and explore the landscape of narrow AI vs general AI. Understand pattern-matching at scale vs 'thinking'.	AI in My Life: Identify 10 everyday AI apps, map their inputs/outputs, and discuss their impact.	`Narrow AI vs general AI, pattern recognition, training data, input/output, recommendation systems`
1.2	How Large Language Models Work	Understand LLMs conceptually (predicting the next token). Learn about training data, context windows, temperature, and hallucinations.	Hallucination Hunt: Ask an AI chatbot questions likely to induce hallucination. Verify and document the responses.	`LLM, tokens, context window, training data, hallucination, temperature, statistically probable`
1.3	What is a Prompt?	Define prompts (instruction + context + input + format). Learn that 'garbage in, garbage out' applies to AI models.	Prompt Comparison: Run 5 versions of a prompt (vague to specific) and rank output quality.	`Prompt = instruction + context + input + output format, vague vs specific, garbage in/garbage out`
1.4	Types of Prompts	Explore instruction, question, completion, conversation, and transformation prompts. Learn zero/one/few-shot prompting.	Prompt Type Lab: Complete 6 tasks using different prompt types and evaluate which produced the most controlled output.	`Instruction, question, completion, conversation, transformation, zero-shot, one-shot, few-shot`
1.5	AI Tools Overview	Survey Claude, ChatGPT, Gemini, Copilot, DALL-E, and Firefly. Understand tool strengths and limitations.	Tool Comparison: Run the same crafted prompt through two different AI text tools. Compare accuracy, tone, and structure.	`Claude, ChatGPT, Gemini, Copilot, DALL-E, Adobe Firefly, GitHub Copilot, tool strengths`
1.6	Responsible Use of AI	Explore academic integrity, copyright, privacy, AI bias, and environmental costs. Develop an ethical AI framework.	AI Ethics Debate: Debate topics like academic integrity vs AI assistance or copyright vs AI art.	`Academic integrity, copyright, AI bias, data privacy, environmental cost, responsible AI`
1.7	Critical Evaluation of AI Outputs	Develop verification skills using the SIFT method (Stop, Investigate, Find, Trace). Check factual accuracy vs plausibility.	SIFT in Action: Ask AI to explain recent news events. Apply the SIFT method to fact-check the response.	`SIFT (Stop/Investigate/Find/Trace), knowledge cutoff, hallucination verification, fact-checking`
1.8	Module 1 Project: AI Interaction Report	Produce a structured report demonstrating understanding of LLMs, prompt types, hallucination, and evaluation.	Project: 'AI Interaction Report' — create a 2-page report documenting prompt experiments, a hallucination case study, and personal use guidelines.	`Full Module 1 — LLM concepts, prompt types, hallucination, responsible use, critical evaluation`

Module 2

Prompt Engineering Techniques

Students master professional prompt engineering: the PCTF framework, role-based prompting, chain-of-thought, context injection, iterative refinement, few-shot, and template design.

Approx. 6–7 hrs

#	Lesson Title	What Students Learn	Activity / Project	Key Concepts / Syntax
2.1	The Anatomy of an Effective Prompt	Break down prompt effectiveness. Introduce the PCTF framework: Persona, Context, Task, Format.	Prompt Anatomy Lab: Rewrite 5 weak prompts using PCTF. Run before/after comparisons and rate the outputs.	`PCTF (Persona/Context/Task/Format), specificity, constraints, output specification`
2.2	Role-Based Prompting	Assign personas to shape the style, depth, and tone of the response (e.g., 'You are an expert nutritionist').	Role Comparison: Ask AI to explain a topic using 4 different personas (child's author, professor, comedian, journalist).	`Role assignment: 'You are a...', persona prompting, tone control, expertise level`
2.3	Chain-of-Thought & Step-by-Step Prompting	Improve reasoning accuracy by forcing the AI to 'think step by step' before answering.	Step-by-Step vs Direct: Test AI on logic puzzles directly vs using chain-of-thought. Evaluate reasoning visibility.	`'Think step by step', chain-of-thought, intermediate reasoning, 'Let\'s work through this'`
2.4	Context & Background Information	Inject context (user knowledge level, constraints) to ground the AI's response. Understand context limits.	Context Injection: Run a research prompt with increasing levels of detailed context. Observe output changes.	`Context injection, student level, purpose statement, pasting text into prompt, context window`
2.5	Output Format Control	Control output structures: bullet points, tables, JSON, markdown, specific lengths, and custom templates.	Format Factory: Run the same prompt using 5 different format instructions (prose, table, template, etc.).	`Format: 'as a table', 'as bullet points', 'in markdown', 'in 100 words', structured template`
2.6	Iterative Refinement & Follow-Up Prompts	Understand prompting as an iterative conversation. Learn targeted follow-up prompts for progressive refinement.	Iteration Challenge: Start with a simple prompt and use 5 follow-ups (tone, length, formatting) to refine the output.	`Follow-up prompts, 'Make this more...', iterative refinement, conversation history, targeted feedback`
2.7	Few-Shot Prompting & Templates	Provide examples (few-shot) and build reusable templates with [placeholders] for repetitive tasks.	Template Builder: Build 3 reusable prompt templates for study/school tasks with [placeholders]. Test with inputs.	`Few-shot examples, [placeholder] templates, example-driven prompting, reusable templates`
2.8	Module 2 Project: Prompt Engineering Toolkit	Compile tested, documented prompt templates covering diverse use cases (study, coding, writing).	Project: 'Prompt Engineering Toolkit' — produce a formatted document with 8+ refined prompt templates and examples.	`Full Module 2 — PCTF, role, chain-of-thought, context, format, iteration, few-shot, templates`

Module 3

AI for Productivity & Creativity

Students apply AI tools to tasks across six domains: writing, research, study, coding assistance, design ideation, and data analysis. Ethical considerations are woven throughout.

Approx. 6–7 hrs

#	Lesson Title	What Students Learn	Activity / Project	Key Concepts / Syntax
3.1	AI for Writing & Editing	Use AI to overcome writer's block, change tone, restructure arguments, and provide critical feedback on drafts.	Writing Workflow: Write 150 words unprompted, then use AI to identify weak sentences, rewrite hooks, and adjust tone.	`Draft generation, tone adjustment, 'what is the weakest...', critique prompts, assisted vs generated`
3.2	AI for Research & Summarisation	Use AI for ELI5 explanations, document summarisation, and theme identification. Verify against primary sources.	Research Sprint: Use AI to get topic overviews, identify subtopics, and summarise text. Verify 3 facts manually.	`ELI5 prompts, 'summarise this text', 'identify key themes', 'explain X to a beginner', verification`
3.3	AI for Study & Revision	Generate quizzes, flashcards, worked examples, and comparison tables to support active recall study sessions.	Personalised Study Session: Create a 20-minute revision pack (quiz, table, cheat sheet) using tailored prompts.	`Quiz generation, flashcard prompts, 'explain X like I\'m struggling with it', worked examples, cheat sheet`
3.4	AI for Coding Assistance	Use AI to explain code, identify bugs, generate boilerplate, and convert languages. Understand the need for human testing.	Code Help Session: Use AI to debug Python, explain snippets line-by-line, and translate functions to JavaScript.	`'What does this code do?', 'Debug this code:', 'Write a function that...', code explanation`
3.5	AI for Design & Creative Ideas	Generate brainstorming ideas, creative briefs, and specific image generation prompts for DALL-E/Firefly.	Creative AI Collaboration: Brainstorm brand names, write a creative brief, and generate image/social prompts for an event.	`Brainstorming prompts, image generation prompt structure (subject/style/lighting/mood/composition)`
3.6	AI for Data Analysis & Interpretation	Ask AI to identify trends, suggest chart types, and write interpretations from provided data tables.	Data Interpretation Sprint: Paste a data table. Ask AI to identify trends, suggest charts, and draft report sentences.	`'Identify trends in this data:', 'What chart type best shows...', 'Write 3 interpretation sentences'`
3.7	Ethical Considerations & AI Limitations	Examine deepfakes, algorithmic bias, AI in hiring, and job displacement. Discuss human-in-the-loop systems.	Ethics Case Studies: Debate real-world AI ethics scenarios (e.g., AI hiring tools, algorithmic bias).	`Algorithmic bias, deepfakes, AI in hiring/justice, misinformation, human-in-the-loop, AI policy`
3.8	Module 3 Project: AI Productivity Portfolio	Demonstrate practical AI assistance across multiple domains, showing initial prompts, outputs, and manual refinements.	Portfolio: 'AI Productivity Portfolio' — document 4 AI-assisted tasks, showing the iterative workflow and a reflection.	`Full Module 3 — writing, research, study, coding, design, data, ethics, reflection`

Module 4

AI Project: Prompt Design Challenge

Students design, build, test, and present a complete prompt-based solution: a multi-prompt system solving a real-world task. Includes edge-case testing, a sample output pack, and a live demo.

Approx. 6–7 hrs

#	Lesson Title	What Students Learn	Activity / Project	Key Concepts / Syntax
4.1	Project Briefing & Problem Selection	Frame a problem for an AI solution. Define the target user, expected outputs, and success criteria.	Problem Framing: Submit a Problem Statement detailing the task, audience, and 3 success criteria.	`Problem Statement, target user, success criteria, prompt system concept`
4.2	Research & Inspiration	Analyse existing prompt libraries and templates. Use AI to research common pain points in the chosen problem space.	Inspiration Research: Audit 3 existing prompt tools/templates and use AI to identify user frustrations.	`Prompt library research, competitive analysis, 'What are the biggest frustrations...', prompt audit`
4.3	Prompt System Design	Map out a 5+ prompt chained system. Design the input/output flow where each prompt handles a subtask.	System Design: Produce a Prompt System Map diagramming the flow. Draft version 1 of all prompts with placeholders.	`Prompt system map, input/output flow, subtask decomposition, chained prompts, [placeholders]`
4.4	Prompt Testing & Iteration — Round 1	Test prompts systematically. Apply PCTF, role, and format techniques to fix identified output issues.	Testing Log Round 1: Test all prompts with 3 inputs. Document the initial output, identify issues, and write version 2.	`Testing Log, version 1 → version 2, identify issues, apply technique (role/format/context/few-shot)`
4.5	Prompt Testing & Iteration — Round 2	Test for edge cases (too long, adversarial inputs). Apply guardrails ('Do not include X') to prevent hallucination.	Testing Log Round 2: Stress-test prompts. Add guardrails to reach version 3. Conduct a peer test using only the templates.	`Edge cases, guardrails, 'Do not include...', 'Keep under X words', adversarial testing, peer test`
4.6	Sample Output Pack	Document the complete chained system in action: input → prompt → output for every step. Mark manual post-edits.	Output Pack Build: Run the prompt system end-to-end with a real example. Document outputs and manual edits.	`End-to-end system run, complete input → prompt → output documentation, [edited] marking`
4.7	Evaluation, Limitations & Reflection	Evaluate the system against success criteria. Honestly document limitations, edge-case failures, and human oversight needs.	Evaluation Report: Rate success criteria, list 3 limitations, and write a reflection on the system's human-in-the-loop requirements.	`Success criteria evaluation, limitations documentation, human-in-the-loop, reflection`
4.8	Final Presentation: Prompt Design Challenge	Deliver a live presentation demonstrating the prompt system, its iterative journey, and its limitations.	Final Demo: 5-minute live demo of the prompt system with real inputs + Q&A. Certificates awarded.	`Full course — LLM concepts, prompt types, PCTF, role/CoT/format/iteration, AI tools, ethics`

Teaching Notes & Tips

Pacing Guidance

Each module contains 8 lessons (~40–50 mins), totalling ~25 hours. Highly discussion-driven. Lessons on AI ethics and critical evaluation often run long; plan accordingly. Teachers should run prompts live alongside students.

Differentiation

Advanced students can explore the Anthropic Prompt Library, API access (Python), or Retrieval-Augmented Generation (RAG). Students needing support should focus on modifying provided prompt templates in Modules 1-2.

Assessment Criteria

Capstone assessed on: (1) Problem Clarity. (2) Prompt Quality (PCTF structure, iterations). (3) System Design (chained prompts). (4) Output Quality (useful, edited). (5) Evaluation Honesty. (6) Live Demo.

Tools & Access

Primary tool: Claude or ChatGPT (free tier). Image generation: Adobe Firefly or DALL-E. A school-managed account or supervised browser session is highly recommended. Treat AI tool usage as a professional skill.

Capstone Project Tracks

AI Study Assistant System (quiz/summary/flashcards generator), Blog/Content Writing System, Career Exploration Tool (CV/Interview prep), or Creative Writing Toolkit (story ideation/character dev).

Prior Knowledge Expected

Students should be comfortable using a computer, creating accounts, and copy/pasting. No programming experience required. An open, curious mindset and willingness to question AI outputs is essential.