💬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 / Tools
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 / Tools
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 / Tools
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 / Tools
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.

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