Master AI context switching and task breakdown by building a smart planner that turns complex learning goals into manageable steps.
We'll use AI to generate the planner logic and practice switching contexts during development
Any text editor or VS Code to save your HTML file and test the planner
We'll begin by getting AI to create the basic HTML structure and planning logic. This gives us a foundation to build on before we add more complex features.
Create an HTML page for an AI Learning Project Planner. I need: 1. A clean, modern interface with a dark theme 2. An input form where users can enter complex learning goals (like "Learn Python for data science") 3. Basic styling that looks professional 4. A placeholder for where the chunked results will appear Make it a single HTML file with embedded CSS and JavaScript. Focus on the structure and styling first - we'll add the chunking logic next.
What to look for: The AI should generate a complete HTML file with a form, dark styling, and a results area. Look for clean CSS organization and semantic HTML structure. Don't worry if the form doesn't do anything yet - we're building the foundation first.
Now we'll switch contexts and focus purely on the chunking logic. This demonstrates how to change direction mid-conversation while building on what we already have.
Perfect! Now let's switch focus to the core functionality. I need you to add JavaScript that: 1. Takes a complex learning goal from the form 2. Breaks it down into 4-6 weekly milestones 3. For each milestone, creates 3-4 specific daily tasks 4. Displays results in a structured, easy-to-read format For example, "Learn Python for data science" should become: - Week 1: Python basics and syntax - Week 2: Data structures and libraries - etc. Add this chunking logic to the existing HTML file. Make the breakdown intelligent - not just generic steps.
What to look for: The AI should add JavaScript that actually processes the input text and creates logical learning progressions. The chunking should be specific to common learning goals, not just generic placeholder text.
Here's where we practice another context switch - from chunking logic to user experience. We'll add a feature that lets users pivot their learning path mid-journey.
Great work! Now let's add a context switching feature. I want users to be able to: 1. See their current learning plan 2. Click a "Change Direction" button on any week 3. Enter a new focus area (like switching from "web development" to "mobile apps") 4. Get a revised plan from that week forward This should demonstrate how you can pivot your learning path without starting over. Add: - "Change Direction" buttons on each weekly milestone - A modal or inline form for the new direction - Logic to regenerate the remaining weeks - Visual indication of what changed This simulates real learning - you often need to adjust your path!
What to look for: The AI should add interactive elements that let you modify the plan mid-stream. Look for buttons on each milestone, some kind of input method for the new direction, and logic that preserves completed weeks while regenerating future ones.
Time for another context shift - from functionality to user experience. We'll add visual progress tracking so users can see their learning journey unfold.
Excellent! Now let's focus on progress visualization. Add these features: 1. Checkboxes for each daily task that users can mark complete 2. Progress bars for each week showing completion percentage 3. An overall progress indicator at the top 4. Visual styling changes as tasks are completed (like strike-through text) 5. Celebration messaging when a week is completed Make it feel rewarding to check things off! The visual feedback should make progress feel tangible and motivating. Use colors and animations to show progress clearly. Also add local storage so progress persists between sessions.
What to look for: The AI should add interactive checkboxes, progress calculations, visual feedback like progress bars or color changes, and localStorage functionality. The interface should feel responsive and rewarding when you complete tasks.
For our final context switch, we'll move from progress tracking to data portability. This teaches you how to cleanly transition between different aspects of a project.
Perfect! For the final feature, let's add export capabilities: 1. "Export Plan" button that generates a clean markdown file with the learning roadmap 2. "Copy to Clipboard" option for quick sharing 3. "Print-Friendly" view that formats nicely for physical printing 4. Option to export just completed items vs. the full plan The export should be clean and readable - something you'd actually want to reference or share with a mentor. Include: - Original learning goal - Current progress percentage - Detailed breakdown by week - Completion status for each task Make the exported format look professional and useful outside the app.
What to look for: The AI should add export buttons and functions that generate clean, readable output. Look for markdown formatting, clipboard functionality, and a print-friendly layout. The exported content should be something you'd actually find useful.
If the chunking logic creates vague tasks like "Study topic X," ask the AI to make them more specific: "Instead of generic steps, create actionable tasks like 'Complete Python tutorial chapters 1-3' or 'Build a simple calculator project.'"
If the AI starts over instead of building on existing code, be explicit: "Add this feature to the existing HTML file - don't recreate the whole thing. Just show me the new JavaScript function and where to add it."
If localStorage isn't working, ask the AI to "add console.log statements to help me debug the localStorage saving and loading functions." This helps you understand what's happening.
If you lose track of the code structure, ask: "Can you add detailed comments explaining what each major function does? I want to understand how the pieces fit together."
You practiced changing conversation direction 5 times - from structure to logic to UX to tracking to export - without confusing the AI.
You learned to break a multi-feature project into logical AI-friendly chunks, building each piece systematically.
You practiced reading AI-generated code to understand how HTML, CSS, and JavaScript pieces work together in a complete project.
You experienced how real projects evolve - starting simple, adding complexity gradually, and switching focus as needs emerge.
Enhance the chunking logic to recognize different types of goals - coding vs. languages vs. creative skills - and adjust the breakdown accordingly.
Add the ability to create learning plans for teams, with role assignments, shared milestones, and group progress tracking.
Use AI to analyze completed learning patterns and suggest optimal next steps or complementary skills to learn.
Integrate with APIs to automatically suggest actual courses, tutorials, or books for each learning milestone.