How to Set Goals for English Learning with AI: A Complete Guide Using the English-Level-Up-Tips Framework

You can set effective English learning goals by embedding a structured system prompt into an LLM that acts as a persistent English Level-Up Coach, breaking high-level objectives into weekly micro-goals and following a closed-loop workflow of warm-up, input, output, correction, and review.

The byoungd/English-level-up-tips repository provides a production-ready architecture for AI-assisted language acquisition. Instead of using ChatGPT or Gemini as a simple translation dictionary, this framework treats the model as a personal tutor that maintains context across sessions, turning abstract aspirations into executable daily lessons stored in docs/en/threads/part-1/7-ai.md.

Understanding the Repository's Goal-Setting Architecture

The repository organizes its methodology into modular chapters under docs/en/. The entry point at docs/en/README.md maps the learning journey, while the AI-specific chapter at docs/en/threads/part-1/7-ai.md contains the canonical English Level-Up Coach prompt. This prompt functions as the central configuration file for your AI coaching relationship, encoding your goals directly into the model's system instructions.

Step 1: Define Your High-Level Target

Begin by writing a concise global objective that scopes all subsequent activities. In 7-ai.md (lines 64-66), the author recommends framing your target as a 12-week mission focused on specific contexts like workplace communication, IELTS preparation, or technical English.

Example target statement:

"Improve speaking, listening, reading, and writing over the next 12 weeks, with special focus on spoken English and workplace communication at the B1-B2 proficiency level."

This high-level target acts as the global objective in your AI prompt, ensuring the model tailors all generated content to the appropriate difficulty band and domain vocabulary.

Step 2: Decompose Goals into Weekly Micro-Targets

The architecture emphasizes small, measurable goals (Rule 5 in the prompt at lines 70-73). Rather than asking the AI to "make me fluent," you provide discrete, weekly milestones that the model can operationalize into specific lesson plans.

Effective micro-goals include:

• Week 1: Master 20 high-frequency speaking expressions for meetings
• Week 2: Create flashcards for 50 new technical vocabulary items
• Week 3: Deliver a coherent 2-minute answer to IELTS Speaking Part 2 topics

These micro-goals enable the AI to generate focused activities and track progress against concrete benchmarks rather than vague aspirations.

Step 3: Configure the English Level-Up Coach Prompt

The core of the methodology is embedding your goals into a system prompt that persists across sessions. Located at lines 64-86 in docs/en/threads/part-1/7-ai.md, this prompt transforms the LLM from a stateless chatbot into a personalized coach.

Copy and customize this prompt in Gemini, ChatGPT, Claude, or any LLM supporting system instructions:

You are my English Level-Up Coach.  
My native language is Chinese. My current English level is around B1-B2. My goal is to improve speaking, listening, reading, and writing over the next 12 weeks, with special focus on spoken English and workplace communication.

Please follow these rules:
1) Use English by default, but use brief Chinese when explaining hard grammar or subtle meaning differences.
2) Keep each lesson around 20-30 minutes.
3) Each lesson must include warm-up, input, output, correction, and review.
4) Prioritize frequent, high-value mistakes instead of minor perfectionism.
5) Give me one clear goal per lesson.
6) Track my recurring mistakes and review them weekly.
7) If I say "start today's lesson," begin immediately.
8) If I upload an article, resume, email, meeting note, or transcript, turn it into learning material.
9) Push me to produce language instead of doing all the work for me.
10) At the end of each lesson, summarize useful expressions, key mistakes, homework, and the focus of the next lesson.

If you already know my goals, add them directly:
- IELTS speaking
- job interviews
- email writing
- workplace English
- technical English

By encoding your specific micro-goals under the "If you already know my goals" section, the LLM maintains persistent awareness of your current learning trajectory across multiple sessions.

Step 4: Generate Structured Lessons from Goals

Once your goals are embedded in the system prompt, request lessons that target specific micro-objectives. The lesson-template rule (Rule 3 at lines 68-71) guarantees every session contains the full learning loop essential for long-term retention.

Prompt template for lesson generation:

Generate a 25-minute lesson that helps me achieve the goal "IELTS speaking – deliver a coherent 2-minute answer to Part 2 topics". Follow the warm-up → input → output → correction → review structure and end with a brief homework assignment.

This structure ensures you move through input (exposure to target language), output (active production), and correction (error identification) in a single session, preventing passive consumption without practice.

Step 5: Capture Feedback and Build an Error Bank

After each lesson, the AI generates a summary following Rule 10: useful expressions, key mistakes, homework, and next-lesson focus. The tracking rule (Rule 6) creates a reusable error bank for spaced repetition.

Log this output to enable longitudinal progress tracking:

# After each AI session, append the summary to a local log

cat <<EOF >> ~/english_coach.log
$(date +"%Y-%m-%d %H:%M")
Goal: IELTS speaking – Part 2
Useful expressions: …
Key mistakes: …
Homework: …
Next focus: …
EOF

This log file serves as the data source for your weekly review, allowing the AI to identify patterns in your recurring errors and adjust future goals accordingly.

Step 6: Iterate Goals Based on Performance

Review your log weekly and ask the AI to generate targeted reinforcement:

Based on the log entries from the past week, create a 10-question quiz that revisits the top 5 recurring mistakes. Include both multiple-choice and open-ended items.

Because the system prompt is reused each session, any refinement to your micro-goals immediately influences the AI's behavior, creating an adaptive learning path that evolves with your proficiency.

Summary

• Embed goals in system prompts to create persistent AI coaching context across sessions, as implemented in docs/en/threads/part-1/7-ai.md.
• Break 12-week targets into weekly micro-goals (Rule 5) to ensure each lesson has one clear, measurable objective.
• Follow the five-phase lesson structure (warm-up → input → output → correction → review) for maximum retention.
• Log mistakes weekly to build an error bank that enables spaced repetition and targeted remediation.
• Iterate based on AI feedback by updating the goal list in your system prompt, keeping the learning path adaptive.

Frequently Asked Questions

Can I use this framework with ChatGPT or Claude instead of Gemini?

Yes. The prompt in 7-ai.md uses standard instruction-following syntax compatible with any modern LLM. Paste the entire text into ChatGPT's custom instructions field, Claude's project settings, or Gemini's Gems feature. The architecture is model-agnostic as long as the system supports persistent context or custom instructions.

How long should each learning session be?

According to Rule 2 in the canonical prompt (lines 67-68), each lesson should last 20-30 minutes. This duration balances cognitive load with sufficient time to complete the full warm-up → input → output → correction → review loop. The repository emphasizes frequency over duration—daily 25-minute sessions outperform weekly 2-hour cramming.

What if my English level is beginner (A1) instead of intermediate (B1-B2)?

Adjust the proficiency declaration in the system prompt. Change "My current English level is around B1-B2" to your actual level (A1, A2, etc.). However, review docs/en/threads/part-1/1-understanding.md first, as the repository recommends ensuring you have enough foundational vocabulary to benefit from AI-driven output activities. You may need to extend the timeline from 12 weeks to 24 weeks for A1 learners.

How do I track progress across multiple weeks?

Create a plain-text log file (as shown in the bash example) that stores the AI-generated summaries after each lesson. Weekly, feed this log back to the AI with the request: "Review my error patterns from the past week and adjust next week's goals to address the top 3 recurring mistakes." This closed-loop system, documented in the AI chapter's tracking rules (lines 79-86), ensures your goals remain data-driven rather than arbitrary.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}