Tutor Meeting - LucasWolke/code-tutor GitHub Wiki
Feedback
AI
- AI suggests concepts like Stack even when only Arrays should be used → limit to relevant concepts based on context.
- AI should guide students toward official Java documentation instead of answering all Java-related questions directly.
- Implement an AI layer that connects students to documentation to reduce hallucinations and promote better habits.
- Ensure AI suggestions are appropriate for the student's level; professor-provided context should guide suggestions.
- The AI should be able to answer "Am I done?" to help students know when they've completed a task.
- AI feedback loop should end eventually; it should not keep suggesting endless improvements.
- The AI can be misled into non-relevant topics (e.g., becoming a cooking tutor).
Usability
- Input gets deleted when switching between tasks → should persist user input.
- Students need clearer answers to task-specific questions to understand what to do.
- AI responses should be markdown formatted for better readability.
- Include test cases to automatically check the code instead of just running it.
- Professors should be able to add metadata to tasks, such as hints or relevant links (e.g., course slides).
- Add a button group with pre-defined prompts (like Gemini in Google Docs), e.g.:
- "What is this tool about?"
- "How is this different from ChatGPT/Gemini?"
- "Any tips for me?"
- "How do I break this problem down?"
EP1
- Prevent 'Content Strategy' tutor from suggesting strategies above the student's level.
- Context provided by the professor should influence AI suggestions.
- Provide a dedicated section that explains the learning goals and expectations for EP1.
UI
- Some UI elements (e.g. "Tutor", "Java Editor") have poor readability.
- Text hierarchy is unclear — instructions/settings too large, logo and important labels too small.
- Improve visual clarity so users know where to focus at first glance.
Tutor Meeting
Goal of the Session
- Simulate the experience of a student using the Coding Tutor IDE.
- Assess the AI assistant's usefulness, accuracy, and clarity.
- Provide feedback on technical functionality, user experience, and overall educational value.
Acting Like a Student
Tutors should replicate typical student behavior and explore common use cases.
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Ask Common Student Questions
Think of questions you often hear from students and ask those to the AI. Examples:- Help in understanding the problem
- Debugging help
- Code explanations
- Syntax or logic questions
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Test Different Levels
(The AI Tutor responds on different levels - based on the students needs)- Does the tutor respond with the correct level?
- Are the levels meaningful and helpful?
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Gaming the system
- Does the tutor respond with too much help like code snippets?
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Evaluate UI/UX
- Is it intuitive?
- Does it look good?
- Does writing code/asking questions feel engaging and fun?
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Comment on System Design Short feedback on my implementation, ideas on how to improve it.