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.

• 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

• 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?

• Gaming the system

  • Does the tutor respond with too much help like code snippets?

• Evaluate UI/UX

  • Is it intuitive?
  • Does it look good?
  • Does writing code/asking questions feel engaging and fun?

• Comment on System Design Short feedback on my implementation, ideas on how to improve it.