
Worked on the JetBrains/educational-plugin repository to enhance AI-driven hint functionality and user feedback mechanisms for Kotlin-based educational content. Developed an AI hint reduction system that expanded support for additional PSI element types, particularly Kotlin functions, and refactored solution logic to improve code maintainability and correctness. Leveraged Kotlin and Java within the IntelliJ plugin framework, ensuring proper threading with runReadAction to prevent UI blocking. Later, implemented a new UI for AI hint feedback using Java Swing, introducing like and dislike buttons directly on hint banners. These changes streamlined user feedback, improved data quality, and enabled more actionable insights for future enhancements.
Month 2025-03: Delivered the AI Hint Feedback Buttons UI for the JetBrains educational-plugin, introducing like and dislike actions directly on AI-generated hint banners to streamline user feedback and improve signal quality. The change reduces friction by replacing the previous feedback action link and enables more actionable data for hint improvements.
Month 2025-03: Delivered the AI Hint Feedback Buttons UI for the JetBrains educational-plugin, introducing like and dislike actions directly on AI-generated hint banners to streamline user feedback and improve signal quality. The change reduces friction by replacing the previous feedback action link and enables more actionable data for hint improvements.
January 2025 monthly summary for JetBrains/educational-plugin focused on delivering a robust AI hint enhancement for Kotlin PSI types, with a strong emphasis on code correctness, expand PSI coverage, and cleaner code paths. The work prioritizes business value by improving accuracy in AI-driven hints, reducing noise in student feedback, and enabling smoother future enhancements.
January 2025 monthly summary for JetBrains/educational-plugin focused on delivering a robust AI hint enhancement for Kotlin PSI types, with a strong emphasis on code correctness, expand PSI coverage, and cleaner code paths. The work prioritizes business value by improving accuracy in AI-driven hints, reducing noise in student feedback, and enabling smoother future enhancements.

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