17 citations · 36 across the 8 of their papers we have counts for
8 papers
Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models
Chao Wen, Tung Phung, Pronita Mehrotra +4
Generative AI has democratized content creation, but popular chatbot-based interfaces often prioritize execution, generating fully rendered artifacts right away. This issue can lea…
Reflection-Satisfaction Tradeoff: Investigating Impact of Reflection on Student Engagement with AI-Generated Programming Hints
Heeryung Choi, Tung Phung, Mengyan Wu +2
Generative AI tools, such as AI-generated hints, are increasingly integrated into programming education to offer timely, personalized support. However, little is known about how to…
Humanizing Automated Programming Feedback: Fine-Tuning Generative Models with Student-Written Feedback
Victor-Alexandru Pădurean, Tung Phung, Nachiket Kotalwar +4
The growing need for automated and personalized feedback in programming education has led to recent interest in leveraging generative AI for feedback generation. However, current a…
Bridging Gaps Between Student and Expert Evaluations of AI-Generated Programming Hints
Tung Phung, Mengyan Wu, Heeryung Choi +4
Generative AI has the potential to enhance education by providing personalized feedback to students at scale. Recent work has proposed techniques to improve AI-generated programmin…
Plan More, Debug Less: Applying Metacognitive Theory to AI-Assisted Programming Education
Tung Phung, Heeryung Choi, Mengyan Wu +2
The growing adoption of generative AI in education highlights the need to integrate established pedagogical principles into AI-assisted learning environments. This study investigat…
Automating Human Tutor-Style Programming Feedback: Leveraging GPT-4 Tutor Model for Hint Generation and GPT-3.5 Student Model for Hint Validation
Tung Phung, Victor-Alexandru Pădurean, Anjali Singh +5
Generative AI and large language models hold great promise in enhancing programming education by automatically generating individualized feedback for students. We investigate the r…
Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors
Tung Phung, Victor-Alexandru Pădurean, José Cambronero +5
Generative AI and large language models hold great promise in enhancing computing education by powering next-generation educational technologies for introductory programming. Recen…
Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models
Tung Phung, José Cambronero, Sumit Gulwani +4
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to g…