1 citations · 1 across the 1 of their papers we have counts for
5 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 PÄdurean, 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…