activity
20242026
most citedExploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models

1 citations · 1 across the 15 of their papers we have counts for

collaborators
Showing 2024Show all

10 papers · 1 filter

cs.SE2024

Hints Help Finding and Fixing Bugs Differently in Python and Text-based Program Representations

Ruchit Rawal, Victor-Alexandru Pădurean, Sven Apel +2

With the recent advances in AI programming assistants such as GitHub Copilot, programming is not limited to classical programming languages anymore--programming tasks can also be e…

cs.SE2024

BugSpotter: Automated Generation of Code Debugging Exercises

Victor-Alexandru Pădurean, Paul Denny, Adish Singla

Debugging is an essential skill when learning to program, yet its instruction and emphasis often vary widely across introductory courses. In the era of code-generating large langua…

cs.CY2024

Exploring the Impact of Quizzes Interleaved with Write-Code Tasks in Elementary-Level Visual Programming

Ahana Ghosh, Liina Malva, Alkis Gotovos +2

We explore the role of quizzes in elementary visual programming domains popularly used for K-8 computing education. Prior work has studied various quiz types, such as fill-in-the-g…

cs.AI2024

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…

cs.LG2024

Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences

Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban +3

In this paper, we take a step towards a deeper understanding of learning from human preferences by systematically comparing the paradigm of reinforcement learning from human feedba…

cs.LG2024

Learning Embeddings for Sequential Tasks Using Population of Agents

Mridul Mahajan, Georgios Tzannetos, Goran Radanovic +1

We present an information-theoretic framework to learn fixed-dimensional embeddings for tasks in reinforcement learning. We leverage the idea that two tasks are similar if observin…