7 citations · 13 across the 12 of their papers we have counts for
14 papers
When AI Is Wrong on Purpose: How Students Respond to Buggy GenAI Code
Victor-Alexandru Pădurean, Kaitlin Riegel, Alkis Gotovos +6
As Generative AI (GenAI) becomes increasingly central to software development, CS education is integrating prompt-centered workflows where students describe intended program behavi…
Understanding Student Perceptions, Mistakes, and Debugging Approaches when Solving Natural Language Programming Tasks
Victor-Alexandru Pădurean, Kaitlin Riegel, Gweneth Barbre +7
Learning to communicate with code-generating AI models is an emerging skill for novice programmers. One recent pedagogical approach, Prompt Problems, has students solve computation…
The Right Kind of Help: Evaluating the Effectiveness of Feedback Methods in Elementary-Level Visual Programming
Ahana Ghosh, Liina Malva, Alkis Gotovos +2
We present a large-scale study comparing the effectiveness of various feedback methods in elementary-level programming. While prior work has explored different feedback methods, th…
Inference-Time Personalized Alignment with a Few User Preference Queries
Victor-Alexandru Pădurean, Parameswaran Kamalaruban, Nachiket Kotalwar +2
We study the problem of aligning a generative model's response with a user's preferences. Recent works have proposed several different formulations for personalized alignment; howe…
Interleaving Natural Language Prompting with Code Editing for Solving Programming Tasks with Generative AI Models
Victor-Alexandru Pădurean, Alkis Gotovos, Ahana Ghosh +5
Modern computing students often rely on both natural-language prompting and manual code editing to solve programming tasks. Yet we still lack a clear understanding of how these two…
Synthesizing High-Quality Programming Tasks with LLM-based Expert and Student Agents
Manh Hung Nguyen, Victor-Alexandru Pădurean, Alkis Gotovos +2
Generative AI is transforming computing education by enabling the automatic generation of personalized content and feedback. We investigate its capabilities in providing high-quali…
Prompt Programming: A Platform for Dialogue-based Computational Problem Solving with Generative AI Models
Victor-Alexandru Pădurean, Paul Denny, Alkis Gotovos +1
Computing students increasingly rely on generative AI tools for programming assistance, often without formal instruction or guidance. This highlights a need to teach students how t…
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…
Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation
Nachiket Kotalwar, Alkis Gotovos, Adish Singla
Generative AI and large language models hold great promise in enhancing programming education by generating individualized feedback and hints for learners. Recent works have primar…
On the Existence of Universal Lottery Tickets
Rebekka Burkholz, Nilanjana Laha, Rajarshi Mukherjee +1
The lottery ticket hypothesis conjectures the existence of sparse subnetworks of large randomly initialized deep neural networks that can be successfully trained in isolation. Rece…
Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification
Alkis Gotovos, Rebekka Burkholz, John Quackenbush +1
Modeling the time evolution of discrete sets of items (e.g., genetic mutations) is a fundamental problem in many biomedical applications. We approach this problem through the lens…
Strong Log-Concavity Does Not Imply Log-Submodularity
Alkis Gotovos
We disprove a recent conjecture regarding discrete distributions and their generating polynomials stating that strong log-concavity implies log-submodularity.
Discrete Sampling using Semigradient-based Product Mixtures
Alkis Gotovos, Hamed Hassani, Andreas Krause +1
We consider the problem of inference in discrete probabilistic models, that is, distributions over subsets of a finite ground set. These encompass a range of well-known models in m…
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
Philippe Wenk, Alkis Gotovos, Stefan Bauer +3
Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data ha…