Investigating the Potential of GPT-3 in Providing Feedback for Programming Assessments
Rishabh Balse, Bharath Valaboju, Shreya Singhal, Jayakrishnan Madathil Warriem, Prajish Prasad
Abstract
Recent advances in artificial intelligence have led to the development of large language models (LLMs), which are able to generate text, images, and source code based on prompts provided by humans. In this paper, we explore the capabilities of an LLM - OpenAI's GPT-3 model to provide feedback for student written code. Specifically, we examine the feasibility of GPT-3 to check, critique and suggest changes to code written by learners in an online programming exam of an undergraduate Python programming course.
Topics & Concepts
Python (programming language)Computer scienceProgramming languageCode (set theory)Source codeSoftware engineeringArtificial intelligenceMultimediaSet (abstract data type)Teaching and Learning ProgrammingSoftware Testing and Debugging TechniquesSoftware Engineering Research