Work

Students' Perceptions to a Large Language Model's Generated Feedback and Scores of Argumentation Essays

Year: 2025

Type: conference-paper

Abstract: Students in introductory physics courses often rely on ineffective strategies, focusing on final answers rather than understanding underlying principles.Integrating scientific argumentation into problem-solving fosters critical thinking and links conceptual knowledge with practical application.By facilitating learners to articulate their scientific arguments for solving problems, and by providing real-time feedback on students' strategies, we aim to enable students to develop superior problem-solving skills.Providing timely, individualized feedback to students in large-enrollment physics courses remains a challenge.Recent advances in Artificial Intelligence (AI) offer promising solutions.This study investigates the potential of AI-generated feedback on students' written scientific arguments in an introductory physics class.Using Open AI's GPT-4o, we provided delayed feedback on student written scientific arguments and surveyed them about the perceived usefulness and accuracy of this feedback.Our findings offer insights into the viability of implementing real-time AI feedback to enhance students' problem-solving and metacognitive skills in large-enrollment classrooms.

Source: Physics Education Research Conference (PERC)

DOI: 10.1119/perc.2025.pr.allen

Authors: Winter Allen, Anand Shanker, N. Sanjay Rebello

Institutions: Purdue University West Lafayette, Purdue University in Indianapolis

Language: English

Topic: Student Assessment and Feedback

Subfield: Education

Field: Social Sciences

Domain: Social Sciences

SDG: Quality education

Open Access status: hybrid

Funder: National Science Foundation