Calamas, D. (2024, March), Student and Instructor Feedback on an AI-Assisted Grading Tool  Paper presented at 2024 South East Section Meeting, Marietta, Georgia. 10.18260/1-2--45560

\bibitem{asee_peer_45560}
  Calamas, D. (2024, March), \emph{Student and Instructor Feedback on an AI-Assisted Grading Tool}
  Paper presented at 2024 South East Section Meeting, Marietta, Georgia.
  10.18260/1-2--45560

David Calamas.  "Student and Instructor Feedback on an AI-Assisted Grading Tool".  2024 South East Section Meeting, Marietta, Georgia, 2024, March.  ASEE Conferences, 2024.  https://peer.asee.org/45560 Internet.  21 Jan, 2025

\bibitem{asee_peer_45560}
  David Calamas.
  "Student and Instructor Feedback on an AI-Assisted Grading Tool".
  \emph{2024 South East Section Meeting, Marietta, Georgia, 2024, March}.
  ASEE Conferences, 2024.
  https://peer.asee.org/45560 Internet.  21 Jan, 2025

@INPROCEEDINGS{asee_peer_45560
author = "David Calamas"
title = "Student and Instructor Feedback on an AI-Assisted Grading Tool"
booktitle = "2024 South East Section Meeting"
year = "2024"
month = "March"
address = "Marietta, Georgia"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/45560}
number = {10.18260/1-2--45560}
}

TY  - CPAPER
AB  - A browser-based AI-assisted grading tool was deployed in an undergraduate mechanical engineering course, offering the instructor online grading capabilities, time savings, a transparent and unbiased evaluation process, personalized feedback, and detailed analytics for student performance visualization. A Likert-scale-based survey instrument gauged responses from a cohort of undergraduate mechanical engineering students, probing the efficiency of Gradescope, grading transparency, bias reduction, and overall satisfaction. Preliminary analysis indicates positive sentiment, with students expressing satisfaction with the speed and transparency of grading. The platform's ability to provide detailed feedback on problem-solving steps emerged as a significant benefit, contributing to a more standardized and unbiased grading experience. This study offers nuanced insights into student perspectives on technology-enhanced grading tools, contributing to discussions on digital platforms in academia.
AU  - David Calamas
CY  - Marietta, Georgia
DA  - 2024/03/10
PB  - ASEE Conferences
TI  - Student and Instructor Feedback on an AI-Assisted Grading Tool
UR  - https://peer.asee.org/45560
DO  - 10.18260/1-2--45560
ER  -