Najafi, F. T., & Pabba, V. R., & Subramanian, R., & Vidalis, S. M. (2025, March), AI-Assisted Grading – A Study on Efficiency and Fairness  Paper presented at 2025 ASEE Southeast Conference , Mississippi State University, Mississippi. 10.18260/1-2--54139

\bibitem{asee_peer_54139}
  Najafi, F. T., \& Pabba, V. R., \& Subramanian, R., \& Vidalis, S. M. (2025, March), \emph{AI-Assisted Grading – A Study on Efficiency and Fairness}
  Paper presented at 2025 ASEE Southeast Conference , Mississippi State University, Mississippi.
  10.18260/1-2--54139

Fazil T. Najafi, Vani Ruchika Pabba, Rajarajan Subramanian, and Sofia M Vidalis.  "AI-Assisted Grading – A Study on Efficiency and Fairness".  2025 ASEE Southeast Conference , Mississippi State University, Mississippi, 2025, March.  ASEE Conferences, 2025.  https://peer.asee.org/54139 Internet.  20 Apr, 2025

\bibitem{asee_peer_54139}
  Fazil T. Najafi, Vani Ruchika Pabba, Rajarajan Subramanian, and Sofia M Vidalis.
  "AI-Assisted Grading – A Study on Efficiency and Fairness".
  \emph{2025 ASEE Southeast Conference , Mississippi State University, Mississippi, 2025, March}.
  ASEE Conferences, 2025.
  https://peer.asee.org/54139 Internet.  20 Apr, 2025

@INPROCEEDINGS{asee_peer_54139
author = "Fazil T. Najafi, Vani Ruchika Pabba, Rajarajan Subramanian, and Sofia M Vidalis"
title = "AI-Assisted Grading – A Study on Efficiency and Fairness"
booktitle = "2025 ASEE Southeast Conference "
year = "2025"
month = "March"
address = "Mississippi State University, Mississippi"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/54139}
number = {10.18260/1-2--54139}
}

TY  - CPAPER
AB  - The adoption of AI-assisted grading systems in education is gaining attention due to their potential to streamline assessment processes. While AI has proven effective in automating objective tests, its application to theoretical and research-based assignments is underexplored. This study investigates the efficiency and fairness of using AI, specifically ChatGPT, to grade theoretical understanding and research paper assignments in undergraduate and graduate courses. We will conduct the research in two phases. In the first phase, we will assess ChatGPT's performance in grading assignments, focusing on time efficiency, consistency, and grading patterns. We will compare AI-assisted and traditional human grading methods in the second phase. We will analyze score variations, potential biases, and feedback's perceived usefulness. We will conduct surveys to gather perceptions from both students and educators regarding AI-based grading.
We anticipate that AI-assisted grading will significantly reduce grading time and provide more consistent feedback, especially for assignments with clear rubrics. However, challenges may arise in handling nuanced or subjective responses, such as those in research papers, leading to concerns about fairness and bias in grading. These limitations highlight the need to investigate AI's capabilities further in evaluating complex academic work. This research contributes to the growing conversation on AI's role in education, particularly its potential to automate grading for complex assignments. The findings will offer insights into the benefits and drawbacks of AI in educational settings, providing valuable guidance for educators considering AI integration to enhance efficiency while maintaining fairness in grading practices.

AU  - Fazil T. Najafi
AU  - Vani Ruchika Pabba
AU  - Rajarajan Subramanian
AU  - Sofia M Vidalis
CY  - Mississippi State University, Mississippi
DA  - 2025/03/09
PB  - ASEE Conferences
TI  - AI-Assisted Grading – A Study on Efficiency and Fairness
UR  - https://peer.asee.org/54139
DO  - 10.18260/1-2--54139
ER  -