AI-assisted marking: Functionality and limitations of ChatGPT in written assessment evaluation

Authors

  • Joan Li University of Queensland
  • Nikhil Kumar Jangamreddy School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
  • Ryuto Hisamoto School of Electrical Engineering and Computer Science /Business School, The University of Queensland, Brisbane, Australia
  • Ruchita Bhansali School of Public Health, The University of Queensland, Brisbane, Australia
  • Amalie Dyda School of Public Health, The University of Queensland, Brisbane, Australia
  • Luke Zaphir Institute for Teaching and Learning Innovation (ITaLI), The University of Queensland, Brisbane, Australia
  • Mashhuda Glencross School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia

DOI:

https://doi.org/10.14742/ajet.9463

Keywords:

higher education, written assessment, marking, feedback practice, generative AI

Abstract

Generative artificial intelligence technologies, such as ChatGPT, bring an unprecedented change in education by leveraging the power of natural language processing and machine learning.  Employing ChatGPT to assist with marking written assessment presents multiple advantages including scalability, improved consistency, eliminating biases associated with human subjectivity. This work aimed to evaluate the usefulness, reliability and accuracy of ChatGPT in marking written assessments of varied types and to identify its limitations and challenges. ChatGPT was instructed using a set of prompts to mark the assessment based on a rubric. ChatGPT was able to evaluate and assess both coding and reflective assessments and to distinguish between assignments of different quality, demonstrating high consistency and accuracy for higher quality assessments, comparable to a human marking. ChatGPT was also able to generate textual detailed justifications based on the rubric and assessment task description. There was a significant difference in the outcomes generated by different prompts. These preliminary findings suggest that utilising ChatGPT as a marking assistant can increase written assessment marking efficiency, reduce cost and potentially decrease the unfairness and bias by providing a moderating perspective.

 

Implications for practice or policy:

  • Assessment designers could reconsider the design, purpose and objectives of written assessments and leverage ChatGPT effectively for teaching and learning.
  • Assessors might consider adapting the technology as a grading aid, to support a human-in-the-loop grading process, providing additional resources and time, moderating and refining individual feedback, to increase consistency and quality.
  • Curriculum and programme leaders could develop guidelines around the ethical use of generative AI-assisted assessment practice, monitor and regulate the ongoing evaluation and refinement.

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Published

2024-10-09

How to Cite

Li, J., Jangamreddy, N. K. ., Hisamoto, R. ., Bhansali, R. ., Dyda, A. ., Zaphir, L. ., & Glencross, M. . (2024). AI-assisted marking: Functionality and limitations of ChatGPT in written assessment evaluation. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.9463

Issue

Section

Special Issue 2024 - Advancements in Technology-Enhanced Assessment in Higher Education