GAI Versus Teacher Scoring: Which is Better for Assessing Student Performance?

The integration of Generative Artificial Intelligence (GAI) into educational settings offers unprecedented opportunities to enhance the efficiency of teaching and the effectiveness of learning, particularly within online platforms. This study evaluates the development and application of a customized GAI-powered teaching assistant, trained specifically to enhance teaching efficiency for educators and improve learning outcomes for students in online education. Using four Grade 12 courses (i.e., English, Mathematics, Financial Accounting, and Simplified Chinese), we assessed the performance of GPT-4, GPT-4o, and the Trained-GPT model. Results demonstrate that the Trained-GPT achieved grading accuracy and consistency comparable to human teachers, with strong correlations observed in Mathematics (0.996) and English (0.874). While GPT-4o performed well in specific cases, its variability highlights areas for improvement. These findings underscore the potential of AI-powered teaching assistants to streamline grading, deliver timely feedback, and support scalable, high-quality online education

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Learning Technologies
Published
2025-01-01
DOI
https://doi.org/10.1109/tlt.2025.3572518
Citations
2
Primary Topic
Education and Technology Integration
Type
article
Field-Weighted Citation Impact
17.85
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

GAI Versus Teacher Scoring: Which is Better for Assessing Student Performance?

Sining Tao, Tingsong Li, Naiqing Guan, Marco Zappatore et al.
2 citations
IEEE Transactions on Learning Technologies
Education and Technology Integration
17.85
article

GAI Versus Teacher Scoring: Which is Better for Assessing Student Performance?

Sining Tao, Tingsong Li, Naiqing Guan, Marco Zappatore, Xiaoxu Zhou, X.R. Li, Xiaoqing Wei, Weiwei Zhang, A.W. Chan
article en
2 citations

Abstract

The integration of Generative Artificial Intelligence (GAI) into educational settings offers unprecedented opportunities to enhance the efficiency of teaching and the effectiveness of learning, particularly within online platforms. This study evaluates the development and application of a customized GAI-powered teaching assistant, trained specifically to enhance teaching efficiency for educators and improve learning outcomes for students in online education. Using four Grade 12 courses (i.e., English, Mathematics, Financial Accounting, and Simplified Chinese), we assessed the performance of GPT-4, GPT-4o, and the Trained-GPT model. Results demonstrate that the Trained-GPT achieved grading accuracy and consistency comparable to human teachers, with strong correlations observed in Mathematics (0.996) and English (0.874). While GPT-4o performed well in specific cases, its variability highlights areas for improvement. These findings underscore the potential of AI-powered teaching assistants to streamline grading, deliver timely feedback, and support scalable, high-quality online education

IEEE Transactions on Learning TechnologiesVol. 18
Boston University (US), Harvard University (US), University of Salento (IT), Shanghai Jiao Tong University (CN), Northeast Normal University (CN), University of Toronto (CA), Institute for Christian Studies (CA)
Openalex Percentile: Top 1%
Education and Technology Integration
17.85
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

GAI Versus Teacher Scoring: Which is Better for Assessing Student Performance? — Sining Tao, Tingsong Li, et al. · IEEE Transactions on Learning Technologies (2025) | TGRS Research Map | TGRS