Determinants of Teachers’ Acceptance of an AI-Powered Essay Assessment System: A TAM Study of Esygrade in Economics Education
DOI:
https://doi.org/10.23960/E3J/v9.i1.9-16Keywords:
Perceived Usefulness, Attitude Toward Use, Behavioral IntentionAbstract
This study examines teachers’ acceptance of Esygrade.com, an AI powered automated essay assessment system designed to support more efficient and consistent evaluation of students’ conceptual understanding in economics education. Manual essay scoring remains highly time consuming and prone to inter rater inconsistency, prompting increased interest in Automated Essay Scoring (AES) and large language model-based assessment tools. However, despite rapid technological advancements, teacher acceptance of AI assessment systems remains underexplored. Using the Technology Acceptance Model, this study analyzes how perceived ease of use, perceived usefulness, and attitude shape teachers behavioral intention to adopt Esygrade.com. A quantitative research design was employed with 30 senior high school economics teachers who explored the system before completing a validated questionnaire adapted from recent AI TAM studies. Data were analyzed using Partial Least Squares Structural Equation Modeling. The results show that perceived ease of use strongly influences both perceived usefulness and attitude, positioning usability as the most critical factor in teacher’s evaluations of AI supported assessment. Perceived usefulness significantly affects attitude but does not directly predict behavioral intention, while attitude emerges as the strongest determinant of intention to use the system. These findings indicate that teacher’s willingness to adopt AI powered assessment tools depends not only on their functional benefits but also on positive affective evaluations and user readiness. The study provides theoretical insight into AI acceptance in assessment contexts and practical guidance for developers and institutions seeking to integrate AI based scoring systems into classroom practice.
Downloads
References
Barshay, J. (2024). AI Essay Grading Could Help Overburdened Teachers, But Researchers Say It Needs More Work. The Hechinger Report. https://www.kqed.org/mindshift/63809/ai-essay-grading-could-help-overburdened-teachers-but-researchers-say-it-needs-more-work?
Conijn, R., Kahr, P., & Snijders, C. (2023). The Effects of Explanations in Automated Essay Scoring Systems on Student Trust and Motivation. Journal of Learning Analytics, 10(1), 37–53. https://doi.org/10.18608/jla.2023.7801
F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
Grecea Pasaribu, N., Budiman, G., & Dyah Irawati, I. (2024). Auto Evaluation for Essay Assessment Using a 1D Convolutional Neural Network. IEEE Access, 12, 188217–188230. https://doi.org/10.1109/ACCESS.2024.3515837
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Evaluation of Formative Measurement Models (pp. 91–113). https://doi.org/10.1007/978-3-030-80519-7_5
Ireland, D. (2025). A third of US teachers considered leaving education in last 12 months due to grading workload. Learnosity. https://learnosity.com/edtech-blog/a-third-of-us-teachers-considered-leaving-education-in-last-12-months-due-to-grading-workload/
Jauhiainen, J. S., & Garagorry Guerra, A. (2024). Generative AI in education: ChatGPT-4 in evaluating students’ written responses. Innovations in Education and Teaching International, 1–18. https://doi.org/10.1080/14703297.2024.2422337
Khong, H., Celik, I., Le, T. T. T., Lai, V. T. T., Nguyen, A., & Bui, H. (2023). Examining teachers’ behavioural intention for online teaching after COVID-19 pandemic: A large-scale survey. Education and Information Technologies, 28(5), 5999–6026. https://doi.org/10.1007/s10639-022-11417-6
Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, 100328. https://doi.org/10.1016/j.caeai.2024.100328
Koppel, S., Logan, D. B., Zou, X., Kaviani, F., McDonald, H., Hair Jr, J. F., St. Louis, R. M., Molnar, L. J., & Charlton, J. L. (2024). Factors influencing behavioral intentions to use conditionally automated vehicles. Journal of Safety Research, 91, 423–430. https://doi.org/10.1016/j.jsr.2024.10.006
Li, S., & Ng, V. (2024). Automated essay scoring: recent successes and future directions. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2024/897
Mizumoto, A., & Eguchi, M. (2023). Exploring the potential of using an AI language model for automated essay scoring. Research Methods in Applied Linguistics, 2(2), 100050. https://doi.org/10.1016/j.rmal.2023.100050
Smerdon, D. (2024). AI in essay-based assessment: Student adoption, usage, and performance. Computers and Education: Artificial Intelligence, 7, 100288. https://doi.org/10.1016/j.caeai.2024.100288
Tang, T. T., Nguyen, T. N., & Tran, H. T. T. (2022). Vietnamese Teachers’ Acceptance to Use E-Assessment Tools in Teaching: An Empirical Study Using PLS-SEM. Contemporary Educational Technology, 14(3), ep375. https://doi.org/10.30935/cedtech/12106
Valle, N. N., Kilat, R. V., Lim, J., General, E., Dela Cruz, J., Colina, S. J., Batican, I., & Valle, L. (2024). Modeling learners’ behavioral intention toward using artificial intelligence in education. Social Sciences & Humanities Open, 10, 101167. https://doi.org/10.1016/j.ssaho.2024.101167
Xie, S., & Liao, F. (2024). Incorporating personality traits for the study of user acceptance of electric micromobility-sharing services. Transportation Research Part F: Traffic Psychology and Behaviour, 107, 1015–1030. https://doi.org/10.1016/j.trf.2024.10.023
