TRANSFORMATIVE ASSESSMENT IN ACADEMIC WRITING: AI-BASED FEEDBACK SYSTEMS WITH ADAPTIVE RUBRICS

Emad Ali Alawad

DOI Number
https://doi.org/10.22190/JTESAP250113012A
First page
141
Last page
149

Abstract


This study analyzed the effect of AI feedback on students' writing skills, providing reliable and impartial assessments. The study assessed the capability of artificial intelligence to provide feedback and conducted a comparative analysis with conventional methods employed for enhancing student writing skills. Sixty-three students in the Academic Writing course at the Modern College of Business Science were selected as the sample, of which 14 actively participated in focused discussions. A combination of pre-tests, post-tests, and rubrics aligned with course objectives served as the primary research tools, ensuring the feedback remained relevant. One group received feedback based on AI, while another received traditional feedback. Additionally, qualitative data was collected through focused discussions to gain deeper insights. The results suggest combining adaptive rubrics with AI-based feedback can enhance assessment methods and retention. These findings are expected to significantly contribute to the future use of AI in education, as there is substantial potential to transform feedback and assessment across various educational contexts.


Keywords

AI-based feedback, writing skills, educational assessment, adaptive rubrics

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References


Almegren, A., Mahdi, H. S., Hazaea, A. N., Ali, J. K., & Almegren, R. M. (2024). Evaluating the quality of AI feedback: A comparative study of AI and human essay grading. Innovations in Education and Teaching International, 1–16. https://doi.org/10.1080/14703297.2024.2437122

Álvarez Valdivia, I. M. (2021). Rúbrica para evaluar la redacción científica argumentativa. Available online: http://ddd.uab.cat/record/214565.

Álvarez Valdivia, I. M., & Lafuente Martínez, M. (2018). Improving preservice teachers’ scientific argumentative writing through epistemic practices: A learning progression approach. Journal of Education and Teaching, 45(2), 169–185. https://doi.org/10.1080/02607476.2018.1422611

Austin, T., Rawal, B., Diehl, A., & Cosme, J. (2023). AI for equity: Unpacking potential human bias in higher education decision-making. AI, Computer Science and Robotics Technology, 2023(2), 117. https://doi.org/10.5772/acrt.20

Bouziane, K., Bouziane, A. AI versus human effectiveness in essay evaluation. Discov Educ 3, 201 (2024). https://doi.org/10.1007/s44217-024-00320-6

Brown, G. T. L., Glasswell, K., & Harland, D. (2004). Accuracy in writing scoring: Studies of reliability and validity using a New Zealand writing assessment system. Assessing Writing, 9(2), 105–121. https://doi.org/10.1016/j.asw.2004.07.001

Conway, J. (2024). The evolution of rubrics with AI integration. Journal of Educational Technology, 15(3), 45–60.

Correnti, R., Matsumura, L. C., Wang, E. L., Litman, D., & Zhang, H. (2022). Building a validity argument for an automated writing evaluation system (eRevise) as a formative assessment. Computers and Education Open, 3, 1–15. https://doi.org/https://doi.org/10.1016/j.caeo.2022.100084

Devi, S., Boruah, A. S., Nirban, S., Nimavat, D., & Bajaj, K. K. (2023). Ethical considerations in using artificial intelligence to improve teaching and learning. Tuijin Jishu/Journal of Propulsion Technology, 44(4), 10311038. https://doi.org/10.52783/tjjpt.v44.i4.966

Fahmy, A. (2024). Student perceptions of AI-driven assessment: Motivation, engagement, and feedback capabilities. Educational Assessment Review, 12(2), 78–95.

Guo, H. (2024). AI-enhanced feedback generation in peer assessment for writing through EvaluMate. International Journal of AI in Education, 29(1), 112–130.

Guo, H., Pan, Y., Li, X., & Lai, W. (2024). Impact of AI-supported peer feedback on EFL student reviewers. Language Learning & Technology, 28(4), 67–85.

Guo, H., Zhang, L., Pan, Y., & Li, X. (2024). EvaluMate: An AI-supported peer review system. Computers & Education, 105, 123–140.

Hamon, R., Junklewitz, H., Sanchez, I., Malgieri, G., & De Hert, P. (2022). Bridging the gap between AI and explainability in the GDPR: Towards trustworthiness-by-design in automated decision-making. IEEE Computational Intelligence Magazine, 17(1), 72-78.

Hong, W. C. H. (2023). The impact of ChatGPT on foreign language teaching and learning: Opportunities in education and research. Journal of Educational Technology and Innovation, 5(1), 37-45.

Hooda, M., Singh, R., & Sharma, P. (2022). Immediate and valid feedback through AI in higher education. Journal of Machine Learning in Education, 10(4), 234–250.

Kakungulu Samuel, J. (2025). AI's transformative impact on educational assessment. Educational Technology Research and Development, 33(1), 15-30.

Kassorla, M. (2024, March 8). Assessment of student writing in the age of AI: When organization, grammar, and punctuation are correct, what is left?

Kennedy, E., & Shiel, G. (2022). Writing assessment for communities of writers: rubric validation to support formative writing assessment in Pre-K to grade 2. Assessment in Education: Principles, Policy & Practice, 29(2), 127–149.

https://doi.org/10.1080/0969594X.2022.2047608

Knight, A. (2009). A method for collaboratively developing and validating a rubric. International Journal for the Scholarship of Teaching and Learning, 3(1), 10.

Llamas-Nistal, M., Fernández-Iglesias, M. J., González-Tato, J., & Mikic-Fonte, F. A. (2013). Blended e-assessment: Migrating classical exams to the digital world. Computers & Education, 62, 72-87.

Mahamuni, A., Parminder, S., & Tonpe, R. (2025). Integrating AI technologies into the assessment process. Journal of Educational Assessment, 18(1), 98-115.

Olson, J., & Krysiak, R. (2021). Rubrics as tools for effective assessment of student learning and program quality. In Curriculum Development and Online Instruction for the 21st Century (Chapter 10). https://doi.org/10.4018/978-1-7998-7653-3.ch010

Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W. J., Sun, M., Day, I., Rather, R. A., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning & Teaching, 6(1), 1-16

Pang, Y., Koutsoubos, A., & Cheng, L. (2024). AI-assisted feedback tools in higher education. Higher Education Research & Development, 43(2), 210–225.

Sundari, R., Patel, M., & Kumar, S. (2025). Effectiveness of AI-enabled assessment methods. Indian Journal of Educational Technology, 22(1), 55–70.

Talan, T., & Kalinkara, Y. (2023). The role of artificial intelligence in higher education: ChatGPT assessment for anatomy course. International Journal of Management Information Systems and Computer Science, 7(1), 3340.

https://doi.org/10.33461/uybisbbd.1244777

The Role of Artificial Intelligence in Educational Assessment. (2025). Retrieved from ResearchGate.

U.S. Department of Education, Office of Educational Technology. (2023). Artificial Intelligence and the Future of Teaching and Learning.

Wang, X., He, X., Wei, J., Liu, J., Li, Y., & Liu, X. (2023). Application of artificial intelligence to public health education. Frontiers in https://doi.org/10.3389/fpubh.2022.1087174

Zhu, Y., Zhang, H., Zong, W., & Sun, L. (2024). Real-time feedback on English writing using ChatGPT. Journal of Language and Education, 19(3), 89–105.




DOI: https://doi.org/10.22190/JTESAP250113012A

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