Designing Explainable Machine Learning Models for Automated Essay Scoring with Interpretable Feedback in English Language Evaluation
Abstract
This work investigates the development of an explainable framework for Automated Essay Scoring (AES) that integrates advanced natural language processing (NLP) techniques with explainable artificial intelligence (XAI) to enhance transparency and pedagogical value in educational assessment. Automated Essay Scoring (AES) has emerged as a critical application of NLP in educational assessment, enabling scalable and efficient evaluation of written responses. However, most high-performing AES systems rely on complex machine learning models that operate as “black boxes,” limiting transparency and trust among educators and learners. This study proposes a framework for designing explainable machine learning models for AES that provide interpretable, fine-grained feedback in English language evaluation. By integrating XAI techniques such as SHAP, attention visualization, and linguistic feature attribution with deep learning architectures, the proposed system enhances both scoring accuracy and pedagogical usefulness. Experimental results demonstrate that the model achieves competitive scoring performance while offering meaningful explanations aligned with rubric-based evaluation. The findings highlight the importance of interpretability in improving trust, fairness, and learning outcomes in automated assessment systems.
References
M. D. Shermis, “Anchoring validity evidence for automated essay scoring,” Journal of Educational Measurement, vol. 59, no. 3, pp. 314–337, May 2022.
S. Ferrara and S. Qunbar, “Validity arguments for AI‐based automated scores: Essay scoring as an illustration,” Journal of Educational Measurement, vol. 59, no. 3, pp. 288–313, Jun. 2022.
R. Conijn, P. Kahr and C. Snijders, “The effects of explanations in automated essay scoring systems on student trust and motivation,” Journal of Learning Analytics, vol. 10, no. 1, pp. 37–53, 2023.
K. K. Chan, T. Bond, and Z. Yan, “Application of an automated essay scoring engine to English writing assessment using many-facet Rasch measurement,” Language Testing, vol. 40, no. 1, 2023.
T. C. Stephen, M. C. Gierl, and S. King, “Automated essay scoring (AES) of constructed responses in nursing examinations: An evaluation,” Nurse Education in Practice, vol. 54, Jul. 2021.
C. M Ormerod, A. Malhotra, and A. Jafari, “Automated essay scoring using efficient transformer-based language models,” arXiv, Feb. 2021.
S. Ludwig, C. Mayer, C. Hansen, K. Eilers, and S. Brandt, “Automated essay scoring using transformer models,” Psych, vol. 3, no. 4, pp. 897–915, Dec. 2021.
K. Doi, K. Sudoh, and S. Nakamura, “Automated essay scoring using grammatical variety and errors with multi-task learning and item response theory,” in Proceedings of 19th Workshop Innovative Use of NLP Building Educational Applications (BEA 2024), Mexico City, Mexico, Jun. 2024, pp. 316–329. Piscataway, NJ, USA: Association for Computational Linguistics.
A. B. Arrieta, “Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, vol. 58, pp. 82–115, Jun. 2020.
D. V. Carvalho, E. M. Pereira, and J. S. Cardoso, “Machine learning interpretability: A survey on methods and metrics,” Electronics, vol. 8, no. 8, Jul. 2019.
R. Guidotti, A. Monreale, S. Ruggieri, Fr. Turini, F. Giannotti, and D. Pedreschi, “A survey of methods for explaining black box models,” ACM computing surveys (CSUR), vol. 51, no. 5, pp. 1–42, Aug. 2018.
C. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, 2nd ed. Munich, Germany: Christoph Molnar, 2022.
M. T. Ribeiro, S. Singh, and C. Guestrin, ““Why should i trust you?” Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2016, pp. 1135–1144.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, MN, USA, Jun. 2019, pp. 4171–4186. Association for Computational Linguistics.
T. B. Brown et al., “Language models are few-shot learners,” Proceedings of the 34th International Conference on Neural Information Processing Systems, Dec. 2020, pp. 1877–1901.
A. Vaswani et al., “Attention is all you need,” Proceedings of the 31st International Conference on Neural Information Processing Systems, Dec. 2017, pp. 6000–6010.
Y. Liu et al., “RoBERTa: A robustly optimized BERT pretraining approach,” arXiv, Jul. 2019.
T. Wolf et al., “Transformers: State-of-the-art natural language processing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Online, Oct. 2020, pp. 38–45. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” Proceedings of the 33rd International Conference on Neural Information Processing Systems, Dec. 2019, pp. 5753–5763.
P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” Proceedings of the 34th International Conference on Neural Information Processing Systems, Dec. 2020, pp. 9459–9474.
F. Doshi-Velez and B. Kim, “Towards a rigorous science of interpretable machine learning,” arXiv, Feb. 2017.
S. Raschka, Y. Liu, V. Mirjalili, and J. Dzhulgakov, Machine Learning with PyTorch and Scikit-Learn: Develop Machine Learning and Deep Learning Models with Python, Birmingham, U.K.: Packt Publishing, 2022.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, updated ed. Cambridge, MA, USA: MIT Press, 2021.
Y. Tay, M. Dehghani, D. Bahri, and D. Metzler, “Efficient transformers: A survey,” ACM Computing Surveys, vol. 55, no. 6, Dec. 2022.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.
T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,” Artificial Intelligence, vol. 267, pp. 1–38, Feb. 2019.
B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, and R. sayres, “Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV),” Proceedings of the 35th International Conference on Machine Learning, 2018, pp. 2668–2677.
S. Wang et al., “Large language models for education: A survey and outlook,” in IEEE Signal Processing Magazine, vol. 42, no. 6, pp. 51–63, Nov. 2025.
Z. M. Altukhi and S. Pradhan, “Systematic literature review: Explainable AI definitions and challenges in education,” arXiv, Apr. 2025.
D. Dunning and D. W. Aha, “DARPA’s explainable artificial intelligence program,” AI magazine, vol. 40, no. 2, pp. 44–58, Jun. 2019.
