ARCHIVES
Original Article
AI-Based Question Paper Generation and Quality Optimization: An Integrated Computational Approach to Intelligent Assessment Construction
Shubham M. Koshti1
Dr. Dhanpal N. Waghulde2
Dr. Yogesh N. Chaudhari3
Harshal B. Patil4
Rita P. Kurkure5
1 3 4 5 Assistant Professor, KCES’s Institute of Management and Research, Jalgaon, Maharashtra, India. 2 Associate Professor, KCES’s Institute of Management and Research, Jalgaon, Maharashtra, India.
Published Online: May-August 2026
Pages: 753-760
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502085References
1. Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's educational
objectives. Addison Wesley Longman.
2. Agarwal, P., Shah, M., & Bhatt, C. (2019). Neural question generation from reading passages using BERT and bidirectional attention.
Proceedings of the ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD 2019), 256–
263.
3. Baker, F. B., & Kim, S. H. (2004). Item response theory: Parameter estimation techniques (2nd ed.). Marcel Dekker.
4. Benedetto, L., Crammer, K., & Cohn, T. (2021). Introducing neural IRT for calibrating newly authored test items. Proceedings of the 11th
International Conference on Learning Analytics and Knowledge (LAK '21), 361–370.
5. Brown, T., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems,
33, 1877–1901.
6. Chen, Z., Wang, L., & Liu, Q. (2023). Graph neural networks for curriculum-aware examination content structuring. IEEE Transactions
on Knowledge and Data Engineering, 35(8), 8120–8135.
7. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language
understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
8. Du, X., Shao, J., & Cardie, C. (2017). Learning to ask: Neural question generation for reading comprehension. Proceedings of ACL 2017,
1342–1352.
9. Gao, Y., Bing, L., Li, P., King, I., & Lyu, M. R. (2021). Difficulty controllable question generation from knowledge graphs.
arXiv:2101.06295.
10. Gierl, M. J., Lai, H., & Turner, S. R. (2012). Using automatic item generation to create multiple-choice items for assessing medical
knowledge. Medical Education, 46(8), 757–765.
11. Heilman, M., & Smith, N. A. (2010). Good question! Statistical ranking for question generation. Proceedings of NAACL-HLT 2010, 609–
617.
12. Huang, Z., Liu, Q., Chen, E., Zhao, H., Gao, M., Wei, S., Su, Y., & Hu, G. (2017). Question difficulty prediction for reading problems in
standard tests. Proceedings of the 31st AAAI Conference on Artificial Intelligence, 1352–1359.
13. Kim, D., Jeong, H., & Park, J. (2022). Cognitive bias detection in examination items using transformer-based natural language inference.
Computers & Education: Artificial Intelligence, 3, 100059.
14. Lalor, J. P., Wu, H., & Yu, H. (2019). Learning latent parameters without human response patterns: IRT with artificial crowds. Proceedings
of EMNLP 2019, 4240–4250.
15. Li, X., Zhang, H., & Zhou, Y. (2022). Topic-modelling-based syllabus coverage analysis for automated examination evaluation. Computers
& Education, 178, 104405.
16. Liu, Y., Ott, M., Goyal, N., et al. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv:1907.11692.
17. Lord, F. M. (1980). Applications of item response theory to practical testing problems. Lawrence Erlbaum Associates.
18. Mitkov, R., & Ha, L. A. (2003). Computer-aided generation of multiple-choice tests. Proceedings of the HLT-NAACL 2003 Workshop on
Building Educational Applications Using NLP, 17–22.
19. OpenAI. (2023). GPT-4 technical report. arXiv:2303.08774.
20. Peng, J., Wang, L., & Zhao, Y. (2023). Holistic examination quality assessment using multi-dimensional AI evaluation: A large-scale
empirical study. Expert Systems with Applications, 214, 119151.
21. Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., & Finn, C. (2023). Direct preference optimization: Your language model
is secretly a reward model. Advances in Neural Information Processing Systems, 36.
22. Rodriguez, C., Gutierrez, F., & Deco, C. (2021). Knowledge graph-based curriculum alignment and examination coverage evaluation.
IEEE Transactions on Learning Technologies, 14(4), 501–514.
23. Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009). The graph neural network model. IEEE Transactions on
Neural Networks, 20(1), 61–80.
24. Settles, B., LaFlair, G. T., & Hagiwara, M. (2020). Machine learning-driven language assessment. Transactions of the Association for
Computational Linguistics, 8, 247–263.25. Su, Y., Liu, Q., Liu, Q., et al. (2018). Exercise-enhanced sequential modeling for student performance prediction. Proceedings of AAAI
2018, 2435–2443.
26. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
27. Touvron, H., Martin, L., Stone, K., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288.
28. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
29. Wei, J., Bosma, M., Zhao, V. Y., et al. (2022). Finetuned language models are zero-shot learners. Proceedings of ICLR 2022.
30. Yahya, A. A., & Osman, A. (2012). Automatic classification of questions in Bloom's taxonomy based on question structure. International
Journal of Engineering Research and Technology, 1(3), 1–6.
31. Yuan, Z., Liu, X., Zhao, Y., & Xu, J. (2023). EduQG: A multi-format question generation dataset for the education domain. IEEE Access,
11, 20358–20373.
32. Zhang, L., Li, X., & Xing, W. (2024). Automated item generation and difficulty calibration for adaptive testing: A generative AI
perspective. Computers & Education: Artificial Intelligence, 6, 100192.
33. Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C. M., & Eger, S. (2023). MoverScore: Text generation evaluating with contextualized
embeddings and earth mover distance. Proceedings of EMNLP 2023, 563–578.
34. Zhou, Y., Liu, H., & Jiang, Y. (2022). Adaptive learning pathway construction using reinforcement learning and knowledge graphs. IEEE
Transactions on Learning Technologies, 15(3), 320–334.
35. Zhu, M., Su, Y., & Chen, E. (2023). Fairness-aware automatic question generation with debiased language model prompting. Proceedings
of the 16th International Conference on Educational Data Mining (EDM 2023), 190–200.
objectives. Addison Wesley Longman.
2. Agarwal, P., Shah, M., & Bhatt, C. (2019). Neural question generation from reading passages using BERT and bidirectional attention.
Proceedings of the ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD 2019), 256–
263.
3. Baker, F. B., & Kim, S. H. (2004). Item response theory: Parameter estimation techniques (2nd ed.). Marcel Dekker.
4. Benedetto, L., Crammer, K., & Cohn, T. (2021). Introducing neural IRT for calibrating newly authored test items. Proceedings of the 11th
International Conference on Learning Analytics and Knowledge (LAK '21), 361–370.
5. Brown, T., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems,
33, 1877–1901.
6. Chen, Z., Wang, L., & Liu, Q. (2023). Graph neural networks for curriculum-aware examination content structuring. IEEE Transactions
on Knowledge and Data Engineering, 35(8), 8120–8135.
7. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language
understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
8. Du, X., Shao, J., & Cardie, C. (2017). Learning to ask: Neural question generation for reading comprehension. Proceedings of ACL 2017,
1342–1352.
9. Gao, Y., Bing, L., Li, P., King, I., & Lyu, M. R. (2021). Difficulty controllable question generation from knowledge graphs.
arXiv:2101.06295.
10. Gierl, M. J., Lai, H., & Turner, S. R. (2012). Using automatic item generation to create multiple-choice items for assessing medical
knowledge. Medical Education, 46(8), 757–765.
11. Heilman, M., & Smith, N. A. (2010). Good question! Statistical ranking for question generation. Proceedings of NAACL-HLT 2010, 609–
617.
12. Huang, Z., Liu, Q., Chen, E., Zhao, H., Gao, M., Wei, S., Su, Y., & Hu, G. (2017). Question difficulty prediction for reading problems in
standard tests. Proceedings of the 31st AAAI Conference on Artificial Intelligence, 1352–1359.
13. Kim, D., Jeong, H., & Park, J. (2022). Cognitive bias detection in examination items using transformer-based natural language inference.
Computers & Education: Artificial Intelligence, 3, 100059.
14. Lalor, J. P., Wu, H., & Yu, H. (2019). Learning latent parameters without human response patterns: IRT with artificial crowds. Proceedings
of EMNLP 2019, 4240–4250.
15. Li, X., Zhang, H., & Zhou, Y. (2022). Topic-modelling-based syllabus coverage analysis for automated examination evaluation. Computers
& Education, 178, 104405.
16. Liu, Y., Ott, M., Goyal, N., et al. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv:1907.11692.
17. Lord, F. M. (1980). Applications of item response theory to practical testing problems. Lawrence Erlbaum Associates.
18. Mitkov, R., & Ha, L. A. (2003). Computer-aided generation of multiple-choice tests. Proceedings of the HLT-NAACL 2003 Workshop on
Building Educational Applications Using NLP, 17–22.
19. OpenAI. (2023). GPT-4 technical report. arXiv:2303.08774.
20. Peng, J., Wang, L., & Zhao, Y. (2023). Holistic examination quality assessment using multi-dimensional AI evaluation: A large-scale
empirical study. Expert Systems with Applications, 214, 119151.
21. Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., & Finn, C. (2023). Direct preference optimization: Your language model
is secretly a reward model. Advances in Neural Information Processing Systems, 36.
22. Rodriguez, C., Gutierrez, F., & Deco, C. (2021). Knowledge graph-based curriculum alignment and examination coverage evaluation.
IEEE Transactions on Learning Technologies, 14(4), 501–514.
23. Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009). The graph neural network model. IEEE Transactions on
Neural Networks, 20(1), 61–80.
24. Settles, B., LaFlair, G. T., & Hagiwara, M. (2020). Machine learning-driven language assessment. Transactions of the Association for
Computational Linguistics, 8, 247–263.25. Su, Y., Liu, Q., Liu, Q., et al. (2018). Exercise-enhanced sequential modeling for student performance prediction. Proceedings of AAAI
2018, 2435–2443.
26. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
27. Touvron, H., Martin, L., Stone, K., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288.
28. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
29. Wei, J., Bosma, M., Zhao, V. Y., et al. (2022). Finetuned language models are zero-shot learners. Proceedings of ICLR 2022.
30. Yahya, A. A., & Osman, A. (2012). Automatic classification of questions in Bloom's taxonomy based on question structure. International
Journal of Engineering Research and Technology, 1(3), 1–6.
31. Yuan, Z., Liu, X., Zhao, Y., & Xu, J. (2023). EduQG: A multi-format question generation dataset for the education domain. IEEE Access,
11, 20358–20373.
32. Zhang, L., Li, X., & Xing, W. (2024). Automated item generation and difficulty calibration for adaptive testing: A generative AI
perspective. Computers & Education: Artificial Intelligence, 6, 100192.
33. Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C. M., & Eger, S. (2023). MoverScore: Text generation evaluating with contextualized
embeddings and earth mover distance. Proceedings of EMNLP 2023, 563–578.
34. Zhou, Y., Liu, H., & Jiang, Y. (2022). Adaptive learning pathway construction using reinforcement learning and knowledge graphs. IEEE
Transactions on Learning Technologies, 15(3), 320–334.
35. Zhu, M., Su, Y., & Chen, E. (2023). Fairness-aware automatic question generation with debiased language model prompting. Proceedings
of the 16th International Conference on Educational Data Mining (EDM 2023), 190–200.
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