EQG-RACE: Examination-Type Question Generation

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · 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) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

Semi-supervised classification with graph convolutional networks

Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · see citing works

Get To The Point: Summarization with Pointer-Generator Networks

See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1099 · Cited by 3,775 · see citing works

Graph Convolutional Networks for Text Classification

Yao, L., Mao, C., & Luo, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017370 · Cited by 2,171 · see citing works

Unified Language Model Pre-training for Natural Language Understanding and Generation

Dong, L., Yang, N., Wang, W., Wei, F., Liu, X., Wang, Y., Gao, J., Zhou, M., & Hon, H.-W. (2019) · arXiv · doi:10.48550/arxiv.1905.03197 · Cited by 1,225 · see citing works

MS MARCO: A Human Generated MAchine Reading COmprehension DatasetPreprint

Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T. G., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.giscience.2025.5 · Cited by 1,190 · see citing works

RACE: Large-scale ReAding Comprehension Dataset From Examinations

Lai, G., Xie, Q., Liu, H., Yang, Y., & Hovy, E. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1082 · Cited by 1,044 · see citing works

CoQA: A Conversational Question Answering Challenge

Reddy, S., Chen, D., & Manning, C. D. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00266 · Cited by 975 · see citing works

NewsQA: A Machine Comprehension Dataset

Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., & Suleman, K. (2017) · Proceedings of the 2nd Workshop on Representation Learning for NLP · doi:10.18653/v1/w17-2623 · Cited by 706 · see citing works

Learning to Ask: Neural Question Generation for Reading Comprehension

Du, X., Shao, J., & Cardie, C. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1123 · Cited by 696 · see citing works

Gated Self-Matching Networks for Reading Comprehension and Question Answering

Wang, W., Yang, N., Wei, F., Chang, B., & Zhou, M. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1018 · Cited by 669 · see citing works

Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting

Chen, Y.-C., & Bansal, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1063 · Cited by 601 · see citing works

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

Nguyen, T. G., Rosenberg, M., Xia, S., Gao, J., Tiwary, S., Majumder, R., & Deng, L. (2016) · neural information processing systems · Cited by 537 · see citing works

Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN

Peng, H. M., Li, J., He, Y., Liu, Y., Bao, M., Wang, L., Song, Y., & Yang, Q. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186005 · Cited by 428 · see citing works

Good Question! Statistical Ranking for Question Generation

Heilman, M., & Smith, N. A. (2018) · Figshare · doi:10.1184/r1/6473390 · Cited by 366 · see citing works

Neural Question Generation from Text: A Preliminary Study

Zhou, Q. S., Yang, N., Wei, F., Tan, C., Bao, H., & Zhou, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73618-1_56 · Cited by 353 · see citing works

Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks

Zhao, Y., Ni, X., Ding, Y., & Ke, Q. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1424 · Cited by 288 · see citing works

Question Generation for Question Answering

Duan, N., Tang, D., Chen, P., & Zhou, M. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1090 · Cited by 271 · see citing works

Computer-aided generation of multiple-choice tests

Mitkov, R., & Ha, L. A. (2003) · doi:10.3115/1118894.1118897 · Cited by 258 · see citing works

Answer-focused and Position-aware Neural Question Generation

Sun, X., Liu, J., Lyu, Y., He, W. P., Ma, Y., & Wang, S. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1427 · Cited by 185 · see citing works

Improving Neural Question Generation Using Answer Separation

Kim, Y., Lee, H., Shin, J., & Jung, K. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016602 · Cited by 178 · see citing works

Machine Comprehension by Text-to-Text Neural Question Generation

Yuan, X., Wang, T., Gülçehre, Ç., Sordoni, A., Bachman, P., Zhang, S., Subramanian, S., & Trischler, A. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2603 · Cited by 158 · see citing works

Question Answering and Question Generation as Dual TasksPreprint

Tang, D., Duan, N., Qin, T., Yan, Z., & Zhou, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02027 · Cited by 155 · see citing works

Leveraging Context Information for Natural Question Generation

Song, L., Wang, Z., Hamza, W., Zhang, Y., & Gildea, D. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2090 · Cited by 148 · see citing works

Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering

Zhang, S., & Bansal, M. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1253 · Cited by 115 · see citing works

LearningQ: A Large-Scale Dataset for Educational Question Generation

Chen, G., Yang, J., Hauff, C., & Houben, G.-J. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.14987 · Cited by 83 · see citing works

Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders

Wang, Y., Liu, C., Huang, M., & Nie, L. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1204 · Cited by 72 · see citing works

Generating Distractors for Reading Comprehension Questions from Real Examinations

Gao, Y., Bing, L., Li, P., King, I., & Lyu, M. R.-T. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016423 · Cited by 65 · see citing works

Let’s Ask Again: Refine Network for Automatic Question Generation

Nema, P., Mohankumar, A. K., Khapra, M. M., Srinivasan, B. V., & Ravindran, B. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1326 · Cited by 57 · see citing works

Improving Question Generation With to the Point Context

Li, J., Gao, Y., Bing, L., King, I., & Lyu, M. R.-T. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1317 · Cited by 52 · see citing works

Asking Clarification Questions in Knowledge-Based Question Answering

Xu, J., Wang, Y., Tang, D., Duan, N., Yang, P., Zeng, Q., Zhou, M., & Sun, X. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1172 · Cited by 46 · see citing works

Automatic Question Generation for Learning Evaluation in Medicine

Wang, W., Hao, T., & Liu, W. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-78139-4_22 · Cited by 42 · see citing works

Key Phrase Extraction for Generating Educational Question-Answer Pairs

Willis, A., Davis, G. M., Ruan, S., Manoharan, L., Landay, J. A., & Brunskill, E. (2019) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3330430.3333636 · Cited by 41 · see citing works

Experiments on Generating Questions About Facts

Rus, V., Cai, Z., & Graesser, A. C. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-70939-8_39 · Cited by 34 · see citing works

Evaluating Rewards for Question Generation Models

Hosking, T., & Riedel, S. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1237 · Cited by 34 · see citing works

Multi-Task Learning with Language Modeling for Question Generation

Zhou, W., Zhang, M., & Wu, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1337 · Cited by 29 · see citing works

Question Generation Shared Task and Evaluation Challenge -- Status Report

Rus, V., Brendan Wyse, A., Piwek, P., Lintean, M. C., Stoyanchev, S., & Moldovan, C. (2011) · Natural Language Generation · Cited by 22 · see citing works

Automatically Generating Discussion Questions

Adamson, D., Bhartiya, D., Gujral, B., Kedia, R., Singh, A., & Rosé, C. P. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39112-5_9 · Cited by 20 · see citing works

Matching Long Text Documents via Graph Convolutional NetworksPreprint

Liu, B., Zhang, T., Niu, D., Lin, J., Lai, K., & Xu, Y. (2018) · arXiv (Cornell University) · Cited by 18 · see citing works

QG-Net: A Data-Driven Question Generation Model for Educational Content

Wang, J. (2020) · Rice Research Repository (Rice University) · Cited by 14 · see citing works

Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence ModelPreprint

Chen, Y., Wu, L., & Zaki, M. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.08832 · Cited by 11 · see citing works

The 2nd Workshop on Question Generation

Vasile, R., & Lester, J. (2009) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-60750-028-5-808 · Cited by 7 · see citing works

GraphSeq2Seq: Graph-Sequence-to-Sequence for Neural Machine Translation

Zhao, G., Li, J., Wang, L., Qian, X., & Fu, Y. (2018) · Cited by 6 · see citing works

Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence ModelPreprint

Li, W., Xu, J., He, Y., Yan, S., Wu, Y., & Sun, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01231 · Cited by 6 · see citing works

Multi-Task Learning with Language Modeling for Question GenerationPreprint

Zhou, W., Zhang, M., & Wu, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11813 · Cited by 5 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805

Assessment in the age of artificial intelligence

Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., Selwyn, N., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100075 · Cited by 409 · see citing works

A Review on the Use of Large Language Models as Virtual Tutors

García-Méndez, S., de Arriba-Pérez, F., & Somoza-López, M. D. C. (2024) · Science & Education · doi:10.1007/s11191-024-00530-2 · Cited by 78 · see citing works

AI in medical education: uses of AI in construction type A MCQs

Rezigalla, A. A. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05250-3 · Cited by 36 · see citing works

Review on Neural Question Generation for Education Purposes

Faraby, S. A., Adiwijaya, A., & Romadhony, A. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00374-x · Cited by 31 · see citing works

Asking questions like educational experts: automatically generating question-answer pairs on real-world examination data

Qu, F., Jia, X., & Wu, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.202 · Cited by 30 · see citing works

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

Ghanem, B., Coleman, L. L., Dexter, J. R., von der Ohe, S., & Fyshe, A. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.168 · Cited by 25 · see citing works

Reinforcement learning-driven deep question generation with rich semantics

Guan, M., Mondal, S. K., Dai, H., & Bao, H. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103232 · Cited by 28 · see citing works

QDG: A unified model for automatic question-distractor pairs generation

Shuai, P., Li, L., Liu, S., & Shen, J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03894-6 · Cited by 20 · see citing works

Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends

Lan, Y., Li, X., Du, H., Lu, X., Gao, M. Z., Qian, W., & Zhou, A. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3621181 · Cited by 43 · see citing works

DIRECT: Toward Dialogue-Based Reading Comprehension Tutoring

Huang, J., Lee, Y., & Kwon, O. (2022) · IEEE Access · doi:10.1109/access.2022.3233224 · Cited by 18 · see citing works

QAScore—An Unsupervised Unreferenced Metric for the Question Generation Evaluation

Ji, T., Lyu, C., Jones, G., Zhou, L., & Graham, Y. (2022) · Entropy · doi:10.3390/e24111514 · Cited by 12 · see citing works

Closed-book Question Generation via Contrastive Learning

Dong, X., Lu, J., Wang, J., & Caverlee, J. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.230 · Cited by 11 · see citing works

AI-assisted programming question generation: Constructing semantic networks of programming knowledge by local knowledge graph and abstract syntax tree

Chung, C.-Y., Hsiao, I., & Lin, Y. (2022) · Journal of Research on Technology in Education · doi:10.1080/15391523.2022.2123872 · Cited by 10 · see citing works

A Unified Framework for Contextual and Factoid Question Generation

Dong, C., Shen, Y., Lin, S., Lin, Z., & Deng, Y. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3280182 · Cited by 10 · see citing works

Answer-Aware Question Generation from Tabular and Textual Data using T5

Pandraju, S., & Mahalingam, S. G. (2021) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v16i18.25121 · Cited by 8 · see citing works

Handover QG: Question Generation by Decoder Fusion and Reinforcement Learning

Chung, H.-L., Chan, Y.-H., & Fan, Y.-C. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3426292 · Cited by 8 · see citing works

Automatic Multiple Choice Question Generation: A Systematic Literature Review

Henry, M. M., Heryanto, N. A., Isnan, M., Nugroho, K. S., & Pardamean, B. (2024) · International Conference on Information Technology and Digital Applications (ICITDA) · doi:10.1109/icitda64560.2024.10810102 · Cited by 8 · see citing works

Automating Turkish Educational Quiz Generation Using Large Language Models

Zeinalipour, K., Keptiğ, Y. G., Maggini, M., & Gori, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-82150-9_19 · Cited by 7 · see citing works

Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training

Zhu, P., & Hauff, C. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.183 · Cited by 6 · see citing works

Leveraging Structured Information from a Passage to Generate Questions

Xu, J., Sun, Y., Gan, J., Zhou, M., & Wu, D. (2022) · Tsinghua Science & Technology · doi:10.26599/tst.2022.9010034 · Cited by 6 · see citing works

Latent Reasoning for Low-Resource Question Generation

Huang, X., Qi, J., Sun, Y., & Zhang, R. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.265 · Cited by 5 · see citing works

Comparison of template-based and multilayer perceptron-based approach for automatic question generation system

Sewunetie, W. T., & Kovács, L. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v28.i3.pp1738-1748 · Cited by 4 · see citing works

Improving Controllability of Educational Question Generation by Keyword ProvisionPreprint

Chan, Y.-H., Chung, H.-L., & Fan, Y.-C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.01012 · Cited by 4 · see citing works

Artificial General Intelligence (AGI) for Medical Education and TrainingPreprint

Lema, K. G. (2023) · doi:10.31730/osf.io/xytzm · Cited by 4 · see citing works

A literature review of research on question generation in education

Dong, X., Zhang, X., Li, Z., Hou, Q., Xue, J., & Li, X. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3203 · Cited by 3 · see citing works

Enhancing Question Generation with Commonsense Knowledge

Jia, X., Wang, H. E., Yin, D., & Wu, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84186-7_10 · Cited by 6 · see citing works

Enhancing Question Generation with Commonsense KnowledgePreprint

Jia, X., Wang, H. E., Yin, D., & Wu, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.10454 · Cited by 3 · see citing works

Generating Question-Answer Pairs for Few-Shot Learning

Wang, Y., & Li, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44213-1_35 · Cited by 2 · see citing works

EQGG: Automatic Question Group Generation

Huang, P., Chan, Y.-H., Yang, C.-Y. H., Chen, H.-Y., & Fan, Y.-C. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3430225 · Cited by 2 · see citing works

LargePiG for Hallucination-Free Query Generation: Your Large Language Model is Secretly a Pointer Generator

Sun, Z., Si, Z., Zang, X., Zheng, K., Song, Y., Zhang, X., & Xu, J. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714800 · Cited by 3 · see citing works

Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination DataPreprint

Qu, F., Jia, X., & Wu, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05179 · Cited by 4 · see citing works

High-Quality Distractors Generation for Human Exam Based on Reinforcement Learning from Preference Feedback

Wang, R., Jiang, Y., Tao, Y., Li, M., Wang, X., & Ge, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9440-9_8 · Cited by 2 · see citing works

Human-in-the-Loop: A Conceptual Framework for Business English Teachers’ AI-Empowered Assessment

Chen, X., & Pi, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-4407-0_17 · Cited by 2 · see citing works

Leveraging Language Model and Knowledge Tracing for Personalized Question Generation

Yin, Z., Li, L., Xu, X., Li, Y., & Zhou, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3052-6_7 · Cited by 1 · see citing works

Learning to generate complex question with intent prediction from long passage

Pan, Y., Hu, B., Wang, S., Wang, X., Chen, Q., Xu, Z., & Zhang, M. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03651-9 · Cited by 1 · see citing works

Deep QA: An Open-Domain Dataset of Deep Questions and Comprehensive Answers

Anbarasu, H. S., Navalli, H. V., Vidapanakal, H., Gowd, K. M., & Das, B. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35299-7_16 · Cited by 1 · see citing works

Artificial intelligence in a modernizing science and technology education: a textual narrative synthesis in the COVID-19 era

Pacala, F. A. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2611/1/012028

XV International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2023) 16/05/2023 - 19/05/2023 Kryvyi Rih, Ukraine

освіти, Н. У. І. Ц., & університет, К. К. Д. П. (2023) · Institute of Physics eBooks · doi:10.31812/123456789/7995 · Cited by 2 · see citing works

Keyword Provision Question Generation for Facilitating Educational Reading Comprehension Preparation

Chan, Y.-H., Chung, H.-L., & Fan, Y.-C. (2022) · International Conference on Natural Language Generation · doi:10.18653/v1/2022.inlg-main.15

Personalized Questioning Teaching Mode for English Reading in Junior High School Based on Automatic Question Generation

Xu, J., Zhang, L., Sun, Y., Wang, J., & Duan, S. (2024) · Communications in computer and information science · doi:10.1007/978-981-99-9492-2_9

Clarification question generation diversity and specificity enhancement based on question keyword prediction

Zhou, M., Zhou, J., Gan, J., Gao, W. D., & Xu, J. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05316-1

Exploring the Perception of Indian Educators Towards Use of Generative AI Tools for Assessment

Pillai, N. S., & Arora, P. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2418-9.ch002

Qadg: Generating question–answer-distractors pairs for real examination

Zhou, H. Y., & Li, L. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10658-5

KPQDG: a key sentence prompt-tuning for automatic question-distractor pairs generation

Yin, Z., & Li, L. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11149-x

An Exploratory Research on Reading Comprehension Question Generation Based on Annotated Clause Complex Corpus

Ge, S., & Chen, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-4407-0_18

Algorithmes basés sur les données pour le comportement individuel et collectif des utilisateurs

Bouarour, N. (2023) · doi:10.70675/738ffa7dz111ez4a74zbd9dz7f5cf8f13870

Knowledge Graph Augmented Generation for Examination Questions

Li, Q., Zhao, Z., Liu, Y., Liu, N., & Cui, L. (2026) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2026.3682325

Text-Book Based Question Paper Generation Using Nlp Techniques

Akilandeswari, T., Harshvardhan, P., Krishna, K. G., Murthi, M. S., & Supraja, L. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-27754-1_25

Leveraging Structured Information from a Passage to Generate Questions

Xu, J., Sun, Y., Gan, J., Zhou, M., & Wu, D. (2022) · Tsinghua Science & Technology · doi:10.26599/tst.2022.9010034 · Cited by 6 · see citing works

Asking questions like educational experts: automatically generating question-answer pairs on real-world examination data

Qu, F., Jia, X., & Wu, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.202 · Cited by 30 · see citing works

A Context-Aware Subjective Question Generation Model Using a Domain-Specific Fine-Tuned T5 Transformer

Senthilkumar, P., & Nandhini, K. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.21127

Automatic question generation from natural language texts

Cao, Z. (2022) · doi:10.32657/10356/164625

Generating and answering questions across text and knowledge graphs

Han, K. (2024) · theses.fr (ABES)