The Last Decade of Automatic Question Generation: A Review of Techniques, Limitations, and Applications in Business Process Management Education
DOI:
https://doi.org/10.5753/rbie.2026.6093Keywords:
Automatic Question Generation, Automatic Assessment, Test Generation, Business Process Management, BPMAbstract
Automatic Question Generation (AQG) is a research area that employs Natural Language Processing (NLP) techniques to automatically generate questions from textual content. Although it is still considered an emerging field, AQG has experienced significant growth in recent years, driven by advances in artificial intelligence, especially in deep learning and large language models, as well as by the increasing demand for scalable educational technologies. This article presents a Systematic Literature Review (SLR) focused on AQG research conducted over the last decade. The review aimed to identify and analyze the main computational approaches, practical applications, existing limitations, evaluation methods, and the degree of acceptance by education professionals. The SLR was carried out using major academic databases, resulting in the selection of 103 relevant studies, of which 90 are original research articles and 13 are literature reviews. The results show a clear trend toward the adoption of Transformer-based models, which have significantly improved question generation quality. However, the analysis also reveals a lack of consensus regarding standardized evaluation metrics, particularly for automatic assessments, and a notable gap in studies that investigate how educational professionals perceive and accept questions generated by these systems. This highlights an important area for future research.
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Al Faraby, S., Adiwijaya, A., & Romadhony, A. (2023). Review on Neural Question Generationfor Education Purposes. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00374-x . [GS Search]
Al Shuraiqi, S., Aal Abdulsalam, A., Masters, K., Zidoum, H., & AlZaabi, A. (2024). Automatic Generation of Medical Case-Based Multiple-Choice Questions (MCQs): A Review of Methodologies, Applications, Evaluation, and Future Directions [Section: 0]. Big Data and Cognitive Computing, 8(10). https://doi.org/10.3390/bdcc8100139. [GS Search]
Alshboul, J., & Baksa-Varga, E. (2022). A Review of Automatic Question Generation in Teaching Programming. International Journal of Advanced Computer Science and Applications, 13(10), 45–51. https://doi.org/10.14569/IJACSA.2022.0131006. [GS Search]
Alshboul, J., & Baksa-Varga, E. (2024). A Hybrid Approach for Automatic Question Generation from Program Codes. International Journal of Advanced Computer Science and Applications, 15(1), 10–17. https://doi.org/10.14569/IJACSA.2024.0150102. [GS Search]
Alsubait, T., Parsia, B., & Sattler, U. (2016). Ontology-Based Multiple Choice Question Generation. KI - Künstliche Intelligenz, 30(2), 183–188. https://doi.org/10.1007/s13218-015-0405-9. [GS Search]
Andersson, T., & Picazo-Sanchez, P. (2023). Closing the Gap: Automated Distractor Generation in Japanese Language Testing. Education Sciences, 13(12). https://doi.org/10.3390/educsci13121203. [GS Search]
Awalurahman, H., & Budi, I. (2024a). Automatic distractor generation in multiple-choice questions: A systematic literature review [Section: 0]. PeerJ Computer Science, 10. https://doi.org/10.7717/peerj-cs.2441. [GS Search]
Awalurahman, H., & Budi, I. (2024b). Paragraph vs Sentence in Automatic Question Generation Fine-Tuning using Text-to-Text Transfer Transformer for Bahasa Indonesia - Proc. Int. Conf. Educ. Technol. ICET [Publisher: Institute of Electrical and Electronics Engineers Section: 0]. Proc. Int. Conf. Educ. Technol. ICET, 155–161. https://doi.org/10.1109/ICET64717.2024.10778465. [GS Search]
Babakhani, P., Lommatzsch, A., Brodt, T., Sacker, D., Sivrikaya, F., & Albayrak, S. (2024). Opinerium: Subjective Question Generation Using Large Language Models. IEEE Access, 12, 66085–66099. https://doi.org/10.1109/ACCESS.2024.3398553. [GS Search]
Bachiri, Y.-A., & Mouncif, H. (2023). Artificial Intelligence System in Aid of Pedagogical Engineering for Knowledge Assessment on MOOC Platforms: Open EdX and Moodle. International Journal of Emerging Technologies in Learning, 18(5), 144–160. https://doi.org/10.3991/ijet.v18i05.36589. [GS Search]
Bhadouria, A., Gupta, P., Bindal, P., Madan, K., & Sonal, S. (2024). Automated Examination System using Machine Learning and Natural Language Processing - Proceedings of the 2024 Sixteenth International Conference on Contemporary Computing [Place: New York, NY, USA Publisher: Association for Computing Machinery Section: 0]. Proceedings of the 2024 Sixteenth International Conference on Contemporary Computing, 752–761. https://doi.org/10.1145/3675888.3676144. [GS Search]
Blšták, M., & Rozinajová, V. (2022). Automatic question generation based on sentence structure analysis using machine learning approach. Natural Language Engineering, 28(4), 487–517. https://doi.org/10.1017/S1351324921000139. [GS Search]
Caporale, T., Citak, M., Lehner, J., Schoknecht, A., & Ullrich, M. (2013). Social bpm labcharacterization of a collaborative approach for business process management education. 2013 IEEE 15th Conference on Business Informatics, 367–373. https://doi.org/10.1109/CBI.2013.60. [GS Search]
Ch, D. R., & Saha, S. K. (2023). Generation of Multiple-Choice Questions From Textbook Contents of School-Level Subjects. IEEE Transactions on Learning Technologies, 16(1), 40–52. https://doi.org/10.1109/TLT.2022.32242. [GS Search]
Ch, D. R., & Saha, S. K. (2020). Automatic Multiple Choice Question Generation From Text: A Survey. IEEE Transactions on Learning Technologies, 13(1), 14–25. https://doi.org/10.1109/TLT.2018.2889100. [GS Search]
Chinkina, M., Ruiz, S., & Meurers, D. (2020). Crowdsourcing evaluation of the quality of automatically generated questions for supporting computer-assisted language teaching. Re-CALL, 32(2), 145–161. https://doi.org/10.1017/S0958344019000193. [GS Search]
Chow, W. (2021). Teaching business process management with a flipped-classroom and problem-based learning approach with the use of apromore and other bpm software in graduate information systems courses. 2021 IEEE International Conference on Engineering, Technology & Education (TALE), 1–8. https://doi.org/10.1109/TALE52509.2021.9678885. [GS Search]
Chung, H.-L., Chan, Y.-H., & Fan, Y.-C. (2024). Handover QG: Question Generation by Decoder Fusion and Reinforcement Learning [Section: 0]. IEEE/ACM Trans. Audio, Speech and Lang. Proc., 32, 3644–3655. https://doi.org/10.1109/TASLP.2024.3426292. [GS Search]
Corporation for Digital Scholarship. (2024). Zotero [Software for managing research sources]. Disponível em: [link].
Cubric, M., & Tosic, M. (2020). Design and evaluation of an ontology-based tool for generating multiple-choice questions. Interactive Technology and Smart Education, 17(2), 109–131. . [GS Search]
da Silva, D.-P., Ives, de Moura, Arruda, S., et al. (2023). Compreendendo a inteligência artificial generativa na perspectiva da língua. SciELO Preprints. https://doi.org/https://doi.org/10.1590/SciELOPreprints.7077 [GS Search]
Das, B., & Majumder, M. (2017). Factual open cloze question generation for assessment of learner’s knowledge. International Journal of Educational Technology in Higher Education, 14(1), 24. https://doi.org/10.1186/s41239-017-0060-3. [GS Search]
Das, B., Majumder, M., Phadikar, S., & Sekh, A. A. (2021a). Multiple-choice question generation with auto-generated distractors for computer-assisted educational assessment. Multimedia Tools and Applications, 80(21), 31907–31925. https://doi.org/10.1007/s11042-021-11222-2. [GS Search]
Das, B., Majumder, M., Phadikar, S., & Sekh, A. A. (2021b). Automatic question generation and answer assessment: A survey. Research and Practice in Technology Enhanced Learning, 16(1), 5. https://doi.org/10.1186/s41039-021-00151-1. [GS Search]
de-Fitero-Dominguez, D., Garcia-Cabot, A., & Garcia-Lopez, E. (2024). Automated multiple-choice question generation in Spanish using neural language models [Section: 0]. Neural Computing and Applications, 36(29), 18223–18235. https://doi.org/10.1007/s00521-024-10076-7. [GS Search]
Doughty, J., Wan, Z., Bompelli, A., Qayum, J., Wang, T., Zhang, J., Zheng, Y., Doyle, A., Sridhar, P., Agarwal, A., Bogart, C., Keylor, E., Kultur, C., Savelka, J., & Sakr, M. (2024). A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education [Publisher: Association for Computing Machinery], 114–123. https://doi.org/10.1145/3636243.3636. [GS Search]
Dumas, M., La Rosa, M., Mendling, J., Reijers, H. A., et al. (2013). Fundamentals of business process management (Vol. 1). Springer. https://doi.org/https://doi.org/10.1007/978-3-662-56509-4. [GS Search]
Elkins, S., Kochmar, E., Cheung, J., & Serban, I. (2024). How Teachers Can Use Large Language Models and Bloom’s Taxonomy to Create Educational Quizzes - Proc. AAAI Conf. Artif. Intell. (Wooldridge M., Dy J., & Natarajan S., Eds.) [Publisher: Association for the Advancement of Artificial Intelligence Section: 0]. Proc. AAAI Conf. Artif. Intell., 38, 23084–23091. https://doi.org/10.1609/aaai.v38i21.30353. [GS Search]
Fahad, A. R., Al Nahian, N., Islam, M. A., & Rahman, R. M. (2024). Answer Agnostic Question Generation in Bangla Language. International Journal of Networked and Distributed Computing. https://doi.org/10.1007/s44227-023-00018-5. [GS Search]
Figshare. (2025, October). Systematic literature review articles on bpm (dataset; Version Modified on 2025-10-11) [Dataset exported from Rayyan containing records that remained after the first two exclusion criteria (duplicate removal and non-article filtering), as well as notes and screening decisions recorded during the review. Available at: [link].
García-Méndez, S., de Arriba-Pérez, F., & Somoza-López, M. D. C. (2024). A Review on the Use of Large Language Models as Virtual Tutors. Science & Education. https://doi.org/10.1007/s11191-024-00530-2. [GS Search]
Gašpar, A., Grubišić, A., & Šarić-Grgić, I. (2023). Evaluation of a rule-based approach to automatic factual question generation using syntactic and semantic analysis. Language Resources and Evaluation, 57(4), 1431–1461. https://doi.org/10.1007/s10579-023-09672-1. [GS Search]
Gorgun, G., & Bulut, O. (2024). Exploring quality criteria and evaluation methods in automated question generation: A comprehensive survey [Section: 0]. Education and Information Technologies, 29(18), 24111–24142. https://doi.org/10.1007/s10639-024-12771-3. [GS Search]
Goyal, R., Kumar, P., & Singh, V. P. (2023). Automated Question and Answer Generation from Texts using Text-to-Text Transformers. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-023-07840-7. [GS Search]
Grévisse, C., Pavlou, M. A. S., & Schneider, J. G. (2024). Docimological Quality Analysis of LLM-Generated Multiple Choice Questions in Computer Science and Medicine [Section: 0]. SN Computer Science, 5(5), 636. https://doi.org/10.1007/s42979-024-02963-6. [GS Search]
Hsiao, I.-H., & Chung, C.-Y. (2022). AI-infused Semantic Model to Enrich and Expand Programming Question Generation. Journal of Artificial Intelligence and Technology, 2(2), 47–54. https://doi.org/10.37965/jait.2022.0090. [GS Search]
Hwang, W.-Y., & Utami, I. Q. (2024). Using GPT and authentic contextual recognition to generate math word problems with difficulty levels [Section: 0]. Education and Information Technologies, 29(13), 1–29. https://doi.org/10.1007/s10639-024-12537-x. [GS Search]
Izo, F., Leão, J., Pirovani, J., & Oliveira, E. (2022). Automatic generation of large-scale assessment questions. Anais do XVIII Simp´osio Brasileiro de Sistemas de Informação. https://sol.sbc.org.br/index.php/sbsi/article/view/21348. [GS Search]
Jadhav, P., Deore, S., Aute, S., Gaikwad, R., Barphe, S., & Meshram, E. (2024). ’Questomatic’: Automated Question Formulation System - Int. Conf. Sustain. Expert Syst., ICSES - Proc. [Publisher: Institute of Electrical and Electronics Engineers Inc. Section: 0]. Int. Conf. Sustain. Expert Syst., ICSES - Proc., 577–581. https://doi.org/10.1109/ICSES63445.2024.10763124. [GS Search]
Jasuja, H., Negi, U., & Kaur, G. (2024). Automatic Question Generation from Youtube Lectures using Deep Learning - Int. Conf. Comput. Commun. Netw. Technol., ICCCNT [Publisher: Institute of Electrical and Electronics Engineers Inc. Section: 0]. Int. Conf. Comput. Com-mun. Netw. Technol., ICCCNT. https://doi.org/10.1109/ICCCNT61001.2024.10726107. [GS Search]
Jouault, C., Seta, K., & Hayashi, Y. (2016). Content-Dependent Question Generation Using LOD for History Learning in Open Learning Space. New Generation Computing, 34(4), 367–394. https://doi.org/10.1007/s00354-016-0404-x. [GS Search]
Kalpakchi, D., & Boye, J. (2024). Quinductor: A multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies. Natural Language Engineering, 30(2), 217–255. https://doi.org/10.1017/S1351324923000037. [GS Search]
Keklik, O., Tuglular, T., & Tekir, S. (2019). Rule-based automatic question generation using semantic role labeling. IEICE Transactions on Information and Systems, (7), 1362–1373. https://doi.org/10.1587/transinf.2018EDP7199. [GS Search]
Khandait, K., Bhura, S., & Asole, S. (2022). AUTOMATIC QUESTION GENERATION THROUGH WORD VECTOR SYNCHRONIZATION USING LAMMA. Indian Journal of Computer Science and Engineering, 13(4), 1083–1095. https://doi.org/10.21817/indjcse/2022/v13i4/221304046. [GS Search]
Khandait, K., Bhura, S., & Asole, S. (2023). Neural Approach to Automatic Subjective Question Generation System Using Multiple Filters for Supporting Correct WH-type Question. [Publisher: Ismail Saritas]. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 60–72. Available at: [link].
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering version 2.3 [Accessed: 2025-08-31]. Engineering, 45(4ve), 1051. Available at: [link]. [GS Search]
Kumar, A., Nayak, A., K, M., & Ghosh, K. (2023). A Novel Framework for the Generation of Multiple Choice Question Stems Using Semantic and Machine-Learning Techniques. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00333-6. [GS Search]
Kurdi, G., Leo, J., Parsia, B., Sattler, U., & Al-Emari, S. (2020). A Systematic Review of Automatic Question Generation for Educational Purposes. International Journal of Artificial Intelligence in Education, 30(1), 121–204. https://doi.org/10.1007/s40593-019-00186-y. [GS Search]
Kusuma, S., Siahaan, D., & Fatichah, C. (2022). Automatic question generation with various difficulty levels based on knowledge ontology using a query template. Knowledge-Based Systems, 249. https://doi.org/10.1016/j.knosys.2022.108906. [GS Search]
Kyung-Mi, O. (2024). A comparative study of ai-human-made and human-made test forms for a university tesol theory course. Language Testing in Asia, 14(1), 19. https://doi.org/10.1186/s40468-024-00291-3. [GS Search]
Lafkiar, S., & En Nahnahi, N. (2024). An end-to-end transformer-based model for Arabic question generation [Section: 0]. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-19958-3. [GS Search]
Larranaga, M., Aldabe, I., Arruarte, A., Elorriaga, J., & Maritxalar, M. (2022). A Qualitative Case Study on the Validation of Automatically Generated Multiple-Choice Questions From Science Textbooks. IEEE Transactions on Learning Technologies, 15(3), 338–349. https://doi.org/10.1109/TLT.2022.3171589. [GS Search]
Le, N.-T., & Pinkwart, N. (2015). Evaluation of a question generation approach using semantic web for supporting argumentation. Research and Practice in Technology Enhanced Learning, 10(1), 3. . [GS Search]
Lee, U., Jung, H., Jeon, Y., Sohn, Y., Hwang, W., Moon, J., & Kim, H. (2023). Few-shot is enough: Exploring ChatGPT prompt engineering method for automatic question generation in english education. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12249-8. [GS Search]
Leo, J., Kurdi, G., Matentzoglu, N., Parsia, B., Sattler, U., Forge, S., Donato, G., & Dowling, W. (2019). Ontology-Based Generation of Medical, Multi-term MCQs. International Journal of Artificial Intelligence in Education, 29(2), 145–188. https://doi.org/10.1007/s40593-018-00172-w. [GS Search]
Li, E., Su, J., Sheng, H., & Wai, L. (2020). AGenT Zero: Zero-shot Automatic Multiple-Choice Question Generation for Skill Assessments. arXiv preprint arXiv:2012.01186. https://doi.org/10.48550/arXiv.2012.01186 [GS Search]
Li, Z., Cao, Z., Li, P., Zhong, Y., & Li, S. (2023). Multi-Hop Question Generation with Knowledge Graph-Enhanced Language Model. Applied Sciences (Switzerland), 13(9). . [GS Search]
Liu, B., Zhao, M., Niu, D., Lai, K., He, Y., Wei, H., & Xu, Y. (2019). Learning to Generate Questions by LearningWhat not to Generate [Publisher: Association for Computing Machinery], 1106–1118. https://doi.org/10.1145/3308558.3313737. [GS Search]
Lohr, D., Berges, M., Chugh, A., Kohlhase, M., & M¨uller, D. (2024). Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning Objects [Section: 0]. Journal of Computer Assisted Learning, 41(1). https://doi.org/10.1111/jcal.13101. [GS Search]
Lui, A. K.-F., Ng, S.-C., & Fung, Y.-C. (2015). A parse tree based computation technique for generating comprehension style questions from chinese text. Technology in Education. Transforming Educational Practices with Technology: First International Conference, ICTE 2014, Hong Kong, China, July 2-4, 2014. Revised Selected Papers, 61–73. https://doi.org/10.1007/978-3-662-46158-7. [GS Search]
Madri, V. R., & Meruva, S. (2023). A comprehensive review on MCQ generation from text. Multimedia Tools and Applications, 82(25), 39415–39434. https://doi.org/10.1007/s11042-023-14768-5. [GS Search]
Maheen, F., Asif, M., Ahmad, H., Ahmad, S., Alturise, F., Asiry, O., & Ghadi, Y. (2022). Automatic computer science domain multiple-choice questions generation based on informative sentences. PeerJ Computer Science, 8. . [GS Search]
Maity, S., Deroy, A., & Sarkar, S. (2024). Investigating Large Language Models for Prompt-Based Open-Ended Question Generation in the Technical Domain [Section: 0]. SN Computer Science, 5(8), 1128. https://doi.org/10.1007/s42979-024-03464-2. [GS Search]
Marrella, A., Resinas, M., Jans, M., & Rosemann, M. (Eds.). (2024). Business process management: Blockchain, robotic process automation, central and eastern european, educators and industry forum (Vol. 527). Springer. https://doi.org/10.1007/978-3-031-70445-1.
Matsumori, S., Okuoka, K., Shibata, R., Inoue, M., Fukuchi, Y., & Imai, M. (2023). Mask and Cloze: Automatic Open Cloze Question Generation Using a Masked Language Model. IEEE Access, 11, 9835–9850. https://doi.org/10.1109/ACCESS.2023.3239005. [GS Search]
Mostow, J., Huang, Y.-T., Jang, H., Weinstein, A., Valeri, J., & Gates, D. (2016). Developing, evaluating, and refining an automatic generator of diagnostic multiple choice cloze questions to assess children’s comprehension while reading. Natural Language Engineering, 23(2), 245–294.https://doi.org/10.1017/S1351324916000024. [GS Search]
Mulla, N., & Gharpure, P. (2023a). Automatic question generation: A review of methodologies, datasets, evaluation metrics, and applications. Progress in Artificial Intelligence, 12(1), 1–32. https://doi.org/10.1007/s13748-023-00295-9. [GS Search]
Mulla, N., & Gharpure, P. (2023b). Leveraging well-formedness and cognitive level classifiers for automatic question generation on Java technical passages using T5 transformer. International Journal of Information Technology, 15(4), 1961–1973. https://doi.org/10.1007/s41870-023-01262-2. [GS Search]
Murad, D., Wijanarko, B., Leandros, R., & Murad, S. (2023). LONG SHORT-TERM MEMORY-BASED ENCODER-DECODER WITH ATTENTION MECHANISM MODEL FOR QUESTION GENERATION. ICIC Express Letters, 17(9), 1067–1074. https://doi.org/10.24507/icicel.17.09.1067. [GS Search]
Narayana, B., Harsha, V., Prudhvi, K., Vardhan, B., & Sravika, A. (2024). Automated Question Paper Generation using Natural Language Processing - Int. Conf. Comput. Intell. Green Sustain. Technol., ICCIGST - Proc. [Publisher: Institute of Electrical and Electronics Engineers Inc. Section: 0]. Int. Conf. Comput. Intell. Green Sustain. Technol., ICCIGST - Proc. https://doi.org/10.1109/ICCIGST60741.2024.10717510. [GS Search]
Neto, A. S., Silva, S., & Júnior, R. S. (2024). Chatgpt como ferramenta de aprendizagem: Um estudo com estudantes do ensino médio no interior do rn. Anais do XXXV Simpósio Brasileiro de Informática na Educação, 1480–1489. https://doi.org/10.5753/sbie.2024.241816. [GS Search]
Odilinye, L., Popowich, F., Zhang, E., Nesbit, J., & Winne, P. H. (2015). Aligning automatically generated questions to instructor goals and learner behaviour. Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), 216–223. https://doi.org/10.1109/ICOSC.2015.7050809. [GS Search]
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan—a web and mobile app for systematic reviews. Systematic Reviews, 5(1), 210. https://doi.org/10.1186/s13643-016-0384-4. [GS Search]
Pereira, F. R., Costa, H. A. X., & Parreira, P. A. (2024). A Comparative Study of Tools for Anomaly Detection in Software Requirements, 11–21. https://doi.org/10.1145/3701625.3701641. [GS Search]
Pham, P. V. L., Duc, A. V., Hoang, N. M., Do, X. L., & Luu, A. T. (2024). ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math Questions [Publisher: Association for Computing Machinery], 65–73. https://doi.org/10.1145/3605098.3636030.
Phan, T. H. V., & Do, P. (2022). NER2QUES: Combining named entity recognition and sequence to sequence to automatically generating Vietnamese questions. Neural Computing and Applications, 34(2), 1593–1612. https://doi.org/10.1007/s00521-021-06477-7 [GS Search]
Reis, I., Fischer, C. E., Pereira, R., Juliani, D., & Ulbricht, V. (2024). A pedagogia digital a partir do conectivismo e o uso da inteligência artificial na educação: Uma revisão integrativa. Anais do XXXV Simpósio Brasileiro de Informática na Educação, 1689–1700. https://doi.org/10.5753/sbie.2024.242378. [GS Search]
Rezigalla, A. (2024). AI in medical education: Uses of AI in construction type A MCQs [Section: 0]. BMC Medical Education, 24(1). https://doi.org/10.1186/s12909-024-05250-3. [GS Search]
Rockembach, G. R., & Thom, L. H. (2024). Investigating the use of intelligent tutors based on large language models: Automated generation of business process management questions using the revised bloom’s taxonomy. Simpósio Brasileiro de Informática na Educação (SBIE), 1587–1601. https://doi.org/https://doi.org/10.5753/sbie.2024.242199. [GS Search]
Rodrigues, H., Nyberg, E., & Coheur, L. (2022). Towards the benchmarking of question generation: Introducing the Monserrate corpus. Language Resources and Evaluation, 56(2), 573–591. https://doi.org/10.1007/s10579-021-09545-5. [GS Search]
Ruiz-Calleja, A., Vega-Gorgojo, G., Bote-Lorenzo, M., Asensio-Pérez, J., Dimitriadis, Y., & Gómez-Sánchez, E. (2021). Supporting contextualized learning with linked open data. Journal of Web Semantics, 70. https://doi.org/10.1016/j.websem.2021.100657. [GS Search]
Ruma, J., Mayeesha, T., & Rahman, R. (2023). Transformer based Answer-Aware Bengali Question Generation. International Journal of Cognitive Computing in Engineering, 4, 314–326. https://doi.org/10.1016/j.ijcce.2023.09.003[GS Search]
Santi, M., Manacero, A., Peronaglio, F. F., Lobato, R. S., Spolon, R., & Cavenaghi, M. A. (2022). Training transformers for question generation task in intelligent tutoring systems. 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), 1–6. http://dx.doi.org/10.23919/CISTI54924.2022.9820606. [GS Search]
Sarvepalli, A., & Godin, J. (2017). Business process management in the classroom. Journal of Cases on Information Technology (JCIT), 19(2), 17–28. https://doi.org/10.4018/JCIT.2017040102. [GS Search]
Seethamraju, R. (2012). Business process management: A missing link in business education. Business Process Management Journal, 18(3), 532–547. https://doi.org/https://doi.org/10.1108/14637151211232696. [GS Search]
Sewunetie, W. T., & Kovács, L. (2022). Comparison of Automatic Question Generation Techniques. 2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo), 000025–000030. https://doi.org/10.1109/CINTI-MACRo57952.2022.10029559. [GS Search]
Sewunetie, W., & Kovacs, L. (2024). Automatic question generation using extended dependency parsing. Indonesian Journal of Electrical Engineering and Computer Science, 33(2), 1108–1115. https://doi.org/10.11591/ijeecs.v33.i2.pp1108-1115. [GS Search]
Sewunetie, W., & Kovács, L. (2022). Comparison of template-based and multilayer perceptron-based approach for automatic question generation system. Indonesian Journal of Electrical Engineering and Computer Science, 28(3), 1738–1748. https://doi.org/10.11591/ijeecs.v28.i3.pp1738-1748. [GS Search]
Shirude, A., Totala, S., Nikhar, S., Attar, V., & Ramanand, J. (2015). Automated Question Generation tool for structured data. 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1546–1551. https://doi.org/10.1109/ICACCI.2015.7275833. [GS Search]
Shwe, L. L., Matayong, S., & Witosurapot, S. (2024). The unified difficulty ranking mechanism for automatic multiple choice question generation in digital storytelling domain. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12666-3. [GS Search]
Silva, D., & Thom, L. (2021). 3d environment approach to teaching and learning business process management concepts: A systematic literature review. Anais do XXXII Simpósio Brasileiro de Informática na Educação, 328–337. https://doi.org/10.5753/sbie.2021.218159. [GS Search]
Suhartono, D., Majiid, M. R. N., & Fredyan, R. (2024). Towards automatic question generation using pre-trained model in academic field for Bahasa Indonesia. Education and InformationTechnologies. https://doi.org/10.1007/s10639-024-12717-9. [GS Search]
Susanti, Y., Tokunaga, T., Nishikawa, H., & Obari, H. (2017). Evaluation of automatically generated English vocabulary questions. Research and Practice in Technology Enhanced Learning, 12(1), 11. https://doi.org/10.1186/s41039-017-0051-y. [GS Search]
Thüs, D., Malone, S., & Brünken, R. (2024). Exploring generative AI in higher education: A RAG system to enhance student engagement with scientific literature [Section: 0]. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1474892. [GS Search]
Tuhpatussania, S., Utami, E., Kusrini, K., & Yuana, K. (2024). Automatic Question-Answer Generation for Education on Indonesian Texts : A Review of Methodologies, Dataset and Evaluation Metrics - Proc. - Int. Conf. Technol. Innov. Its Appl., ICTIIA [Publisher: Institute of Electrical and Electronics Engineers Inc. Section: 0]. Proc. - Int. Conf. Technol. Innov. Its Appl., ICTIIA. https://doi.org/10.1109/ICTIIA61827.2024.10761325. [GS Search]
Vincentio, K., & Suhartono, D. (2022). Automatic Question Generation using RNN-based and Pre-trained Transformer-based Models in Low Resource Indonesian Language. Informatica (Slovenia), 46(7), 103–118. https://doi.org/10.31449/inf.v46i7.4236. [GS Search]
Wang, H.-C., Chiang, Y.-H., & Chen, I.-F. (2024). A Method for Generating Course Test Questions Based on Natural Language Processing and Deep Learning. Education and Information Technologies, 29(7), 8843–8865. https://doi.org/10.1007/s10639-023-12159-9. [GS Search]
Wang, H.-C., Maslim, M., & Kan, C.-H. (2023). A question–answer generation system for an asynchronous distance learning platform. Education and Information Technologies, 28(9), 12059–12088. https://doi.org/10.1007/s10639-023-11675-y. [GS Search]
Wang, Z., Lan, A. S., Nie, W., Waters, A. E., Grimaldi, P. J., & Baraniuk, R. G. (2018). QG-net: A data-driven question generation model for educational content [Publisher: Association for Computing Machinery]. https://doi.org/10.1145/3231644.3231654. [GS Search]
Waswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., & Polo sukhin, I. (2017). Attention is all you need. Available at: [link]. [GS Search]
Weske, M., et al. (2007). Concepts, languages, architectures. Business Process Management. https://doi.org/https://doi.org/10.1007/978-3-642-28616-2. [GS Search]
Wiechetek, Ł., Medrek, M., & Banas, J. (2017). Business process management in higher education. the case of students of logistics. Problemy Zarzadzania, (4/2017 (71)), 146–164. https://doi.org10.7172/1644-9584.71.10. [GS Search]
Wijanarko, B. D., Heryadi, Y., Toba, H., & Budiharto, W. (2021). Question generation model based on key-phrase, context-free grammar, and Bloom’s taxonomy. Education and Information Technologies, 26(2), 2207–2223. https://doi.org/10.1007/s10639-020-10356-4. [GS Search]
Xin, Y., Cao, L., Wang, X., He, X., & Feng, L. (2021). Generating instructive questions from multiple articles to guide reading in e-bibliotherapy. Sensors, 21(9). https://doi.org/10.3390/s21093223. [GS Search]
Xu, J., Sun, Y., Gan, J., Zhou, M., & Wu, D. (2023). Leveraging Structured Information from a Passage to Generate Questions. Tsinghua Science and Technology, 28(3), 464–474. https://doi.org/10.26599/TST.2022.9010034. [GS Search]
Yu, J., Wang, S., & Yin, J. (2021). Adaptive Cross-Lingual Question Generation with Minimal Resources. Computer Journal, 64(7), 1056–1068. https://doi.org/10.1093/comjnl/bxab106. [GS Search]
Zeghouani, O., Ali, Z., van Dijkhuizen, W., Hong, J., & Clos, J. (2024). AI in the Classroom: Examining the Feasibility of AI-Generated Questions in Educational Settings - ACM Int. Conf. Proc. Ser. [Publisher: Association for Computing Machinery Section: 0]. ACM Int. Conf. Proc. Ser. https://doi.org/10.1145/3686038.3686652. [GS Search]
Zhang, R., Guo, J., Chen, L., Fan, Y., & Cheng, X. (2021). A Review on Question Generation from Natural Language Text. ACM Trans. Inf. Syst., 40(1). https://doi.org/10.1145/3468889. [GS Search]
Zimerman, F., Duarte, F. H., Silva, P. H., & Fortes, R. (2024). Explorando chatgpt para criação automática de questões práticas de programação de computadores. Anais do XXXV Simpósio Brasileiro de Informática na Educação, 2353–2364. https://doi.org/10.5753/sbie.2024.242440. [GS Search]
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