Comprehensive Review: Transforming Self-Education through Automatic Question Generation Technology

Authors

  • Erry Fuadillah UIN Sunan Gunung Djati Bandung Author
  • Lala Septem Riza Universitas Pendidikan Indonesia Author
  • Rani Megasari Universitas Pendidikan Indonesia Author

DOI:

https://doi.org/10.63461/cadikajournal.v21.296

Keywords:

automatic question generator (AQG), independent education, comprehensive review, systematic literature review

Abstract

Automatic question generator (AQG) technology is a system developed to create questions automatically from input in the form of text, images, and videos. AQG has been developed using various approaches such as natural language processing (NLP), statistical approaches, and other machine approaches. AQG has a very important role in the world of education, especially in independent education, because it can be used as a good evaluation medium for students. Utilizing AQG in independent education gives students full control to determine their learning. AQG turns learning into a more interactive experience by generating questions that can trigger critical thinking and problem-solving skills. AQG technology developed in independent learning will encourage students to respond actively to the material and understand concepts more deeply. Approximately 60% of research related to AQG has been conducted for assessment, 18% for knowledge acquisition, and the remainder for validation and other purposes. This research was conducted by conducting a comprehensive review of 63 articles related to AQG in education.

References

Abelha, M., Fernandes, S., Mesquita, D., Seabra, F., & Ferreira-oliveira, A. T. (2020). Graduate Employability and Competence Development in Higher Education — A Systematic Literature Review Using PRISMA. Sustainability, 12. https://doi.org/doi:10.3390/su12155900

Akyön, F. Ç., Çavuşoğlu, D., Cengiz, C., Altinuç, S. O., & Temizel, A. (2022). Automated question generation and question answering from Turkish texts. Turkish Journal of Electrical Engineering and Computer Sciences, 30(5), 1931–1940. https://doi.org/10.55730/1300-0632.3914

Alhashedi, S., Suaib, N. M., & Bakri, A. (2022). Arabic Automatic Question Generation Using Transformer Model. https://wwww.easychair.org/publications/preprint_download/tzZ2

Alshboul, J., & Baksa-Varga, E. (2022). A Review of Automatic Question Generation in Teaching Programming. (IJACSA) International Journal of Advanced Computer Science and Applications, 13(10), 45–51. https://doi.org/10.14569/IJACSA.2022.0131006

Anwar, F. S., Riza, L. S., & Rahman, E. F. (2018). Implementasi Levenshtein Distance untuk Sistem Penghasil Soal Error Identification dalam Toefl.

Araki, J., Rajagopal, D., Sankaranarayanan, S., Holm, S., Yamakawa, Y., & Mitamura, T. (2016). Generating questions and multiple-choice answers using semantic analysis of texts. COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers, 1125–1136.

Bi, S., Cheng, X., Fang Li, Y., Qu, L., Shen, S., Qi, G., Pan, L., & Jiang, Y. (2021). Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model. Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021, 4645–4654. https://doi.org/10.18653/v1/2021.findings-emnlp.397

Bitew, S. K., Hadifar, A., Sterckx, L., Deleu, J., Develder, C., & Demeester, T. (2022). Learning to Reuse Distractors to Support Multiple Choice Question Generation in Education. IEEE Transactions on Learning Technologies, 1–24. https://doi.org/10.1109/TLT.2022.3226523

Bulathwela, S., Muse, H., & Yilmaz, E. (2023). Scalable Educational Question Generation with Pre-trained Language Models. 327–339. https://doi.org/10.1007/978-3-031-36272-9_27

Chakankar, T., Shinkar, T., Waghdhare, S., Waichal, S., & Phadtare, M. M. M. (2023). Automated Question Generator using NLP. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 11(3), 216–221. https://doi.org/10.22214/ijraset.2023.49390

Chan, Y.-H., Chung, H.-L., & Fan, Y.-C. (2021). Improving Controllability of Educational Question Generation by Keyword Provision. http://arxiv.org/abs/2112.01012

Chen, G., Yang, J., & Gasevic, D. (2019). A Comparative Study on Question-Worthy Sentence Selection Strategies for Educational Question Generation. AIED, 1, 59–70. https://doi.org/10.1007/978-3-030-23204-7

Daar, G. F. (2020). Students’ independent learning implementation during learning from home period (a study at Unika Santu Paulus Ruteng, Flores). Journal of Applied Studies in Language, 4(2), 313–320. https://doi.org/10.31940/jasl.v4i2.2164

Das, B., Majumder, M., Phadikar, S., & Sekh, A. A. (2019). Automatic generation of fill-in-the-blank question with corpus-based distractors for e-assessment to enhance learning. In Computer Applications in Engineering Education (Vol. 27, Issue 6, pp. 1485–1495). https://doi.org/10.1002/cae.22163

Dong, X., Lu, J., Wang, J., & Caverlee, J. (2023). Closed-book Question Generation via Contrastive Learning. EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, 3142–3154.

Duong, D. H. T., Son, N. H., Le, H., & Nguyen, M.-T. (2022). Learning to Generate Questions by Enhancing Text Generation with Sentence Selection. http://arxiv.org/abs/2212.12192

Elkins, S., Kochmar, E., Serban, I., & Cheung, J. C. K. (2023). How Useful Are Educational Questions Generated by Large Language Models? 536–542. https://doi.org/10.1007/978-3-031-36336-8_83

Ferlanda, M. R., Wahyudin, & Riza, L. S. (2022). Automatic Question Generation Jenis Summary Completion Untuk Calon Partisipan Ielts Menggunakan Metode Natural Language Processing Dan Deep Learning.

Firdaus, Y., & Riza, L. S. (2020). Automatic Generate Question untuk Short-Answer Question di Reading Comprehension IELTS Menggunakan Metode Natural Language Processing dan Algoritma K-Nearest Neighbor.

Flor, M., & Riordan, B. (2018). A semantic role-based approach to open-domain automatic question generation. Proceedings of the 13th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018, 2012, 254–263. https://doi.org/10.18653/v1/w18-0530

Fung, Y., Kwok, J. C., Lee, L., & Chui, K. T. (2020). Automatic Question Generation System for English Reading Comprehension. ICTE 2020, 1, 136–146.

Giovani, R. . (2021). Automatoc Question Generation untuk Soal Vocabulary pada Reading Comprehension TOEFL Menggunakan Algoritma Learning Vector Quantization.

Guin, N., & Lefevre, M. (2022). An Authoring Tool based on Semi-automatic Generators for Creating Self-assessment Exercises. International Conference on Computer Supported Education, CSEDU - Proceedings, 1(Csedu), 181–188. https://doi.org/10.5220/0010996100003182

Gumaste, P., Joshi, S., Khadpekar, S., & Mali, S. (2020). Automated Question Generator System Using NLP Libraries. International Research Journal of Engineering and Technology (IRJET), 7(6), 4568–4572. https://www.irjet.net/archives/V7/i6/IRJET-V7I6848.pdf

Gupta, D., Chauhan, H., Tej, A. R., Ekbal, A., & Bhattacharyya, P. (2020). Reinforced Multi-task Approach for Multi-hop Question Generation. COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference, 2760–2775. https://doi.org/10.18653/v1/2020.coling-main.249

Indrihapsari, Y., Jati, H., Wardani, R., & Setialana, P. (2023). A Comparison of OpenNMT Sequence Model for Indonesian Automatic Question Generation. ELINVO (Electronics, Informatics, and Vocational Education), 8(1), 55–63. https://doi.org/https://doi.org/10.21831/elinvo.v8i1.56491 A

Ishak, D. (2021). Mechanism , Implementation , and Challenges in Independent Campus Education Policy in Indonesia. International Journal of Science and Society, 3(4), 52–63.

Kalman, R., MacIas Esparza, M., & Weston, C. (2020). Student views of the online learning process during the covid-19 pandemic: A comparison of upper-level and entry-level undergraduate perspectives. Journal of Chemical Education, 97(9), 3353–3357. https://doi.org/10.1021/acs.jchemed.0c00712

Kulshreshtha, D., Shayan, M., Belfer, R., Reddy, S., Serban, I. V., & Kochmar, E. (2022). Few-Shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems. Frontiers in Artificial Intelligence and Applications, 351(May), 17–30. https://doi.org/10.3233/FAIA220062

Kumar, N. A., Fernandez, N., Wang, Z., & Lan, A. (2023). Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank. http://arxiv.org/abs/2306.08847

Kumar, V., Chaki, R., Talluri, S. T., Ramakrishnan, G., Li, Y.-F., & Haffari, G. (2019). Question Generation from Paragraphs: A Tale of Two Hierarchical Models. Computation and Language (Cs.CL). http://arxiv.org/abs/1911.03407

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

Kusuma, S. F., & Alhamri, R. Z. (2018). Generating Indonesian Question Automatically Based on BloGenerating Indonesian Question Automatically Based on Bloom’s Taxonomy Using Template Based Methodom’s Taxonomy Using Template Based Method. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 3(2), 145–152. https://doi.org/10.22219/kinetik.v3i2.650

Kusuma, S. F., Siahaan, D. O., & Fatichah, C. (2020). Automatic Question Generation in Education Domain Based on Ontology. CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020, 251–256. https://doi.org/10.1109/CENIM51130.2020.9297991

Last, M., & Danon, G. (2020). Automatic question generation. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(6), 1–11. https://doi.org/10.1002/widm.1382

Lee, C. H., Chen, T. Y., Chen, L. P., Yang, P. C., & Tsai, R. T. H. (2018). Automatic question generation from children’s stories for companion chatbot. Proceedings - 2018 IEEE 19th International Conference on Information Reuse and Integration for Data Science, IRI 2018, 491–494. https://doi.org/10.1109/IRI.2018.00078

Liu, M., Calvo, R. A., & Rus, V. (2010). Automatic Question Generation for Literature Review Writing Support. Artificial Intelligence and Lecture Notes in Bioinformatics.

Marrese-Taylor, E., Nakajima, A., Matsuo, Y., & Yuichi, O. (2018). Learning to Automatically Generate Fill-In-The-Blank Quizzes. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 152–156. https://doi.org/10.18653/v1/w18-3722

Mawardi, Agustin, & Atalya. (2020). Open Access Dominant Factor Analysis of Independent Learning as a Key Success Factors of Student Learning. American Journal of Humanities and Social Sciences Research (AJHSSR), 9, 229–235.

Mulla, N., & Gharpure, P. (2023). 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

Muse, H., Bulathwela, S., & Yilmaz, E. (2023). Pre-training with Scientific Text Improves Educational Question Generation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16288–16289. https://doi.org/10.1609/aaai.v37i13.27004

Nguyen, H. A., Bhat, S., Moore, S., Bier, N., & Stamper, J. (2022). Towards Generalized Methods for Automatic Question Generation in Educational Domains. Springer International Publishing. https://doi.org/10.1007/978-3-031-16290-9

Oh, S., Go, H., Moon, H., Lee, Y., Jeong, M., Lee, H. S., & Choi, S. (2023). Evaluation of Question Generation Needs More References. http://arxiv.org/abs/2305.16626

Pandraju, S., & Mahalingam, S. G. (2021). Answer-Aware Question Generation from Tabular and Textual Data using T5. International Journal of Emerging Technologies in Learning, 16(18), 256–267. https://doi.org/10.3991/ijet.v16i18.25121

Papasalouros, A., & Chatzigiannakou, M. (2018). Semantic web and question generation: An overview of the state of the art. MCCSIS 2018 - Multi Conference on Computer Science and Information Systems; Proceedings of the International Conferences on e-Learning 2018, 2018-July, 189–192.

Park, J., Cho, H., & Lee, S. goo. (2018). Automatic generation of multiple-choice fill-in-the-blank question using document embedding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 10948 LNAI. Springer International Publishing. https://doi.org/10.1007/978-3-319-93846-2_48

Patra, R., & Saha, S. K. (2018a). A hybrid approach for automatic generation of named entity distractors for multiple choice questions. Education and Information Technologies, 24(2), 973–993. https://doi.org/10.1007/s10639-018-9814-3

Patra, R., & Saha, S. K. (2018b). Automatic generation of named entity distractors of multiple choice questions using web information. In Advances in Intelligent Systems and Computing (Vol. 710). Springer Singapore. https://doi.org/10.1007/978-981-10-7871-2_49

Pertiwi, A. D., Riza, L. S., Rahman, E. F., & Abdullah, C. U. (2018). Sistem Penghasil Soal Sentence Completion dalam TOEFL menggunakan Metode K-Nearest Neighbor dan Natural Language Processing.

Putera, G. S. A., & Riza, L. S. (2019). Automatic Question Generation untuk Pronoun Reference Question di Reading Comprehension untuk Toefl Menggunakan Metode Natural Language Processing, Tree Processing, dan Heuristik.

Raina, V., & Gales, M. (2022). Multiple-Choice Question Generation: Towards an Automated Assessment Framework. http://arxiv.org/abs/2209.11830

Rarasati, Y., Tiyas, I., & Febriyanti, S. (2016). The Use of Ict To Increase Learners’ Independece. International Seminar on English Language Teaching (ISELT 2016), Iselt 2016, 251–260.

Riza, L. S., Pertiwi, A. D., Rahman, E. F., Munir, & Abdullah, C. U. (2019). Question generator system of sentence completion in TOEFL using NLP and K-nearest Neighbor. Indonesian Journal of Science and Technology, 4(2), 294–311. https://doi.org/10.17509/ijost.v4i2.18202

Riza, L. S., Syaiful Anwar, F., Rahman, E. F., Abdullah, C. U., & Nazir, S. (2020). Natural Language Processing and Levenshtein Distance for Generating Error Identification Typed Questions on TOEFL. Journal of Computers for Society, 1(1), 1–23.

Rodrigues, H., Nyberg, E., & Coheur, L. (2023). Using Implicit Feedback to Improve Question Generation. http://arxiv.org/abs/2304.13664

Rodríguez Rocha, O., & Faron Zucker, C. (2018). Automatic Generation of Quizzes from DBpedia According to Educational Standards. The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018, 1035–1041. https://doi.org/10.1145/3184558.3191534

Sarnoto, A. Z., Sastradiharja, E. E. J., & Mansur, A. (2022). Prospects And Challenges Of Implementation Of Independent Learning-Independent Campus In Higher Education During The Covid-19 Pandemic. Webology, 19(2), 3343–3358.

Scharpf, P., Schubotz, M., Spitz, A., Greiner-Petter, A., & Gipp, B. (2022). Collaborative and AI-aided Exam Question Generation using Wikidata in Education. CEUR Workshop Proceedings, 3262(Wikidata), 1–16. https://doi.org/10.13140/RG.2.2.30988.18568

Shimmei, M., & Matsuda, N. (2022). Automatic Question Generation for Evidence-based. 1–8.

Silva, V. A., Bittencourt, I. I., & Maldonado, J. C. (2019). Automatic Question Classifiers: A Systematic Review. IEEE Transactions on Learning Technologies, 12(4), 485–502. https://doi.org/10.1109/TLT.2018.2878447

Soni, S., Kumar, P., & Saha, A. (2019). Automatic Question Generation: A Systematic Review. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3403926

Srivastava, M., & Goodman, N. (2021). Question Generation for Adaptive Education. ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2, 692–701. https://doi.org/10.18653/v1/2021.acl-short.88

Steuer, T., Filighera, A., Meuser, T., & Rensing, C. (2021). I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection. IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, XX(X), 1–14. http://arxiv.org/abs/2110.04123

Steuer, T., Filighera, A., Tregel, T., & Miede, A. (2022). Educational Automatic Question Generation Improves Reading Comprehension in Non-native Speakers: A Learner-Centric Case Study. Frontiers in Artificial Intelligence, 5(June), 1–14. https://doi.org/10.3389/frai.2022.900304

Suardipa, I. P., & Primayana, K. H. (2020). Peran Desain Evaluasi Pembelajaran Untuk Meningkatkan Kualitas Pembelajaran. Widyacarya, 4(2), 88–100. http://jurnal.stahnmpukuturan.ac.id/index.php/widyacarya/article/view/796

Sun, X., Tang, H., & Xu, chengzhong. (2023). Inflected Forms Are Redundant in Question Generation Models. http://arxiv.org/abs/2301.00397

Sung, E. (2019). Fostering Computational Thinking in Technology and Engineering Education. Technology and Engineering Teacher, 78(5), 9–13.

Surana, H. M., Bhatnagar, G., Srivastava, A., & Srinivasa, G. (2019). NVR Guess: Automated Question Generation for Honing NonVerbal Reasoning Skills. Proceedings - IEEE 10th International Conference on Technology for Education, T4E 2019, 210–213. https://doi.org/10.1109/T4E.2019.00047

Susanti, Y., Iida, R., & Tokunaga, T. (2015). Automatic generation of english vocabulary tests. CSEDU 2015 - 7th International Conference on Computer Supported Education, Proceedings, 1(October), 77–87. https://doi.org/10.5220/0005437200770087

Thotad, P., Kallur, S., & Amminabhavi, S. (2022). Automatic Question Generator Using Natural Language Processing. Journal of Pharmaceutical Negative Results, 13(10), 2759–2764. https://doi.org/10.47750/pnr.2022.13.S10.330

Tsai, D. C. L., Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2021). Automatic question generation for repeated testing to improve student learning outcome. In Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021 (pp. 339–341). https://doi.org/10.1109/ICALT52272.2021.00108

Vincentio, K., & Suhartono, D. (2022). Automatic Question Generation Monolingual Multilingual pre-trained Models using RNN and Transformer in Low Resource Indonesian Language. Informatica, 46(7), 103–118. https://doi.org/10.31449/inf.v46i7.4236

Wang, X., Liu, B., Tang, S., & Wu, L. (2023). SkillQG: Learning to Generate Question for Reading Comprehension Assessment. http://arxiv.org/abs/2305.04737

Wang, Z., & Baraniuk, R. (2023). MultiQG-TI: Towards Question Generation from Multi-modal Sources. http://arxiv.org/abs/2307.04643

Wu, Z., Jia, X., Qu, F., & Wu, Y. (2022). Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation. http://arxiv.org/abs/2209.04179

Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing [Review Article]. IEEE Computational Intelligence Magazine, 13(3), 55–75. https://doi.org/10.1109/MCI.2018.2840738

Yuan, X., Wang, T., Wang, Y.-H., Fine, E., Abdelghani, R., Lucas, P., Sauzéon, H., & Oudeyer, P.-Y. (2022). Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation. http://arxiv.org/abs/2209.11000

Zhang, C., & Wang, J. (2022). Tag-Set-Sequence Learning for Generating Question-answer Pairs. International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings, 1, 138–147. https://doi.org/10.5220/0011586800003335

Zhang, L., & VanLehn, K. (2021). Evaluation of auto-generated distractors in multiple choice questions from a semantic network. Interactive Learning Environments, 29(6), 1019–1036. https://doi.org/10.1080/10494820.2019.1619586

Zhang, T., Liu, Y., & Quan, P. (2018). Domain specific automatic Chinese multiple-type question generation. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, 1967–1971. https://doi.org/10.1109/BIBM.2018.8621162

Zhao, Z., Hou, Y., Wang, D., Yu, M., Liu, C., & Ma, X. (2022). Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1, 5073–5085. https://doi.org/10.18653/v1/2022.acl-long.348

Zhu, F., Lei, W., Wang, C., Zheng, J., Poria, S., & Chua, T. (2021). Retrieving and Reading : A Comprehensive Survey on Open-domain Question Answering. Arxiv, Artificial Intelligence (Cs.AI), 1–21. https://doi.org/arXiv:2101.00774v3

Zou, B., Li, P., Pan, L., & Aw, A. T. (2022). Automatic True/False Question Generation for Educational Purpose. BEA 2022 - 17th Workshop on Innovative Use of NLP for Building Educational Applications, Proceedings, Bea, 61–70. https://doi.org/10.18653/v1/2022.bea-1.10

Downloads

Published

2025-10-31

Issue

Section

Articles

How to Cite

Fuadillah, E., Riza, L. S., & Megasari, R. (2025). Comprehensive Review: Transforming Self-Education through Automatic Question Generation Technology. Master Journal of Future Education, 2(1), 27-42. https://doi.org/10.63461/cadikajournal.v21.296