The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Automatic question generation and answer assessment: a survey
Open accessAbstract
Learning through the internet becomes popular that facilitates learners to learn anything, anytime, anywhere from the web resources. Assessment is most important in any learning system. An assessment system can find the self-learning gaps of learners and improve the progress of learning. The manual question generation takes much time and labor. Therefore, automatic question generation from learning resources is the primary task of an automated assessment system. This paper presents a survey of automatic question generation and assessment strategies from textual and pictorial learning resources. The purpose of this survey is to summarize the state-of-the-art techniques for generating questions and evaluating their answers automatically.
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“There are a few specific reviews, in particular domain areas of question generation. For example, the work done for question generation in the educational domain is discussed in [15].”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [16]
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“For instance, [3] investigates automated question generation through natural language processing, identifying core challenges like sentence simplification, transformation, and ranking.”
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Rami, C., Ouahbi, I., Makkaoui, K. E., & Maleh, Y. (2026) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-97825-8_2
Mohamed Irfan Ali, J., p, A., Nithyaraj, U., & Prasad, J., V (2026) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca68095.2026.11485326
“In a survey conducted by Das et al. [5], a number of different automated question generation (AQG) and assessment methods were detailed with respect to the use of natural language processing (NLP) techniques.”
Soh, D. C. L., Atmosukarto, I., Rong, T., McLoughlin, I. V., Hao, L. J., Sirajudeen, M., Kan, J., Pranav, T., Zhang, B., Latiff, N. A. A., et al. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-16157-4_3
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“Subsequently, machine learning algorithms added statistical models to react to selection and distractor creation that were more scalable, albeit relying extensively on manually created features as well as subject-specific heuristics [6], [8].”
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Mu, N., & Mine, T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-1947-6_45
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Mekterović, I., Brkić, L., Tadijal, M., & Vanjak, T. (2026) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro70003.2026.11591576
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Ni, L., Yin, S., Denny, P. C., & Liu, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2864-5_24
Almeniana, F. C., Lagman, A. C., Tano, I. M., Victoriano, J. M., Espino, J., Mababa, J. C., & Cabrera, J. J. D. (2026) · IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) · doi:10.1109/i2cacis69435.2026.11600120
Štefanko, M., & Lang, J. (2026) · Information Technology Scientific Research Conference (IIT.SRC) · doi:10.1109/iit.src67760.2026.11629076
“Automatic question generation typically involves text extraction, segmentation, question generation, and evaluation [4], [6].”
A Review of Deep Learning-Based Question Generation: Challenges, Methods, and Future Directions
Yehia, M. F., Helmi, Y. M., & Bahloul, M. M. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01497-4
A Modular Multi-Agent Architecture for Automated Reading Comprehension Question Generation
Zhao, P., Blake, J., & Pyshkin, E. (2026) · International Conference on Information Technology and Education Technology (ITET) · doi:10.1109/itet70052.2026.11651012
“There are multiple approaches to Automatic Question Generation (AQG) [1] [2] [3].”
The Evolution of Question Generation: Analyzing the Impact of Large Language Models
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Zourlidou, S. (2026) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc70543.2026.11693841
“…multiple technical university courses using open-weight, locally deployed LLMs. Pre-LLM work on automatic question generation and answer assessment [6] surveyed methods for generating questions and evaluating answers, but did not benchmark LLM-based detection of pedagogical defects such as Bloom…”
Zhang, Y., & Fu, P. (2026) · International Conference on Computational Intelligence and Machine Learning (CIML) · doi:10.1109/ciml69155.2026.11692804
“Das et al. [1] conducted a systematic review of automatic test item generation and evaluation, showing that instructional generation driven by NLP is still limited by semantic consistency and knowledge alignment; Kasneci et al. [2] focused on the potential of large models in educational creation…”
DSIQ‐GEN: Automatic Generation and Classification of Data Science Interview Questions
Arafat, M. E., Császár, F. B., Farou, Z., & Orosz, T. (2026) · International Journal of Intelligent Systems · doi:10.1155/int/1523398
Metadata
- Authors
- Das, B., Majumder, M., Phadikar, S., & Sekh, A. A.
- Affiliations
- Haldia Institute of Technology (IN)
- North Bengal University (IN)
- Maulana Abul Kalam Azad University of Technology, West Bengal (IN)
- UiT The Arctic University of Norway (NO)
- Corresponding author
- Das, Bidyut
- Venue
- Research and Practice in Technology Enhanced Learning
- Published
- 2021-03-18
- Volume
- 16
- Issue
- 1
- ISSN
- 1793-2068 (ISSN-L)
- 1793-7078
- OpenAlex
- W3143827210
- Cited by
- 123
- References
- 82
- Citations by year
- 2026: 24 · 2025: 28 · 2024: 27 · 2023: 29 · 2022: 13 · 2021: 2
- FWCI
- 16.05
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Research and Practice in Technology Enhanced Learning (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- automatic question generation, automated assessment systems, learning resources, self-assessment, online learning
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