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Automatic question generation and answer assessment: a survey
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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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Cites
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015 · cited 3×
“They applied WordNet (Miller 1995) for obtaining the synonym, antonym, and hyponym to develop the question key and the distractors.”
MethodInfluentialSQuAD: 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
Proceedings of the 19th International Joint Conference on Artificial Intelligence
Pujol, J. M., Delgado, J., Ramon Sangüesa, A., & Flache, A. (2005) · International Joint Conference on Artificial Intelligence · Cited by 5,771
VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
Practical Assessment, Research & Evaluation, 2000-2001.
Rudner, L. M., & Schafer, W. D. (2001) · Practical assessment, research & evaluation · Cited by 3,905
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al. (2016) · arXiv · doi:10.48550/arxiv.1611.09268 · Cited by 2,473
Natural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., & Girshick, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.215 · Cited by 1,781
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690
Research works printed but not linked: 4
- Agarwal, M. (2012). Cloze and open cloze question generation systems and their evaluation guidelines. Master’s thesis. International Institute of Information Technology, (IIIT), Hyderabad, India.
- Carmichael, M., Reid, A., Karpicke, J.D. (2018). Assessing the impact of educational video on student engagement, critical thinking and learning: The Current State of Play, (pp. 1–21): A SAGE Whitepaper, Sage Publishing.
- Mora, I.M., de la Puente, S.P., Nieto, X.G. (2016). Towards automatic generation of question answer pairs from images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, (pp. 1–2).
- Pino, J., Heilman, M., Eskenazi, M. (2008). A selection strategy to improve cloze question quality. In Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains, 9th International Conference on Intelligent Tutoring Systems. Springer, Montreal, (pp. 22–34).
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“It not only alleviates the manual workload of educators but also introduces scalability to assessments (Das et al., 2021).”
Mulla, N., & Gharpure, P. (2023) · Progress in Artificial Intelligence · doi:10.1007/s13748-023-00295-9 · Cited by 132
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Xu, Y., Wang, D., Yu, M., Ritchie, D., Yao, B., Wu, T., Zhang, Z., Li, T. J.-J., Bradford, N., Sun, B., et al. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.34 · Cited by 73
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“Existing automatic question generation techniques are good at creating factual questions [27, 49], while not being able to generate questions that target higher Bloom goals [14].”
BackgroundGenerative Students: Using LLM-Simulated Student Profiles to Support Question Item Evaluation
Lu, X., & Wang, X. (2024) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3657604.3662031 · Cited by 55
“…questions from students [42, 57], perusing prior students’ solutions and mistakes to generate new questions [46, 47], us-ing teacher-AI collaborative approaches where teachers receive AI suggestions[29], and fully automated techniques leveraging AI [5, 13, 21, 28, 30, 31, 43, 48, 50].”
BackgroundAutomatic story and item generation for reading comprehension assessments with transformers
Bulut, O., & Yildirim‐Erbasli, S. N. (2022) · International Journal of Assessment Tools in Education · doi:10.21449/ijate.1124382 · Cited by 40
“…technologies can facilitate the search for appropriate reading materials and items for teachers, such as text generation using language models and automatic item generation (see Das et al. [2021] for a detailed summary of the state-of-the-art techniques used to generate items automatically).”
Quiz maker: Automatic quiz generation from text using NLP
Gabajiwala, E., Mehta, P., Singh, R., & Koshy, R. (2022) · Lecture Notes in Electrical Engineering · doi:10.1007/978-981-19-5037-7_37 · Cited by 50
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 44
“Severalotherrelatedliter-aturereviewsdidnotfocusonNQG(Zhangetal.,2021;Dasetal.,2021;Amideiet al., 2018).”
Shuriaqi, S. A., Abdulsalam, A. A., Masters, K., Zidoum, H. M., & Alzaabi, A. A. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8100139 · Cited by 29
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“Detailed reviews of test generation technologies are presented in [55,63–65].”
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“Question Construction (QC) aims to automatically construct questions from a given context, which performs a significant role in education [127].”
Enhancing Engineering Education Through LLM-Driven Adaptive Quiz Generation: A RAG-Based Approach
Gopi, S., Sreekanth, D., & Dehbozorgi, N. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893146 · Cited by 19
“The development of automatic question generation systems using LLMs has been validated for their efficiency in creating diverse and challenging questions that adapt to student needs [15].”
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BackgroundAkdeniz, H., Clark, T., & Roberts, J. L. (2025) · Journal of Advanced Academics · doi:10.1177/1932202x251349917 · Cited by 15
“These systems provide a more personalized learning experience for students, which is particularly bene fi cial for gifted students who require more challenging and complex tasks (Das et al., 2021; Kurdi et al., 2020).”
Awalurahman, H. W., Aji, R. F., & Budi, I. D. (2025) · IEEE Access · doi:10.1109/access.2025.3590423 · Cited by 21
“Das et al. [12] extend the existing literature review by covering different modalities and assessment meth-ods that have been used in the existing literature.”
InfluentialAutomated assessment of subjective assignments: A hybrid approach
Birla, N., Jain, M. K., & Panwar, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117315 · Cited by 15
VideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment
Forkan, A. R. M., Kang, Y., Jayaraman, P. P., Du, H., Thomson, S., Kollias, E., & Wieland, N. (2023) · International Journal of Advanced Corporate Learning (iJAC) · doi:10.3991/ijac.v16i1.35207 · Cited by 15
Roles for Information in Trace Data Used to Model Self-Regulated Learning
Winne, P. H. (2023) · Advances in analytics for learning and teaching · doi:10.1007/978-3-031-30992-2_11 · Cited by 12
Agrawal, R., Mishra, H., Kandasamy, I., Terni, S. R., & Kandasamy, W. B. V. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122470 · Cited by 12
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Li, R., Wang, Y., Zheng, C., Jiang, Y.-H., & Jiang, B. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-64315-6_48 · Cited by 11
Fine-Tuning a Large Language Model with Reinforcement Learning for Educational Question Generation
Lamsiyah, S., Mahdaouy, A. E., Nourbakhsh, A., & Schommer, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-64302-6_30 · Cited by 12
Mulla, N., & Gharpure, P. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01262-2 · Cited by 10
“In general, question generation models focus on generating questions, based on training on publicly curated datasets [4].”
BackgroundDevelopment of system for generating questions, answers, distractors using transformers
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“Subsequent articles have tried to enhance the effectiveness of RNN-based Seq2Seq structures by using question types [18], [19], response position characteristics [20], [21], response splitting [22], [23] and implementing an internal attention mechanism [24], [25].”
BackgroundBachiri, Y.-A., & Mouncif, H. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-06458-6_11 · Cited by 10
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“An example of a narrower domain of low-variance text problems would be the problem of machine-gener-ating texts that resemble first aid or driving school theory test questions [22].”
Wang, J., Tang, H., Kantor, T., Soltani, T., Popov, V., & Wang, X. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642587 · Cited by 9
“However, prior work on question generation are primarily text-based [13, 45, 53, 54, 77, 89, 91, 92, 97, 105, 107].”
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Johnson, B. G., Campenhout, R. V., Jerome, B., Castro, M. F., Bistolfi, R., & Dittel, J. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00394-1 · Cited by 8
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Does ChatGPT Spell the End of Automatic Question Generation Research?
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“We can use the question generation mobasedasis on the large language model [14].”
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Renes, J., van der Vleuten, C. P. M., & Collares, C. F. (2022) · Medical Teacher · doi:10.1080/0142159x.2022.2137011 · Cited by 6
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BackgroundA Review of Automatic Question Generation in Teaching Programming
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BackgroundMulti-Hop Question Generation Using Hierarchical Encoding-Decoding and Context Switch Mechanism
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“However, the GPT-2 models have the best performance on BLEU-[2,3,4].”
MethodSewunetie, W. T., & Kovács, L. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v28.i3.pp1738-1748 · Cited by 4
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Sewunetie, W. T., & Kovács, L. (2023) · International Carpathian Control Conference · doi:10.1109/iccc57093.2023.10178946 · Cited by 5
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Adithya, R., Ravi, N., Daivajna, O. N., Niranjanamurthy, M., & Raviram, V. (2023) · International Conference on Mobile Networks and Wireless Communications (ICMNWC) · doi:10.1109/icmnwc60182.2023.10435767 · Cited by 3
“One framework generates a score based on the semantic comparability level using the assigned marking pattern for the question [7].”
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Jadhav, P., Deore, S., Aute, S., Gaikwad, R., Barphe, S. S., & Meshram, E. (2024) · International Conference on Sustainable Expert Systems (ICSES) · doi:10.1109/icses63445.2024.10763124 · Cited by 3
“In evaluating responses to objective-type questions, recent developments in deep literacy-grounded natural language processing (NLP) show promise [6].”
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“[17].”
Learning problem generator for introductory programming courses
Prokudin, A., Александрович, С. О., & Denisov, M. (2023) · Software Impacts · doi:10.1016/j.simpa.2023.100519 · Cited by 3
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Banerjee, A., Kumar, V., & Banik, D. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-34222-6_10 · Cited by 3
Shah, A., Devmane, A., Ranka, M., & Churi, P. P. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12045-4 · Cited by 3
“In any learning system, assessment is essential (Das et al., 2021).”
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Park, G., & Choi, S. (2025) · Journal of Curriculum and Evaluation · doi:10.29221/jce.2025.28.1.215 · Cited by 5
Zhong, C., Ye, F., Wang, Z., Jigeer, A., & Zhan, Z. (2025) · International Conference on Educational and Information Technology · doi:10.1109/iceit64364.2025.10975892 · Cited by 4
“Finally, traditional manual question design is inefficient and struggles to meet the growing demand for high-quality test items in interdisciplinary education [7].”
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Kang, Y., Forkan, A. R. M., Jayaraman, P. P., Wieland, N., Kollias, E., Du, H., Thomson, S., & Li, Y.-F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.01229 · Cited by 3
“In digital learning domain, Authors in [23] proposed an NLP-based method that combined syntax-based and semantic-based methods for AQG.”
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Kang, Y., Forkan, A. R. M., Banerjee, A., Jayaraman, P. P., McCosker, A., Kim, S.-S., Wieland, N., & Kollias, L. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3620274 · Cited by 2
“The survey work [5] pinpointed that “automatic assessments from video content can help learners to learn quickly in a new area.””
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“Auto-generating assessments is an established research topic in technology-enabled education [39].”
Rockembach, G. R., & Thom, L. H. (2026) · Revista Brasileira de Informática na Educação · doi:10.5753/rbie.2026.6093 · Cited by 2
Ding, G., Ou, Y., & Li, S. (2026) · Teaching and Teacher Education · doi:10.1016/j.tate.2026.105585 · Cited by 2
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Wang, J., Rong, W., Bai, J., Sun, Z., Ouyang, Y., & Xiong, Z. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3325549 · Cited by 2
“With the development of natural language processing technology, it is now possible to automatically generate MCQs, which greatly improves the efficiency of designing MCQs and saves the time of teachers [4], [5].”
BackgroundHamzaoui, Bachiri, Ouassam, E., Mouncif, H., & Bouikhalene, B. (2024) · International Conference on Networking, Information Systems & Security (NISS) · doi:10.1145/3659677.3659823 · Cited by 4
Arabic Question Generation Using Transformers
Alajmi, A., Altabaa, H., Abed, S., & Ahmad, I. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3701559 · Cited by 2
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Winata, A. J., Surjawan, D. J., & Mawardi, V. C. (2025) · International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS) · doi:10.1109/icadeis65852.2025.10933227 · Cited by 2
“Das et al. identified in their survey that many existing AQG systems face challenges in producing high-quality and relevant questions [4].”
Dong, X., Li, Z., Su, H., Xue, J., & Dang, X. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11237-3 · Cited by 2
“Existing related research predominantly focuses on generating multiple-choice questions (Kurdi et al. 2020; Das et al. 2021; Zhang et al. 2021) without discussing the DG task.”
Speaker Diarization and BERT-Based Model for Question Set Generation from Video Lectures
Nittala, S., Agarwal, P. R., Vishnu, R., & Shanbhag, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0095-2_42 · Cited by 2
On an Integrated Assessment for the Students Within an Academic Consortium
Popescu, D. A., Daniela-Maria, C., & Bold, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-32883-1_46 · Cited by 2
Resolving Single-Sentence Answers for the development of an Automated Assessment Process
Raut, A. P., & Mahender, C. N. (2023) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc457730.2023.10262870 · Cited by 2
“The three approaches to question generation that were examined in this research [15] were subjective question generation with learner response evaluation, a summary of visual question generation techniques, and methods for objective question generation.”
BackgroundVersion Control System for Auto Generation of Question Papers
Naik, P. G., & Oza, K. S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-0769-4_33 · Cited by 1
Enhancing Question Generation in Bahasa Using Pretrained Language Models
Fredyan, R., Wiyono, I. S., Suhartono, D., Majiid, M. R. N., & Purnomo, F. (2024) · Revue d intelligence artificielle · doi:10.18280/ria.380421 · Cited by 1
“In the case of objective inquiries, the response comprises quoting a word or phrase from the provided paragraph [7].”
Arunnehru, J., Vishnu, S., Ganapathyappan, K., & Kumar, D. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-82386-2_26 · Cited by 1
Can Question Validation Criteria Improve the Quality of LLM-Generated Multiple-Choice Questions?
Morland, R., Jhajj, G., Yan, H., Lin, F. O., Mellick, J., Treu, R., Farrelly, G., Zariski, A., Al-Shamali, F. M., Wang, Z., et al. (2025) · IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00073 · Cited by 1
“Das et al [27] performed a survey on AQG techniques used to generate questions and evaluate their answers.”
Zhao, P., Blake, J., & Pyshkin, E. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.01.070 · Cited by 1
Automatic Question Generation with Large Language Models: A Survey
de Oliveira, R. M., Hernández, A., Garbin, M. C., dos Reis, J. C., & Azevedo, R. (2026) · ACM Computing Surveys · doi:10.1145/3832773 · Cited by 1
“In the literature, relevant reviews on AQG can be found [27, 97, 105].”
An Online MCQ sub-system for CrsMgr
Ratcheva, M. G., Navale, R., & Desai, B. C. (2022) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1145/3548785.3548789 · Cited by 1
“Many datasets such as SQuAD, 30MQA, MS MARCO, RACE, NewsQA, Trivia QA, TabMCQ, SciQ and NarrativeQA contain question-answer sets and are used for training of question-answering machine learning models [23].”
MethodWang, J., Popov, V., & Wang, X. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586182.3615816 · Cited by 1
Сычев, О. А., Александрович, С. О., Prokudin, A., Денисов, М. Е., & Denisov, M. (2024) · Международный журнал Программные продукты и системы · doi:10.15827/0236-235x.146.201-212 · Cited by 1
Popescu, D. A., Bold, N., & Ileana, M. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-4246-2_15 · Cited by 1
EduTube : Enhancing Credibility and User Experience in Online Learning
Bothra, R., -, D. G., -, V. P., & Deshmukh, S. (2023) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2023.v05i05.7145
“The comprehensive survey presented in "Automatic question generation and answer assessment: a survey" [5] provides a valuable overview of the state-of-the-art in automatic question generation and answer assessment.”
Candidate Speech Extraction from Multi-speaker Single-Channel Audio Interviews
Pandharipande, M. A., & Kopparapu, S. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48309-7_18
A Transfer Learning Approach Interaction in an Academic Consortium
Popescu, D. A., Daniela-Maria, C., & Bold, N. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8385-8_16
Stotsky, A. A., & Wik, T. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.08.460
“Preparation of constructively aligned examination (which measures precisely the ILO competences) with a given number of points takes significant amount of time of the control educators, Das et al. (2021).”
BackgroundLee, J. (2024) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2024.27.5.002
Boubaker, A., & Fang, Y. (2024) · International Conference on Educational Technology Management (ICETM) · doi:10.1145/3711403.3711450
“As noted by a meta-analysis by Das et al. [5], these systems typically generate questions based on predefined templates or rules.”
Developing a Tuberculosis Q&A Database Using Q&A-ET: Question and Answer Evaluator Toolkit
Akl, J. A., Jaoude, C. A., Chami, Z. A., Guyeux, C., Laiymani, D., & Sola, C. (2025) · IEEE Middle East and North Africa Communications Conference (MENACOMM) · doi:10.1109/menacomm62946.2025.10911001
Kaqg: A Knowledge-Graph-Enhanced Rag for Difficulty-Controlled Question GenerationPreprint
Chen, C., & 許, 銘. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5254660
A Fuzzy-Based Subjective Answer Assessment Grading System Using Sentence Transformer
Senthilkumar, P., & Nandhini, K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5955-5_31
Leveraging Generative AI for Enhanced Question Generation in Reading Comprehension
Vedantham, U., Chouti, N., Kumar, V., Mahatme, G., Kundu, D., & Ghosh, K. (2025) · International Conference on Computing, Intelligence, and Application (CIACON) · doi:10.1109/ciacon65473.2025.11189701
“In education, systematic reviews conducted by Kurdi et al. [10] and Das et al. [11] discuss the importance of automatic QG systems in technology-enhanced learning environments.”
RELIA: Empathetic Reading Guide with Generative Artificial Intelligence
Sherman, R., & Araújo, S. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-05673-3_6
Evaluating Generative AI and Human Performance in Question-Answer Validation Tasks
Opaiţ, E.-E., Duca, A., & Olariu, M.-E. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.522
Smarter Questions, Smaller Models: RAG-Enhanced Multiple-Choice Question Generation for POSCOMP
Junior, J. R. D. S. A., Marinho, L. B., Campos, L. S., Lima, K. N. D. S., Pereira, D. E., Cavalcanti, H. B., Ramos, A. T., & de Araújo, E. C. (2025) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/sbie.2025.12854 · Cited by 1
“With the advent of LLMs, automatic QG has undergone a significant transformation [Meißner et al. 2024], with recent studies increasingly leveraging their advanced language understanding and generation capabilities [Das et al. 2021].”
Kituku, B., Araka, E., & Maina, E. M. (2025) · International Conference on Information and Communication Technology for Development for Africa (ICT4DA) · doi:10.1109/ict4da67218.2025.11282535
Automated Answer Paper Evaluation (AAPE)
Roopa, C. K., Ashwin, D., Adithya, B., Ajith, C., & Chandan, J. (2025) · International Conference on Intelligent Computing and Communication Systems (CICCS) · doi:10.1109/ciccs66437.2025.11280115
“(Das, Majumder, Phadikar, and Sekh 2021) [5] surveyed techniques for both question generation and answer evaluation using NLP, ML, and computer vision.”
Shrisha, S., Sindhu, K., Kandula, S., & Udyavara, S. S. (2025) · IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE) · doi:10.1109/icwite64848.2025.11306930
“Reference [7], covered different question generation techniques, datasets, and evaluation metrics, making it highly relevant for understanding Question Generation systems in educational mobile apps.”
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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