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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A few specific reviews exist for particular domain areas of question generation.
“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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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
“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 27 · see citing works
Automated Test Creation Using Large Language Models: A Practical Application
Hadzhikoleva, S., Rachovski, T., Ivanov, I., Hadzhikolev, E. N., & Dimitrov, G. P. (2024) · Applied Sciences · doi:10.3390/app14199125 · Cited by 21 · see citing works
“Detailed reviews of test generation technologies are presented in [55,63–65].”
Bachiri, Y.-A., & Mouncif, H. (2023) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v18i05.36589 · Cited by 21 · see citing works
Automatic distractor generation in multiple-choice questions: a systematic literature review
Awalurahman, H. W., & Budi, I. D. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2441 · Cited by 18 · 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
“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 · see citing works
“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].”
Context-Aware Auto-Encoded Graph Neural Model for Dynamic Question Generation using NLP
Dara, S., Srinivasulu, C., Babu, C. M., Ravuri, A., Paruchuri, T., Kilak, A. S., & Vidyarthi, A. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3626317 · Cited by 16 · see citing works
“Question-Answer generation (QAG) is an important task in natural language processing that involves generating questions from a given text [4].”
BackgroundAkdeniz, H., Clark, T., & Roberts, J. L. (2025) · Journal of Advanced Academics · doi:10.1177/1932202x251349917 · Cited by 15 · see citing works
“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 15 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Generating Contextualized Mathematics Multiple-Choice Questions Utilizing Large Language Models
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 · see citing works
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 · see citing works
Mulla, N., & Gharpure, P. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01262-2 · Cited by 10 · see citing works
“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
Barlybayev, A., & Matkarimov, B. T. (2024) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v14i2.pp1851-1863 · Cited by 15 · see citing works
“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 · see citing works
Aspect-based sentiment analysis of user-generated content from a microblogging platform
Drašković, D., & Milanović, S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01244-0 · Cited by 9 · see citing works
“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 · see citing works
“However, prior work on question generation are primarily text-based [13, 45, 53, 54, 77, 89, 91, 92, 97, 105, 107].”
BackgroundEnhancing Question Prediction with Flan T5 -A Context-Aware Language Model ApproachPreprint
Oza, J., & Yadav, H. S. (2023) · doi:10.22541/au.170258918.81486619/v1 · Cited by 9 · see citing works
Automatic Multiple-Choice Question and Answer (MCQA) Generation Using Deep Learning Model
Rao, M. C., Sreedhar, P. S. S., Bhanurangarao, M., & Sujatha, G. (2023) · Cognitive science and technology · doi:10.1007/978-981-99-2746-3_1 · Cited by 8 · see citing works
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 · see citing works
“However, English remains the dominant language in AQG research (Das et al., 2021; Kurdi et al., 2020).”
FAQ-Gen: An automated system to generate domain-specific FAQs to aid content comprehension
Kale, S., Khaire, G., & Patankar, J. (2024) · Journal of Computer-Assisted Linguistic Research · doi:10.4995/jclr.2024.21178 · Cited by 6 · see citing works
“Answer assessment strategies suggested in [13] have been taken into note to build an answer elaboration model that makes answers to questions to the point and easy to understand.”
Does ChatGPT Spell the End of Automatic Question Generation Research?
Singh, M., Patvardhan, C., & Lakshmi, C. V. (2023) · IEEE International Conference on Computer Vision and Machine Intelligence (CVMI) · doi:10.1109/cvmi59935.2023.10464618 · Cited by 6 · see citing works
“Here are some common approaches [4]: 1) Rule-based Approach: This method employs predefined rules and patterns to extract information from text and create questions.”
MethodNavigating the Landscape of Hint Generation Research: From the Past to the Future
Jangra, A., Mozafari, J., Jatowt, A., & Muresan, S. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00751 · Cited by 10 · see citing works
Multimodal LLM Question Generation for Children's Art Engagement via Museum Social Robots
Ferrato, A., Cristina Gena, A., Limongelli, C., & Sansonetti, G. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3708319.3733663 · Cited by 6 · see citing works
“Traditional approaches to QG have relied on rule-based systems, templates, and, more recently, deep learning methods that transform declarative content into interrogative forms [9, 16, 22, 33].”
On the Effects of Automatically Generated Adjunct Questions for Search as Learning
Zhu, P., Câmara, A., Roy, N., Maxwell, D., & Hauff, C. (2024) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3627508.3638332 · Cited by 5 · see citing works
“Some works have investigated the application of AQG to education [1, 8, 13, 19, 39, 40, 64, 65, 72].”
BackgroundHarnessing the power of ChatGPT to develop effective MCQ-based clinical pharmacy exams
Ahmed, A., Jamil, E., Abubakar, M., Batool, A., Akhtar, M. M., Nasiri, M. I., & Ullah, M. (2024) · Journal of Research on Technology in Education · doi:10.1080/15391523.2024.2425435 · Cited by 5 · see citing works
“They found comparable or superior results to traditional methods (Das et al., 2021; Kumar et al., 2023; Pugh et al., 2020).”
Neurosymbolic AI-based Framework For Sports Ball Identification Concerning Toddlers
Mangal, A., Vyas, T., Gupta, R., Tanwar, S., & Shahinzadeh, H. (2024) · CSI International Symposium on Artificial Intelligence and Signal Processing · doi:10.1109/aisp61396.2024.10475201 · Cited by 5 · see citing works
“We can use the question generation mobasedasis on the large language model [14].”
MethodA Systematic Review and Bibliometric Analysis of Automated Multiple-Choice Question Generation
Mitroulias, D., & Sioutas, S. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10010035 · Cited by 6 · see citing works
Renes, J., van der Vleuten, C. P. M., & Collares, C. F. (2022) · Medical Teacher · doi:10.1080/0142159x.2022.2137011 · Cited by 6 · see citing works
“In addition, developments in the automated marking of non-MCQs in terms of computational linguistics and machine learning are in progress (Das et al. 2021).”
BackgroundA Review of Automatic Question Generation in Teaching Programming
Alshboul, J., & Varga, E. B. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0131006 · Cited by 4 · see citing works
“Recent advances in deep learning-based natural language processing (NLP) offer promising solutions in answer assessment of objective-type questions [10].”
BackgroundTeaching Compilers: Automatic Question Generation and Intelligent Assessment of Grammars' Parsing
Conejo, R., Blanco, B. B., del Campo-Ávila, J., & Rodríguez, J. L. T. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3405565 · Cited by 4 · see citing works
“Most of the automated question generation systems are associated with texts that often require the use of natural language analysis techniques [13], [14], [15].”
BackgroundMulti-Hop Question Generation Using Hierarchical Encoding-Decoding and Context Switch Mechanism
Ji, T., Lyu, C., Cao, Z., & Cheng, P. (2021) · Entropy · doi:10.3390/e23111449 · Cited by 4 · see citing works
“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 · see citing works
“The advanced natural language processing (NLP) techniques employed for the textual question creation include natural language understanding (NLU) and natural language generation (NLG) [25].”
BackgroundОбзор существующих методов автоматической генерации задач с условиями на естественном языке
Кручинин, В. В., & Kuzovkin, V. (2022) · Computer Tools in Education · doi:10.32603/2071-2340-2022-1-85-96 · Cited by 4 · see citing works
Automatic Question Generation based on Sentence Structure Analysis
Sewunetie, W. T., & Kovács, L. (2023) · International Carpathian Control Conference · doi:10.1109/iccc57093.2023.10178946 · Cited by 4 · see citing works
“According to the survey [6], the main challenges in AQG development are the following issues: question generation from multiple sentences, short and long-type answer assessment, question generation, and assessment using machine learning tools.”
BackgroundDeep Learning and Linguistic Feature Based Automatic Multiple Choice Question Generation from Text
Agarwal, R., Negi, V., Kalra, A., & Mittal, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-94876-4_18 · Cited by 3 · see citing works
Efficient Automatic Answer Evaluation System
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 · see citing works
“One framework generates a score based on the semantic comparability level using the assigned marking pattern for the question [7].”
MethodImproved Evaluator for Subjective Answers Using Natural Language Processing
Sharif, A. Y., & Ravindhar, N. V. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-69982-5_8 · Cited by 3 · see citing works
“Questomatic”: Automated Question Formulation System
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 · see citing works
“In evaluating responses to objective-type questions, recent developments in deep literacy-grounded natural language processing (NLP) show promise [6].”
Exploring the Role of Humanoid Robots in Adaptive Learning Systems: A Case Study on Quizzing
Noh, E. A., Elsonbaty, H. K., Elnakeib, N. A., El-Monayer, G. K., Enan, N. M., Elzeki, O. M., Al‐Zoghby, A. M., & Fouad, K. M. (2024) · International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · doi:10.1109/miucc62295.2024.10783522 · Cited by 3 · see citing works
“[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 · see citing works
Better Qualitative Searching for Effecting the Performance of Machine Translation
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 · see citing works
Shah, A., Devmane, A., Ranka, M., & Churi, P. P. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12045-4 · Cited by 3 · see citing works
“In any learning system, assessment is essential (Das et al., 2021).”
Linguo-Pedagogical Design of Open Question Items in the Context of Digitalization
Alikina, E. V., & Maltsev, D. V. (2024) · Высшее образование в России · doi:10.31992/0869-3617-2024-33-1-128-148 · Cited by 3 · see citing works
Park, G., & Choi, S. (2025) · Journal of Curriculum and Evaluation · doi:10.29221/jce.2025.28.1.215 · Cited by 3 · see citing works
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 · see citing works
“Finally, traditional manual question design is inefficient and struggles to meet the growing demand for high-quality test items in interdisciplinary education [7].”
An AI-based Solution for Enhancing Delivery of Digital Learning for Future TeachersPreprint
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 · see citing works
“In digital learning domain, Authors in [23] proposed an NLP-based method that combined syntax-based and semantic-based methods for AQG.”
InfluentialAdvanced AI-Powered Platform for Personalized Student Learning and Academic Enhancement
Mounika, G., Gaddi, A., Gaddi, P., Varun, K. N., & Vihar, K. (2025) · International Conference on Visual Analytics and Data Visualization (ICVADV) · doi:10.1109/icvadv63329.2025.10961252 · Cited by 2 · see citing works
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 · see citing works
“The survey work [5] pinpointed that “automatic assessments from video content can help learners to learn quickly in a new area.””
InfluentialGenScore: Agent-Based Short-Answer Question Generation and Scoring in Software Engineering Courses
Duong, T. N. B., & Shar, L. K. (2025) · IEEE Access · doi:10.1109/access.2025.3647111 · Cited by 3 · see citing works
“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 · see citing works
Ding, G., Ou, Y., & Li, S. (2026) · Teaching and Teacher Education · doi:10.1016/j.tate.2026.105585 · Cited by 2 · see citing works
Anatomy of a large-scale real-time peer evaluation system
Nyomi, X., & Moccozet, L. (2022) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet56107.2022.10032005 · Cited by 2 · see citing works
Multisource Soft Labeling and Hard Negative Sampling for Retrieval Distractor Ranking
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 · see citing works
“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 · see citing works
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 · see citing works
“Since Text-To-Text Transformers proved to be efficient in question generation [13], this article proposes different Arabic QG implementations on three variants of transformers.”
Utilizing Large Language Models for Developing Automatic Question Generation in Education
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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
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
“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.”
- 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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