Impact of assignment completion assisted by Large Language Model-based chatbot on middle school students’ learning
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
These studies combine individualized AI interactions, timely feedback, and teacher facilitation to raise engagement, satisfaction, and confidence.
“They respond to well-documented problems in traditional instruction-low motivation, limited participation, and scarce affective support-by combining individualized AI interactions, timely feedback, and teacher facilitation to raise engagement, satisfaction, and confidence [109,110].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [109]
In school settings, students repeatedly interact with AI assistants across English writing, reading comprehension, mathematics problem-solving, physics, quantum, and cognitive science, and computer programming.
“In school settings, students repeatedly interact with AI assistants (such as ChatGPT, custom bots, and social robots) across English writing [85], reading comprehension [76,79,80,107,109], mathematics problem-solving [99,105], physics, quantum, and cognitive science [78,91,104,127], and computer programming [97,98,140,141].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [109]
Studies document limited understanding of model training and datasets, which impairs students' ability to detect errors or deceptive content and leads to weak knowledge/analysis or no significant improvement.
“Studies document limited understanding of model training and datasets [112,122,135,136], which impairs students' ability to detect errors or deceptive content [105,133] and leads to weak knowledge/analysis [109] or no significant improvement [98].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [109]
Motivation gains are not guaranteed: several studies find no significant effects or only short-lived novelty bumps that taper over time.
“Motivation gains are not guaranteed: several studies find no significant effects [109] or only short-lived novelty bumps that taper over time [82,122].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [109]
Cites
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 4× · see citing works
“In addition to LLM-based chatbots’ potential advantages for students’ assignments, their application in education has brought significant ethical and practical challenges (Adiguzel et al., 2023; Kasneci et al., 2023; Karthikeyan, 2023).”
InfluentialCognitive Load Theory and Complex Learning: Recent Developments and Future Directions
van Merriënboer, J. J. G., & Sweller, J. (2005) · Educational Psychology Review · doi:10.1007/s10648-005-3951-0 · Cited by 2,206 · see citing works
Phases of inquiry-based learning: Definitions and the inquiry cycle
Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., van Riesen, S. A. N., Kamp, E. T., Manoli, C. C., Zacharia, Z. C., & Tsourlidaki, E. (2015) · Educational Research Review · doi:10.1016/j.edurev.2015.02.003 · Cited by 2,119 · cited 1× · see citing works
“To encourage inquiry-based learning and active learning, many assignments require students to select a question, retrieve information (either online or textbook), integrate this new information with their knowledge learned in class, and organize them to answer the question (Pedaste et al., 2015).”
A Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093 · see citing works
Self-citationA Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.18223 · Cited by 2,090
Self-citationCain, K., Oakhill, J., & Bryant, P. (2004) · Journal of Educational Psychology · doi:10.1037/0022-0663.96.1.31 · Cited by 1,496 · see citing works
Grassini, S. (2023) · Education Sciences · doi:10.3390/educsci13070692 · Cited by 1,048 · cited 1× · see citing works
“Indeed, LLM has been proven to pass the high-school examination with a relatively high score (Grassini, 2023).”
Bloom’s taxonomy of cognitive learning objectives
Adams, N. E. (2015) · Journal of the Medical Library Association : JMLA · doi:10.3163/1536-5050.103.3.010 · Cited by 1,030 · see citing works
Revolutionizing education with AI: Exploring the transformative potential of ChatGPT
Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13152 · Cited by 975 · cited 2× · see citing works
“In addition to LLM-based chatbots’ potential advantages for students’ assignments, their application in education has brought significant ethical and practical challenges (Adiguzel et al., 2023; Kasneci et al., 2023; Karthikeyan, 2023).”
ChatGPT User Experience: Implications for Education
Zhai, X. (2023) · doi:10.2139/ssrn.4312418 · Cited by 935 · cited 2× · see citing works
“…research focuses on evaluating the application of LLM-based chatbots in higher educational contexts, including assessing teachers’ user experience (Zhai, 2022), students’ motivation (Jishnu et al., 2023), instructors’ and students’ perception (Shoufan, 2023), and attitudes (Strzelecki, 2023) of…”
Chatbots applications in education: A systematic review
Okonkwo, C. W., & Ade-Ibijola, A. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100033 · Cited by 879 · see citing works
Perkins, M. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.02.07 · Cited by 839 · see citing works
Guidance for generative AI in education and research
(2023) · doi:10.54675/ewzm9535 · Cited by 808 · see citing works
Strzelecki, A. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2209881 · Cited by 807 · cited 3× · see citing works
“…focuses on evaluating the application of LLM-based chatbots in higher educational contexts, including assessing teachers’ user experience (Zhai, 2022), students’ motivation (Jishnu et al., 2023), instructors’ and students’ perception (Shoufan, 2023), and attitudes (Strzelecki, 2023) of these tools.”
Ruzek, E. A., Hafen, C. A., Allen, J. P., Gregory, A., Mikami, A. Y., & Pianta, R. C. (2016) · Learning and Instruction · doi:10.1016/j.learninstruc.2016.01.004 · Cited by 635 · see citing works
The rise of <scp>ChatGPT</scp> : Exploring its potential in medical education
Lee, H. (2024) · Anatomical Sciences Education · doi:10.1002/ase.2270 · Cited by 596 · cited 3× · see citing works
“Moreover, LLMs can provide multi-round, in-time, and relatively accurate feedback during/on students’ assignments and exami - nations (Fuller et al., 2024; Guo & Lee, 2023; Lee et al., 2024).”
Do <scp>AI</scp> chatbots improve students learning outcomes? Evidence from a meta‐analysis
Wu, R., & Yu, Z. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13334 · Cited by 576 · see citing works
ChatGPT and large language models in academia: opportunities and challenges
Meyer, J. G., Urbanowicz, R. J., Martin, P. C. N., O’Connor, K., Li, R., Peng, P.-C., Bright, T. J., Tatonetti, N., Won, K. J., Gonzalez-Hernandez, G., et al. (2023) · BioData Mining · doi:10.1186/s13040-023-00339-9 · Cited by 569 · cited 2× · see citing works
“They have the potential to revolutionize various facets of human daily life, including education (Meyer et al., 2023; Tamkin et al., 2021; Rillig et al., 2023).”
InfluentialAre We There Yet? - A Systematic Literature Review on Chatbots in Education
Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.654924 · Cited by 557 · cited 1× · see citing works
“However, limited research has investigated the chatbots’ impact in the context of K-12 education (Alemdag, 2023; Wollny et al., 2021).”
The opportunities and challenges of ChatGPT in education
Adeshola, I., & Adepoju, A. P. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2253858 · Cited by 517 · cited 16× · see citing works
“Our study also confirms the high usability of LLM’s natural, human-like interac - tion mode in a K-12 educational context.”
InfluentialExploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey
Shoufan, A. (2023) · IEEE Access · doi:10.1109/access.2023.3268224 · Cited by 499 · cited 1× · see citing works
“…focuses on evaluating the application of LLM-based chatbots in higher educational contexts, including assessing teachers’ user experience (Zhai, 2022), students’ motivation (Jishnu et al., 2023), instructors’ and students’ perception (Shoufan, 2023), and attitudes (Strzelecki, 2023) of these tools.”
Chatbot learning partners: connecting learning experiences, interest and competence
Fryer, L. K., Nakao, K., & Thompson, A. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.023 · Cited by 470 · see citing works
Yu, H. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1181712 · Cited by 409 · cited 3× · see citing works
“It has been worried that outsourcing assignments to LLM-based chatbots may decrease students’ learning motivation and critical thinking skills(Karthikeyan, 2023; Yu, 2023).”
AlAfnan, M. A., Dishari, S., Jovic, M., & Lomidze, K. (2023) · Journal of Artificial Intelligence and Technology · doi:10.37965/jait.2023.0184 · Cited by 402 · see citing works
Response time and display rate in human performance with computers
Shneiderman, B. (1984) · ACM Computing Surveys · doi:10.1145/2514.2517 · Cited by 347 · see citing works
Large Language Models: A Survey
Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., & Gao, J. (2024) · arXiv · doi:10.48550/arxiv.2402.06196 · Cited by 325 · see citing works
Developing Middle School Students' AI Literacy
Lee, I. A., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3408877.3432513 · Cited by 317 · see citing works
Chang, C., Hwang, G., & Gau, M. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13158 · Cited by 294 · see citing works
e-Learning continuance intention: Moderating effects of user e-learning experience
Lin, K. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.09.017 · Cited by 277 · see citing works
Lee, H.-Y., Chen, P.-H., Wang, W.-S., Huang, Y.-M., & Wu, T.-T. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00447-4 · Cited by 273 · cited 1× · see citing works
“Moreover, LLMs can provide multi-round, in-time, and relatively accurate feedback during/on students’ assignments and exami - nations (Fuller et al., 2024; Guo & Lee, 2023; Lee et al., 2024).”
Self-citationRisks and Benefits of Large Language Models for the Environment
Rillig, M. C., Ågerstrand, M., Bi, M., Gould, K. A., & Sauerland, U. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c01106 · Cited by 272 · see citing works
Liu, M., Ren, Y., Nyagoga, L. M., Stonier, F., Wu, Z., & Yu, L. (2023) · Future in Educational Research · doi:10.1002/fer3.10 · Cited by 267 · see citing works
Wu, T., Lee, H., Li, P., Huang, C.-N., & Huang, Y. (2023) · Journal of Educational Computing Research · doi:10.1177/07356331231191125 · Cited by 254 · see citing works
Self-citationLeveraging ChatGPT for Enhancing Critical Thinking Skills
Guo, Y., & Lee, D. (2023) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00505 · Cited by 250 · see citing works
Wentzel, K. R., Russell, S. L., & Baker, S. (2015) · Journal of Educational Psychology · doi:10.1037/edu0000049 · Cited by 247 · see citing works
An analysis of children’ interaction with an AI chatbot and its impact on their interest in reading
Liu, C.-C., Liao, M.-G., Chang, C.-H., & Lin, H.-M. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104576 · Cited by 240 · see citing works
Generative AI and ChatGPT in School Children’s Education: Evidence from a School Lesson
Jauhiainen, J. S., & Guerra, A. G. (2023) · Sustainability · doi:10.3390/su151814025 · Cited by 234 · see citing works
Impact of reducing intrinsic cognitive load on learning in a mathematical domain
Ayres, P. (2006) · Applied Cognitive Psychology · doi:10.1002/acp.1245 · Cited by 212 · see citing works
The impact of educational chatbot on student learning experience
Ait Baha, T., El Hajji, M., Es-Saady, Y., & Fadili, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12166-w · Cited by 206 · see citing works
Cheung, B. H. H., Lau, G. K. K., Wong, G. T. C., Lee, E. Y. P., Kulkarni, D., Seow, C. S., Wong, R., & Co, M. T.-H. (2023) · PLOS ONE · doi:10.1371/journal.pone.0290691 · Cited by 195 · see citing works
Effects of chatbot-assisted in-class debates on students’ argumentation skills and task motivation
Guo, K., Zhong, Y., Li, D., & Chu, S. K. W. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104862 · Cited by 188 · see citing works
Retrieval practice & Bloom’s taxonomy: Do students need fact knowledge before higher order learning?
Agarwal, P. K. (2018) · Journal of Educational Psychology · doi:10.1037/edu0000282 · Cited by 178 · see citing works
Large Language Models in Education: Vision and Opportunities
Gan, W., Qi, Z., Wu, J., & Lin, J. C. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386291 · Cited by 175 · see citing works
Xiao, C., Xu, S. X., Zhang, K., Wang, Y., & Xia, L. (2023) · Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) · doi:10.18653/v1/2023.bea-1.52 · Cited by 138 · see citing works
ChatGPT's capabilities in providing feedback on undergraduate students’ argumentation: A case study
Wang, L., Chen, X., Wang, C., Xu, L., Shadiev, R., & Li, Y. (2023) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2023.101440 · Cited by 135 · see citing works
Self-citationA review and critique of context-based physics instruction and assessment
Taasoobshirazi, G., & Carr, M. (2008) · Educational Research Review · doi:10.1016/j.edurev.2008.01.002 · Cited by 116 · see citing works
Can ChatGPT Pass High School Exams on English Language Comprehension?
de Winter, J. C. F. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00372-z · Cited by 115 · see citing works
A Chatbot-Based Coaching Intervention for Adolescents to Promote Life Skills: Pilot Study
Gabrielli, S., Rizzi, S., Carbone, S., & Donisi, V. (2020) · JMIR Human Factors · doi:10.2196/16762 · Cited by 105 · see citing works
Ruan, S., Willis, A., Xu, Q., Davis, G. M., Jiang, L., Brunskill, E., & Landay, J. A. (2019) · Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale · doi:10.1145/3330430.3333643 · Cited by 81 · see citing works
Autonomous learning: A teacher-less learning!
Masouleh, N. S., & Jooneghani, R. B. (2012) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2012.09.570 · Cited by 79 · see citing works
The effect of chatbots on learning: a meta-analysis of empirical research
Alemdağ, E. (2023) · Journal of Research on Technology in Education · doi:10.1080/15391523.2023.2255698 · Cited by 78 · see citing works
Teachers’ Roles in Autonomous Learning
Yan, S. (2012) · Journal of Sociological Research · doi:10.5296/jsr.v3i2.2860 · Cited by 66 · see citing works
Playfoot, D. R., Quigley, M., & Thomas, A. G. (2024) · The Internet and Higher Education · doi:10.1016/j.iheduc.2024.100950 · Cited by 65 · see citing works
Waltzer, T., Cox, R. L., & Heyman, G. D. (2023) · Human Behavior and Emerging Technologies · doi:10.1155/2023/1923981 · Cited by 61 · see citing works
Homework assignments to enhance student engagement in secondary education
Buijs, M., & Admiraal, W. (2012) · European Journal of Psychology of Education · doi:10.1007/s10212-012-0139-0 · Cited by 60 · see citing works
Malik, A., Khan, M. L., & Hussain, K. (2023) · doi:10.2139/ssrn.4413516 · Cited by 59 · see citing works
Learning to Prompt in the Classroom to Understand AI Limits: A Pilot Study
Theophilou, E., Koyutürk, C., Yavari, M., Bursic, S., Donabauer, G., Telari, A., Testa, A., Boiano, R., Hernandez-Leo, D., Ruskov, M., et al. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-47546-7_33 · Cited by 58 · see citing works
Literature Review on Pros and Cons of ChatGPT Implications in Education
Karthikeyan, C. (2023) · International Journal of Science and Research (IJSR) · doi:10.21275/sr23219122412 · Cited by 56 · see citing works
Exploring the use of ChatGPT to analyze student course evaluation comments
Fuller, K. A., Morbitzer, K. A., Zeeman, J. M., Persky, A., Savage, A., & McLaughlin, J. E. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05316-2 · Cited by 54 · see citing works
Exploring the Trend and Potential Distribution of Chatbot in Education: A Systematic Review
Chee, K. N., Rahman, M. H. A., Yahaya, N., Ibrahim, N. H., Razak, R. A., & Sugino, C. (2023) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2023.13.3.1834 · Cited by 48 · see citing works
Ekambaranathan, A., Zhao, J., & Kleek, M. G. V. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445599 · Cited by 47 · see citing works
Hallucinations in ChatGPT: An Unreliable Tool for Learning
Ahmad, Z., Kaiser, W., & Rahim, S. (2023) · Rupkatha Journal on Interdisciplinary Studies in Humanities · doi:10.21659/rupkatha.v15n4.17 · Cited by 39 · see citing works
Using ChatGPT and Other Large Language Model (LLM) Applications for Academic Paper Assignments
Jungherr, A. (2023) · doi:10.31235/osf.io/d84q6 · Cited by 37 · see citing works
Generative AI (gAI) in medical education: Chat-GPT and co
Moritz, S., Romeike, B., Stosch, C., & Tolks, D. (2023) · German Medical Science GMS Publishing House · doi:10.3205/zma001636 · Cited by 37 · see citing works
Al-Hafdi, F. S., & AlNajdi, S. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12671-6 · Cited by 37 · see citing works
van Duijn, M., van Dijk, B., Kouwenhoven, T., de Valk, W., Spruit, M., & vanderPutten, P. (2023) · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL) · doi:10.18653/v1/2023.conll-1.25 · Cited by 24 · see citing works
UNVEILING STUDENT MOTIVATIONS: A STUDY OF CHATGPT USAGE IN EDUCATION
Dineshan, J., Srinivasan, M., Dhanunjay, G. S., & Shamala, R. (2023) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v4.i2.2023.503 · Cited by 21 · see citing works
Authority and Power in Social Interaction
Bencherki, N., Matte, F., & Cooren, F. (2019) · Routledge eBooks · doi:10.4324/9781351051668 · Cited by 18 · see citing works
The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology
You, Y., Kou, Y., Ding, X., & Gui, X. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445657 · Cited by 18 · see citing works
Prediction of Learner Information-Seeking Behavior and Classroom Engagement in the Advent of ChatGPT
Adarkwah, M. A., Ying, C., Mustafa, M. Y., & Huang, R. (2023) · Lecture notes in educational technology · doi:10.1007/978-981-99-5961-7_13 · Cited by 16 · see citing works
Chi, S., Wang, Z., & Liu, X. (2022) · Research in Science Education · doi:10.1007/s11165-022-10056-8 · Cited by 13 · see citing works
Berger, E., Sæthre, T. H., & Divitini, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33759-9_23 · Cited by 13 · see citing works
Compulsory Education Policy in China
Li, J., & Xue, E. (2021) · Exploring Education Policy in a Globalized World · doi:10.1007/978-981-33-6358-8 · Cited by 12 · see citing works
Kumar, N. A., & Lan, A. (2024) · arXiv · Cited by 12 · see citing works
Henthorn, R., Lowden, K., & McArdle, K. (2022) · Educational Action Research · doi:10.1080/09650792.2022.2106260 · Cited by 11 · see citing works
Sovrano, F., Ashley, K., & Bacchelli, A. (2023) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-257180 · Cited by 11 · see citing works
A Survey on Large Language Model Hallucination via a Creativity PerspectivePreprint
Jiang, X., Tian, Y., Hua, F., Xu, C., Wang, Y., & Guo, J. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.06647 · Cited by 11 · see citing works
Wei, T. (2022) · Science Insights Education Frontiers · doi:10.15354/sief.22.re065 · Cited by 9 · see citing works
Integrating theory and practice by self-directed inquiry-based learning? A pilot study
Hammarlund, C. S., Nordmark, E., & Gummesson, C. (2013) · European Journal of Physiotherapy · doi:10.3109/21679169.2013.836565 · Cited by 8 · see citing works
Park, S., Subramonyam, H., & Kulkarni, C. E. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.06024 · Cited by 8 · see citing works
Grogan, K. A. (2017) · International Review of Economics Education · doi:10.1016/j.iree.2017.06.001 · Cited by 7 · see citing works
Research on motivation and behavior of ChatGPT use in middle school students
Li, Y. (2023) · International Journal of New Developments in Education · doi:10.25236/ijnde.2023.051911 · Cited by 6 · see citing works
Self-citationZha, S., Qiao, Y., Hu, Q., Li, Z., Gong, J., & Xu, Y. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2403.16159 · Cited by 6 · see citing works
Training of trainers: An action-based research for improving the pedagogical skills of academicians
Özer, B., Duran, V., & Tekke, M. (2020) · International Journal of Evaluation and Research in Education (IJERE) · doi:10.11591/ijere.v9i3.20327 · Cited by 6 · see citing works
Hübscher-Younger, T., & Narayanan, N. H. (2003) · Springer eBooks · doi:10.1007/978-94-017-0195-2_55 · Cited by 6 · see citing works
BİLGİÇ, K., & Doğusoy, B. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11583-1 · Cited by 5 · see citing works
OpineBot: Class Feedback Reimagined Using a Conversational LLMPreprint
Tanwar, H., Shrivastva, K., Singh, R., & Kumar, D. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.15589 · Cited by 4 · see citing works
Functorial Language ModelsPreprint
Toumi, A., & Koziell-Pipe, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14411 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Liu, X., & Zhong, B. (2025) · Educational Research Review · doi:10.1016/j.edurev.2025.100741 · Cited by 78 · see citing works
Zhu, Y., Liu, Q., & Zhao, L. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13420-z · Cited by 92 · see citing works
Self-citationStrzelecki, A. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13222-9 · Cited by 41 · see citing works
Tao, S., Lan, M., Wang, M., & Li, H. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100561 · Cited by 31 · see citing works
Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22 · see citing works
Caffaratti, L. B., Longobardi, C., Badenes‐Ribera, L., & Marengo, D. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1614993 · Cited by 21 · see citing works
Gong, Y., Wang, M., He, L., Xu, C., & Yu, Y. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251396354 · Cited by 12 · see citing works
Benchmarking Large Language Models on Homework Assessment in Circuit Analysis
Chen, L., Qin, Z., Guo, Y., Rohde, J. A., & Zhang, Y. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00501-w · Cited by 9 · see citing works
Jiang, S., Numtong, K., & Asavaratana, W. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1656204 · Cited by 10 · see citing works
Nageshwaran, V., & Nageshwaran, S. (2025) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl65848.2025.10933129 · Cited by 6 · see citing works
Boolzen, C., Kühn, J., Flegr, S., Rott, E.-M., Stausberg, N., & Küchemann, S. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11665-9 · Cited by 9 · see citing works
A Systematic Literature Review of Generative AI Risks and Harms for Youth
Chang, T., & Razi, A. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798673 · Cited by 5 · see citing works
Zhu, Y., & Li, Y. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103654 · Cited by 5 · see citing works
Self-citationHuman or AI? Comparing Design Thinking Assessments by Teaching Assistants and Bots
Khan, S., Liow, W. T., & Ang, L. K. (2025) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale66047.2025.11346622 · Cited by 2 · see citing works
Choi, W.-Y. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13945-x · Cited by 3 · see citing works
Song, Z., Lai, C., Huang, X., & Lin, C. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105654 · Cited by 3 · see citing works
Liao, C.-H., Wu, P., Shih, P.-Y., & Tsao, C.-J. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2527165 · Cited by 1 · see citing works
Cognitive Effort Analysis in Digital Learning Environments
Sharmin, S. (2025) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3716553.3750820 · Cited by 1 · see citing works
Student chatbot use and perceptions in a course assignment: Comparing two geomatics courses
Hochmair, H. H. (2025) · Geomatica · doi:10.1016/j.geomat.2025.100079 · Cited by 1 · see citing works
Afonso, M., Gonçalves, R., Santos, J., & Martins, C. L. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-00701-8_7 · Cited by 1 · see citing works
AI and student motivation: A scoping review
Li, J., King, R. B., Chai, C. S., Lee, V. W. Y., & Li, R. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2026.2672515 · Cited by 1 · see citing works
Generative AI: A New Frontier for Classroom Emotion Management in Basic Education English Teaching
<p>Fan Jiqun, Z. Q. (2025) · Frontiers in Educational Research · doi:10.25236/fer.2025.080306 · Cited by 1 · see citing works
Suryavanshi, C. A., & Nayak, K. R. (2025) · AJP Advances in Physiology Education · doi:10.1152/advan.00087.2025 · Cited by 1 · see citing works
Students’ perspectives on the application of Artificial Intelligence (AI) in completing assignments.
Luu, T. T., & Nguyen, L. T. (2025) · International STEM Education Conference (iSTEM-Ed) · doi:10.1109/istem-ed65612.2025.11129375
Machines flunking an exam: Evaluating large language models on course-related open questionsPreprint
Huang, J., Wei, Y., Zhang, L., Lai, R., Lai, F., & Zheng, Y. (2025) · Research Square · doi:10.21203/rs.3.rs-6679662/v1
Marchella, A. M., Hardiansyah, N., Candra, N., Sutoyo, R., Ubaidah, U., Alexander, D., & Chowanda, A. (2025) · doi:10.1109/icoict66265.2025.11192932
Acuña, B. P. (2026) · EDU REVIEW International Education and Learning Review / Revista Internacional de Educación y Aprendizaje · doi:10.62701/revedu.v13.5520
Using chatbot in education – A systematic review and research agenda
Wut, T. M., Low, M. P., & Tam, K.-W. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102956
A scoping review of artificial intelligence literacy in secondary education
Ghosh, R., Gardia, A., & Singha, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01408-8 · Cited by 1 · see citing works
Anonymous, Chen, X., Xiong, Y., Xu, S., Li, Q., & Zhou, S. (2026) · Physical Review Physics Education Research · doi:10.1103/9t5b-twsb
The Impact of AI-Assisted Learning Tools on Students’ Academic Writing Performance
Ishtiaq, M., Naseem, A., & Akbar, G. (2026) · The regional tribune. · doi:10.55737/trt/v-vii.336
Sun, Y., Keung, J., Yu, H. K., Liao, Y., Mao, Z., Li, Y., & Ma, X. (2026) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac69091.2026.00199
Self-citationKhodjaev, B. K., Masalimova, A. R., Chistyakov, A. A., & Ay, Y. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1971476
- Authors
- Zhu, Y., Zhu, C., Wu, T., Wang, S., Zhou, Y., Chen, J., Wu, F., & Li, Y.
- Affiliations
- Zhejiang University (CN)
- Corresponding author
- Li, Yan
- Venue
- Education and Information Technologies
- Published
- 2024-07-19
- Volume
- 30
- Issue
- 2
- Pages
- 2429-2461
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4400832623
- Cited by
- 33
- References
- 87
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 1
- FWCI
- 12.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Education and Information Technologies (publishedVersion)
- Funders
- National Key R&D Program of China (2022ZD0115904)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- large language model chatbots, assignment completion, junior high school students, student motivation, learning outcomes, learning satisfaction, learning experience, learning design, teacher reflection, self-directed learning, K-12 education, educational technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
