Unlocking Scientific Concepts: How Effective Are LLM-Generated Analogies for Student Understanding and Classroom Practice?
Open accessAbstract
Teaching scientific concepts is essential but challenging, and analogies help students connect new concepts to familiar ideas. Advancements in large language models (LLMs) enable generating analogies, yet their effectiveness in education remains underexplored. In this paper, we first conducted a two-stage study involving high school students and teachers to assess the effectiveness of LLM-generated analogies in biology and physics through a controlled in-class test and a classroom field study. Test results suggested that LLM-generated analogies could enhance student understanding particularly in biology, but require teachers' guidance to prevent over-reliance and overconfidence. Classroom experiments suggested that teachers could refine LLM-generated analogies to their satisfaction and inspire new analogies from generated ones, encouraged by positive classroom feedback and homework performance boosts. Based on findings, we developed and evaluated a practical system to help teachers generate and refine teaching analogies. We discussed future directions for developing and evaluating LLM-supported teaching and learning by analogy.
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?
GenAI is employed to support student engagement with challenging topics such as quantum theory, interdisciplinary knowledge transfer, and experimental design and hypothesis formulation.
“GenAI is employed to address these goals by supporting student engagement with challenging topics such as quantum theory, interdisciplinary knowledge transfer, and experimental design and hypothesis formulation-areas often limited by insufficient teacher resources and the lack of personalized instruction [91-94].”
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 [94]
As a learning partner, AI provides high-quality concept maps, instructional analogies, and personalized feedback, creating deep learning experiences and prompting reflection on socio-ethical implications of technology, thus seeding epistemic insight.
“As a learning partner, AI provides high-quality concept maps [146], instructional analogies [94], and personalized feedback [128,130], creating deep learning experiences [95] and prompting reflection on socio-ethical implications of technology-thus seeding epistemic insight [117,132,135,138,151].”
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 [94]
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,037 · see citing works
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 2× · see citing works
“During the interviews, we asked each teacher the following questions: course coverage (Q1), system usability (Q2), satisfaction with the generated analogies (Q3), satisfaction with the generated principles (Q4), satisfaction with the edited analogies (Q5), satisfaction with the edited principles…”
Schema induction and analogical transfer
Gick, M. L., & Holyoak, K. J. (1983) · Cognitive Psychology · doi:10.1016/0010-0285(83)90002-6 · Cited by 2,955 · cited 2× · see citing works
“Many studies [5, 25, 26, 72] have investigated analogical problem solving, where students of various ages solve unfamiliar problems using well-designed analogies.”
Gick, M. L., & Holyoak, K. J. (1980) · Cognitive Psychology · doi:10.1016/0010-0285(80)90013-4 · Cited by 2,552 · cited 4× · see citing works
“First, these two settings align with those used to evaluate human-made analogies in traditional education research: student-only testing [25, 72] and teacher-led classroom practice [53, 76].”
InfluentialStructure mapping in analogy and similarity.
Gentner, D., & Markman, A. B. (1997) · American Psychologist · doi:10.1037/0003-066x.52.1.45 · Cited by 1,825 · see citing works
Mental leaps: analogy in creative thought
(1995) · Choice Reviews Online · doi:10.5860/choice.32-6192 · Cited by 1,607 · see citing works
Practical and ethical challenges of large language models in education: A systematic scoping review
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13370 · Cited by 935 · see citing works
On the role of analogies and metaphors in learning science
Duit, R. (1991) · Science Education · doi:10.1002/sce.3730750606 · Cited by 921 · see citing works
Structure-mapping: A theoretical framework for analogy
GENTNER, D. (1983) · Cognitive Science · doi:10.1016/s0364-0213(83)80009-3 · Cited by 816 · see citing works
Vanderwende, L., Daumé, H., & Kirchhoff, K. (2013) · North American Chapter of the Association for Computational Linguistics · Cited by 685 · see citing works
Design, analogy, and creativity
Gael, A. K. (1997) · IEEE Expert · doi:10.1109/64.590078 · Cited by 355 · see citing works
Cognitive Supports for Analogies in the Mathematics Classroom
Richland, L. E., Zur, O., & Holyoak, K. J. (2007) · Science · doi:10.1126/science.1142103 · Cited by 286 · see citing works
Kazemitabaar, M., Ye, R., Wang, X.-H., Henley, A. Z., Denny, P. C., Craig, M., & Grossman, T. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642773 · Cited by 280 · see citing works
Proceedings of the Aristotelian Society, 1932-1933.
C., H. T. (1934) · The Journal of Philosophy · doi:10.2307/2015969 · Cited by 222 · see citing works
Computational models of analogy
Gentner, D., & Forbus, K. D. (2010) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.105 · Cited by 219 · see citing works
Analogy, higher order thinking, and education
Richland, L. E., & Simms, N. K. (2015) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1336 · Cited by 219 · see citing works
Can Metaphors and Analogies Improve Communication with Seriously Ill Patients?
Casarett, D., Pickard, A., Fishman, J., Alexander, S. C., Arnold, R. M., Pollak, K. I., & Tulsky, J. A. (2009) · Journal of Palliative Medicine · doi:10.1089/jpm.2009.0221 · Cited by 217 · see citing works
Gladkova, A., Валентинович, Д. А., & Matsuoka, S. (2016) · doi:10.18653/v1/n16-2002 · Cited by 198 · see citing works
Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions
Alkhatib, A., & Bernstein, M. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300760 · Cited by 188 · see citing works
Analogy Use in Eighth-Grade Mathematics Classrooms
Richland, L. E., Holyoak, K. J., & Stigler, J. W. (2004) · Cognition and Instruction · doi:10.1207/s1532690xci2201_2 · Cited by 173 · see citing works
Gentner, D., & Hoyos, C. (2017) · Topics in Cognitive Science · doi:10.1111/tops.12278 · Cited by 170 · see citing works
Analogy, explanation, and education
Thagard, P. R. (1992) · Journal of Research in Science Teaching · doi:10.1002/tea.3660290603 · Cited by 151 · see citing works
Analogical Reasoning in the Classroom: Insights From Cognitive Science
Vendetti, M. S., Matlen, B. J., Richland, L. E., & Bunge, S. A. (2015) · Mind Brain and Education · doi:10.1111/mbe.12080 · Cited by 149 · see citing works
Science teachers’ use of analogies: observations from classroom practice
Treagust, D. F., Duit, R. H., Joslin, P., & Lindauer, I. E. (1992) · International Journal of Science Education · doi:10.1080/0950069920140404 · Cited by 149 · see citing works
Lyu, W., Wang, Y., Chung, T. (., Sun, Y., & Zhang, Y. (2024) · Proceedings of the Eleventh ACM Conference on Learning @ Scale · doi:10.1145/3657604.3662036 · Cited by 131 · see citing works
Abstraction and analogy‐making in artificial intelligence
Mitchell, M. (2021) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14619 · Cited by 125 · see citing works
Teaching by Analogy: From Theory to Practice
Gray, M. E., & Holyoak, K. J. (2021) · Mind Brain and Education · doi:10.1111/mbe.12288 · Cited by 96 · see citing works
Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers
Zhu, Q., Zhang, X., & Luo, J. (2023) · Journal of Mechanical Design · doi:10.1115/1.4056598 · Cited by 89 · see citing works
Analogical transfer in young children: Analogies as tools for communication and exposition
Brown, A. L., Kane, M. J., & Long, C. (1989) · Applied Cognitive Psychology · doi:10.1002/acp.2350030402 · Cited by 84 · see citing works
Chan, J., Chang, J. C., Hope, T., Shahaf, D., & Kittur, A. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274300 · Cited by 75 · see citing works
Using Analogies to Communicate Information about Health Risks
Galešić, M., & García‐Retamero, R. (2012) · Applied Cognitive Psychology · doi:10.1002/acp.2866 · Cited by 70 · see citing works
ReelFramer: Human-AI Co-Creation for News-to-Video Translation
Wang, S., Menon, S., Long, T., Henderson, K., Li, D., Crowston, K., Hansen, M., Nickerson, J. V., & Chilton, L. B. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642868 · Cited by 70 · see citing works
Supporting Sensemaking of Large Language Model Outputs at Scale
Gero, K. I., Swoopes, C., Gu, Z., Kummerfeld, J. K., & Glassman, E. L. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642139 · Cited by 60 · see citing works
Distributed analogical idea generation: inventing with crowds
Yu, L., Kittur, A., & Kraut, R. E. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557371 · Cited by 60 · see citing works
Combining independent modules in lexical multiple-choice problems
Turney, P. D., Littman, M. L., Bigham, J. P., & Shnayder, V. (2004) · Amsterdam studies in the theory and history of linguistic science. Series 4, Current issues in linguistic theory · doi:10.1075/cilt.260.11tur · Cited by 47 · see citing works
Kao, C.-Y. (2020) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2020.100653 · Cited by 45 · see citing works
Augmenting Scientific Creativity with an Analogical Search Engine
Kang, H. B., Qian, X., Hope, T., Shahaf, D., Chan, J., & Kittur, A. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3530013 · Cited by 43 · see citing works
Lu, X., Fan, S., Houghton, J., Wang, L., & Wang, X. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580957 · Cited by 42 · see citing works
Searching for analogical ideas with crowds
Yu, L., Kittur, A., & Kraut, R. E. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557378 · Cited by 41 · see citing works
Tan, M., & Subramonyam, H. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642592 · Cited by 40 · see citing works
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
Bhavya, B., Xiong, J., & Zhai, C. (2022) · Proceedings of the 15th International Conference on Natural Language Generation · doi:10.18653/v1/2022.inlg-main.25 · Cited by 40 · see citing works
Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., & Santos, O. C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-36336-8 · Cited by 39 · see citing works
Analogy Mining for Specific Design Needs
Gilon, K., Chan, J., Ng, F., Lifshitz‐Assaf, H., Kittur, A., & Shahaf, D. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173695 · Cited by 39 · see citing works
Fan, H., Chen, G., Wang, X., & Peng, Z. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676390 · Cited by 38 · see citing works
Ding, Z., Srinivasan, A., Macneil, S., & Chan, J. (2023) · Creativity and Cognition · doi:10.1145/3591196.3593516 · Cited by 35 · see citing works
Bureaucracy as a Lens for Analyzing and Designing Algorithmic Systems
Pääkkönen, J., Nelimarkka, M., Haapoja, J., & Lampinen, A. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376780 · Cited by 35 · see citing works
Teaching Models in the Use of Analogies as a Resource in the Science Classroom
Martínez, J. M. O., Goded, M. D. P. A., & Navarrete, A. M. L. (2006) · International Journal of Science Education · doi:10.1080/09500690600708444 · Cited by 35 · see citing works
Yeo, S., Lim, G., Gao, J., Zhang, W., & Perrault, S. T. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642530 · Cited by 35 · see citing works
Generating Personalized Spatial Analogies for Distances and Areas
Kim, Y., Hullman, J. R., & Agrawala, M. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858440 · Cited by 33 · see citing works
Gao, L., Lu, J., Shao, Z., Lin, Z., Yue, S., Ieong, C., Sun, Y., Zauner, R. J., Wei, Z., & Chen, S. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456145 · Cited by 33 · see citing works
Self-citationMitigating reasoning hallucination through Multi-agent Collaborative Filtering
Shi, J., Zhao, J., Wu, X., Xu, R., Jiang, Y.-H., & He, L. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125723 · Cited by 30 · see citing works
Improving Comprehension of Measurements Using Concrete Re-expression Strategies
Hullman, J. R., Kim, Y., Nguyen, F., Speers, L., & Agrawala, M. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173608 · Cited by 30 · see citing works
Chen, L., Jiang, Z., Xia, D., Cai, Z., Sun, L., Childs, P., & Zuo, H. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642887 · Cited by 30 · see citing works
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning
Chen, J., Xu, R., Fu, Z., Shi, W., Li, Z., Zhang, X., Sun, C., Li, L., Xiao, Y., & Zhou, H. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.311 · Cited by 25 · see citing works
Solving and Explaining Analogy Questions Using Semantic Networks
Boteanu, A., & Chernova, S. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9400 · Cited by 24 · see citing works
Distilling Script Knowledge from Large Language Models for Constrained Language Planning
Yuan, S., Chen, J., Fu, Z., Ge, X., Shah, S., Jankowski, C., Xiao, Y., & Yang, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.236 · Cited by 23 · see citing works
Self-citationChen, Q., Shuai, W., Zhang, J., Sun, Z., & Cao, N. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642480 · Cited by 20 · see citing works
Ngoon, T. J., Sushil, S., Stewart, A., Lee, U.-S. A., Venkatraman, S., Thawani, N., Mitra, P., Clarke, S. N., Zimmerman, J., & Ogan, A. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642487 · Cited by 20 · see citing works
Jiayang, C., Qiu, L., Chan, T., Fang, T., Wang, W., Chan, C., Ru, D., Guo, Q., Zhang, H., Song, Y., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.706 · Cited by 18 · see citing works
Riederer, C., Hofman, J. M., & Goldstein, D. G. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174122 · Cited by 18 · see citing works
VizGroup: An AI-assisted Event-driven System for Collaborative Programming Learning Analytics
Tang, X., Wong, S., Pu, K., Chen, X., Yang, Y., & Chen, Y. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676347 · Cited by 17 · see citing works
Putting scientific results in perspective: Improving the communication of standardized effect sizes
Kim, Y., Hofman, J. M., & Goldstein, D. G. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502053 · Cited by 16 · see citing works
In-Context Analogical Reasoning with Pre-Trained Language Models
Hu, X., Storks, S., Lewis, R. L., & Chai, J. Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.109 · Cited by 15 · see citing works
BioSpark: An End-to-End Generative System for Biological-Analogical Inspirations and Ideation
Kang, H. B., Lin, D. C.-E., Martelaro, N., Kittur, A., Chen, Y.-Y., & Hong, M. K. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3651035 · Cited by 15 · see citing works
Sultan, O., & Shahaf, D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.232 · Cited by 12 · see citing works
CFlow: Supporting Semantic Flow Analysis of Students' Code in Programming Problems at Scale
Zhang, G., Tang, X., Oney, S., & Chen, Y. (2024) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3657604.3662025 · Cited by 12 · see citing works
ParallelPARC: A Scalable Pipeline for Generating Natural-Language Analogies
Sultan, O., Bitton, Y., Yosef, R., & Shahaf, D. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.329 · Cited by 11 · see citing works
ANALOGICAL - A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models
Wijesiriwardene, T., Wickramarachchi, R., Gajera, B., Gowaikar, S., Gupta, C., Chadha, A. R., Reganti, A., Sheth, A., & Das, A. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.218 · Cited by 9 · see citing works
Yuan, S., Chen, J., Ge, X., Xiao, Y., & Yang, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.160 · Cited by 8 · see citing works
Self-citationEdgeworth: Efficient and Scalable Authoring of Visual Thinking Activities
Ni, W., Estep, S., Harriman, H.-S., Koedinger, K. R., & Sunshine, J. (2024) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3657604.3662034 · Cited by 2 · see citing works
Stickipedia: A Search Engine and Repository for Explanatory Analogies
Kumar, V., Bhat, S., & Pedanekar, N. (2015) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2015.106 · Cited by 1 · see citing works
Capturing Cancer as Music: Cancer Mechanisms Expressed through Musification
Hnatyshyn, R., Hong, J., Maciejewski, R., Norby, C., & Maley, C. C. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642153 · Cited by 1 · see citing works
Using Open Data to Automatically Generate Localized Analogies
Spatharioti, S. E., Goldstein, D. G., & Hofman, J. M. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642638 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22 · see citing works
An LLM-Enhanced Multi-agent Architecture for Conversation-Based Assessment
Hou, X., Forsyth, C. M., Andrews‐Todd, J., Rice, J., Cai, Z., Jiang, Y., Zapata‐Rivera, D., & Graesser, A. C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98417-4_9 · Cited by 11 · see citing works
General-Purpose Models for the Chemical Sciences: LLMs and Beyond
Alampara, N., Aneesh, A., Ríos-García, M., Mirza, A., Schilling-Wilhelmi, M., Aghajani, A. A., Sun, M., Prastalo, G., & Jablonka, K. M. (2026) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00583 · Cited by 12 · see citing works
AI-assisted assessment of higher education quality: A visual analytical approach
He, C. Y., Huang, Y., Lan, H., He, J., Fan, X., Zhang, D., Lin, J., Liao, M., & Wang, C. (2026) · Visual Informatics · doi:10.1016/j.visinf.2026.100306 · Cited by 4 · see citing works
Wang, Y., Yu, J., Zhang-Li, D., Lim, J. J. Y., Tu, S., Li, H., Liu, Z., Liu, H., Hou, L., Li, J., et al. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761578 · Cited by 1 · see citing works
Diningrat, M. S. M., Mabruroh, F., Widya, H., & Nurhamidah, N. (2025) · JURNAL Pendidikan dan Ilmu Fisika · doi:10.52434/jpif.v5i1.42586
Liu, L., Tan, W., Zhao, Y., Pang, J., & Wong, D. F. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799384
Zheng, Z., Yu, C., Chen, W., Song, M., Liu, B., Liu, J., Wang, S., Wang, X. Q., Cai, J., & Shi, Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790318
Xu, S., & Zhang, X. X. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790346
MediMate: Co-Crafting Patient-Centered Medical Explanations Using LLMs as a Rehearsal Partner
Jin, Y., Zhang, S., Chen, D., Ouyang, Y., Shao, Y., Jiang, C., Li, H., & Li, Q. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812891
ANVIL: Analogies and Videos for Lecturers
Noviello, Y., Birillo, A., & Migut, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29744-0_23
Supporting systems thinking in environmental science education with large language model chatbots
Nguyen, H., Valdez, E., Pei, J., Ludovise, S., & Santagata, R. (2026) · International Journal of Science Education · doi:10.1080/09500693.2026.2695951
How college students use AI to navigate course readings: evidence from an 8-week study
Fu, Y., Wester, J., van Berkel, N., & Hiniker, A. (2026) · Human-Computer Interaction · doi:10.1080/07370024.2026.2713505
- Authors
- Shao, Z., Yuan, S., Gao, L., He, Y., Yang, D., & Chen, S.
- Affiliations
- Fudan University (CN)
- Venue
- Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2025-04-25
- Pages
- 1-19
- OpenAlex
- W4409747868
- Cited by
- 13
- References
- 74
- Citations by year
- 2026: 9 · 2025: 4
- FWCI
- 14.87
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Natural Science Foundation of China (No.62472099, No.62202105)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Science Education and Pedagogy
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- large language models, analogical reasoning, science education, student understanding, teaching practice, conceptual understanding
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
