Asking generative artificial intelligence the right questions improves writing performance
Open accessAbstract
Generative Artificial Intelligence (GenAI) tools are widely used by learners and this trend is poised to continue. However, little is known about whether and how GenAI use impacts learning and performance. This study aimed to investigate the effect of GenAI on performance by examining a key affordance of GenAI—seeking help via question asking. We compared the questions that learners asked GenAI versus a human tutor online during a writing task. Using quantitative ethnographic methods, we found that: (a) participants in the GenAI condition asked significantly more questions compared to those in the Tutor condition; (b) GenAI participants tended to ask one-off questions, while Tutor participants tended to have longer conversational exchanges; (c) GenAI participants tended to question pragmatically, asking direct questions about conceptual and procedural knowledge, while Tutor participants tended to make indirect request for feedback; (d) question asking, as measured by epistemic network analysis, mediated the relationship between experimental condition and performance—the more pragmatic the questions, and thus the more like questions in the GenAI condition, the better the performance; and (e) questions in the GenAI condition were driven by social coordination and knowledge deficits, while questions in the Tutor condition were driven by social coordination and establishing common ground. These findings suggest learners may be less hesitant to admit knowledge deficits and more wi…
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?
The quality of GenAI output has been shown to be enhanced through an iterative interaction with the user.
“In terms of professional output, GenAI has been found to produce good quality academic texts but can also produce ‘hallucinations’ of entirely fictitious ideas pre sented as facts (Babl & Babl, 2023 who asked the GenAI to write aca demic abstracts), although the quality of the output has been shown to be enhanced through an iterative interaction with the user (Yang et al., 2025, Cheng et al., 2”
From If ChatGPT can do it, where is my creativity? generative AI boosts performance but diminishes experience in creative writing (2025) · cites this paper as Cheng et al., 2025
Cites
Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.51.6.1173 · Cited by 91,542 · see citing works
Preacher, K. J., & Hayes, A. F. (2008) · Behavior Research Methods · doi:10.3758/brm.40.3.879 · Cited by 34,117 · see citing works
A guideline of selecting and reporting intraclass correlation coefficients for reliability research
Koo, T. K., & Li, M. Y. (2016) · Journal of Chiropractic Medicine · doi:10.1016/j.jcm.2016.02.012 · Cited by 29,639 · see citing works
Reconsidering Baron and Kenny: myths and truths about mediation analysis
Zhao, X., Lynch, J. G., & Chen, Q. (2010) · Journal of Consumer Research · doi:10.1086/651257 · Cited by 12,583 · see citing works
Principal component analysis: A review and recent developments
Jolliffe, I. T., & Cadima, J. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2015.0202 · Cited by 10,117 · 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 · see citing works
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
(1956) · Medical Entomology and Zoology · Cited by 4,596 · see citing works
mediation : R Package for Causal Mediation Analysis
Tingley, D. H., Yamamoto, T., Hirose, K., Keele, L. J., & Imai, K. (2014) · Journal of Statistical Software · doi:10.18637/jss.v059.i05 · Cited by 4,216 · see citing works
Self-Regulated Learning and Academic Achievement: An Overview
Zimmerman, B. J. (1990) · Educational Psychologist · doi:10.1207/s15326985ep2501_2 · Cited by 3,687 · see citing works
Self-explanations: How students study and use examples in learning to solve problems
Chi, M. (1989) · Cognitive Science · doi:10.1016/0364-0213(89)90002-5 · Cited by 2,287 · see citing works
Affordance, conventions, and design
Norman, D. A. (1999) · Interactions · doi:10.1145/301153.301168 · Cited by 2,040 · see citing works
Question Asking During Tutoring
Graesser, A. C., & Person, N. K. (1994) · American Educational Research Journal · doi:10.3102/00028312031001104 · Cited by 900 · see citing works
Students' questions: a potential resource for teaching and learning science
Chin, C., & Osborne, J. (2008) · Studies in Science Education · doi:10.1080/03057260701828101 · Cited by 747 · see citing works
Learning Versus Performance: An Integrative Review
Soderstrom, N. C., & Bjork, R. A. (2015) · Perspectives on Psychological Science · doi:10.1177/1745691615569000 · Cited by 651 · see citing works
Chan, C. K. Y., & Lee, K. K. W. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00269-3 · Cited by 634 · see citing works
Help Seeking and Help Design in Interactive Learning Environments
Aleven, V., Stahl, E., Schworm, S., Fischer, F., & Wallace, R. (2003) · Review of Educational Research · doi:10.3102/00346543073003277 · Cited by 609 · see citing works
Impact of AI assistance on student agency
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024) · Computers & Education · doi:10.1016/j.compedu.2023.104967 · Cited by 548 · see citing works
Self-citationTo seek help or not to seek help: The risks of self-disclosure.
Vogel, D. L., & Wester, S. (2003) · Journal of Counseling Psychology · doi:10.1037/0022-0167.50.3.351 · Cited by 548 · see citing works
Promises and challenges of generative artificial intelligence for human learning
Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02004-5 · Cited by 499 · see citing works
Self-citationLee, F. (1997) · Organizational Behavior and Human Decision Processes · doi:10.1006/obhd.1997.2746 · Cited by 464 · see citing works
Help-seeking: An understudied problem-solving skill in children
Gall, S. N.-L. (1981) · Developmental Review · doi:10.1016/0273-2297(81)90019-8 · Cited by 459 · see citing works
Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT
Dai, W., Lin, J., Jin, H., Li, T., Tsai, Y.-S., Gašević, D., & Chen, G. (2023) · 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt58122.2023.00100 · Cited by 395 · see citing works
Self-citationTo ask a question, one must know enough to know what is not known
Miyake, N., & Norman, D. A. (1979) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(79)90200-7 · Cited by 314 · see citing works
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring
Nye, B. D., Graesser, A. C., & Hu, X. (2014) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-014-0029-5 · Cited by 286 · see citing works
Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education
Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F. (2023) · Proceedings of the Tenth ACM Conference on Learning @ Scale · doi:10.1145/3573051.3596191 · Cited by 275 · see citing works
Reciprocity Reconsidered: Gouldner's `Moral Norm of Reciprocity' and Social Support
Uehara, E. S. (1995) · Journal of Social and Personal Relationships · doi:10.1177/0265407595124001 · Cited by 268 · see citing works
Help Helps, But Only So Much: Research on Help Seeking with Intelligent Tutoring Systems
Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0089-1 · Cited by 258 · see citing works
The Social Costs of Seeking Help
Lee, F. (2002) · The Journal of Applied Behavioral Science · doi:10.1177/0021886302381002 · Cited by 243 · see citing works
What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education
Dehouche, N., & Dehouche, K. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16757 · Cited by 217 · see citing works
Csanadi, A., Eagan, B., Kollar, I., Shaffer, D. W., & Fischer, F. (2018) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-018-9292-z · Cited by 205 · see citing works
Taboada, A., & Guthrie, J. (2006) · Journal of Literacy Research · doi:10.1207/s15548430jlr3801_1 · Cited by 203 · see citing works
Computer-supported example-based learning: When instructional explanations reduce self-explanations
Schworm, S., & Renkl, A. (2006) · Comput. Educ. · doi:10.1016/j.compedu.2004.08.011 · Cited by 198 · see citing works
Kitsantas, A., & Chow, A. (2005) · Computers & Education · doi:10.1016/j.compedu.2005.01.008 · Cited by 194 · see citing works
Newman, R. S. (1998) · Journal of Educational Psychology · doi:10.1037/0022-0663.90.4.644 · Cited by 180 · see citing works
Yan, L., Martínez‐Maldonado, R., & Gašević, D. (2024) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3636555.3636856 · Cited by 154 · see citing works
Self-citationChatGPT for Higher Education and Professional Development: A Guide to Conversational AI
Atlas, S. (2023) · Journal of Media Literacy Education · Cited by 144 · see citing works
Guzman, A. L. (2020) · Human-Machine Communication · doi:10.30658/hmc.1.3 · Cited by 143 · see citing works
Questions and Information Systems
Eileen, T. L., Lauer, T. W., Peacock, E., Graesser, A. C., & White, J. S. (2013) · Psychology Press eBooks · doi:10.4324/9780203763148 · Cited by 138 · see citing works
Assessing individual contributions to Collaborative Problem Solving: A network analysis approach
Swiecki, Z., Ruis, A. R., Farrell, C., & Shaffer, D. W. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.01.009 · Cited by 133 · see citing works
Self-citationThe Mathematical Foundations of Epistemic Network Analysis
Bowman, D. A., Swiecki, Z., Cai, Z., Wang, Y., Eagan, B. R., Linderoth, J., & Shaffer, D. W. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-67788-6_7 · Cited by 131 · see citing works
Self-citationMechanisms That Generate Questions
Lauer, T. W., Peacock, E., & Graesser, A. C. (2013) · Psychology Press eBooks · doi:10.4324/9780203763148-12 · Cited by 130 · see citing works
A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language
Paladines, J., & Ramírez, J. (2020) · IEEE Access · doi:10.1109/access.2020.3021383 · Cited by 123 · see citing works
LAK21: 11th International Learning Analytics and Knowledge Conference
(2021) · doi:10.1145/3448139 · Cited by 122 · see citing works
Strengthening the Student Toolbox: Study Strategies to Boost Learning.
Dunlosky, J. T. (2013) · Electronic scientific archive of UrFU (Ural Federal University) · Cited by 120 · see citing works
McLaren, B. M., van Gog, T., Ganoe, C. H., Karabinos, M., & Yaron, D. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.08.038 · Cited by 110 · see citing works
Harper, K., Etkina, E., & Lin, Y. (2003) · Journal of Research in Science Teaching · doi:10.1002/tea.10111 · Cited by 104 · see citing works
Pardos, Z. A., & Bhandari, S. (2024) · PLOS ONE · doi:10.1371/journal.pone.0304013 · Cited by 97 · see citing works
Accounting for Beneficial Effects of Worked Examples in Tutored Problem Solving
Salden, R. J. C. M., Koedinger, K. R., Renkl, A., Aleven, V., & McLaren, B. M. (2010) · Educational Psychology Review · doi:10.1007/s10648-010-9143-6 · Cited by 96 · see citing works
rENA: Epistemic Network Analysis
Marquart, C. L., Swiecki, Z., Collier, W., Eagan, B. R., Woodward, R., & Shaffer, D. W. (2017) · doi:10.32614/cran.package.rena · Cited by 86 · see citing works
Self-citationWhat are the most important predictors of computer science students' online help-seeking behaviors?
Hao, Q., Wright, E., Barnes, B., & Branch, R. M. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.04.016 · Cited by 83 · see citing works
Huet, N., Escribe, C., Dupeyrat, C., & Sakdavong, J.-C. (2011) · Computers in Human Behavior · doi:10.1016/j.chb.2010.09.003 · Cited by 59 · see citing works
Lee, D., Rothstein, R., Dunford, A., Berger, E., Rhoads, J. F., & DeBoer, J. J. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104082 · Cited by 47 · see citing works
Druian, P. R., & DePaulo, B. M. (1977) · Social Behavior and Personality An International Journal · doi:10.2224/sbp.1977.5.1.33 · Cited by 39 · see citing works
To Tutor or Not to Tutor: That is the Question
Leena, R., & Heffernan, N. T. (2009) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-60750-028-5-457 · Cited by 38 · see citing works
Students’ Approaches to Summarisation
Kirby, J. R., & Pedwell, D. (1991) · Educational Psychology · doi:10.1080/0144341910110306 · Cited by 38 · see citing works
Hints: Is It Better to Give or Wait to Be Asked?
Razzaq, L. M., & Heffernan, N. T. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13388-6_39 · Cited by 38 · see citing works
Online help-seeking in communities of practice: Modeling the acceptance of conceptual artifacts
Nistor, N., Schworm, S., & Werner, M. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.03.017 · Cited by 34 · see citing works
Ren, Y. (2011) · International Journal of Pedagogies and Learning · doi:10.5172/ijpl.2011.6.3.243 · Cited by 33 · see citing works
Evidence-centered Assessment for Writing with Generative AI
Cheng, Y., Lyons, K., Chen, G., Gašević, D., & Swiecki, Z. (2024) · Proceedings of the 14th Learning Analytics and Knowledge Conference · doi:10.1145/3636555.3636866 · Cited by 29 · see citing works
Self-citationA Systematic Review of Studies Exploring Help-Seeking Strategies in Online Learning Environments
Yang, F., & Stefaniak, J. (2023) · Online Learning · doi:10.24059/olj.v27i1.3400 · Cited by 27 · see citing works
Using Learner Trace Data to Understand Metacognitive Processes in Writing from Multiple Sources
Rakovic, M., Fan, Y., van der Graaf, J., Singh, S., Kilgour, J., Lim, L., Moore, J., Bannert, M., Molenaar, I., & Gasevic, D. (2022) · LAK22: 12th International Learning Analytics and Knowledge Conference · doi:10.1145/3506860.3506876 · Cited by 27 · see citing works
Self-citationEnhancing the Automatic Generation of Hints with Expert Seeding
Stamper, J. C., Barnes, T. M., & Croy, M. J. (2011) · International Journal of Artificial Intelligence in Education · doi:10.3233/jai-2011-021 · Cited by 27 · see citing works
Proceedings of the 14th Learning Analytics and Knowledge Conference
Flanagan, B., Wasson, B., & Gašević, D. (2024) · doi:10.1145/3636555 · Cited by 26 · see citing works
Self-citationShen, Y., Tang, L., Le, H., Tan, S., Zhao, Y., Shen, K., Li, X., Juelich, T., Wang, Q., Gašević, D., et al. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13562 · Cited by 26 · see citing works
Self-citationTang, L., Shen, K., Le, H., Shen, Y., Tan, S., Zhao, Y., Juelich, T., Li, X., Gašević, D., & Fan, Y. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13036 · Cited by 25 · see citing works
Self-citationAdvances in Quantitative Ethnography
Ruis, A. R., & Lee, S. B. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-67788-6 · Cited by 25 · see citing works
Proceedings of the Tenth ACM Conference on Learning @ Scale
(2023) · doi:10.1145/3573051 · Cited by 23 · see citing works
Enhancing the Automatic Generation of Hints with Expert Seeding
Stamper, J. C., Barnes, T. M., & Croy, M. J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13437-1_4 · Cited by 17 · see citing works
Analysing High-Level Help-Seeking Behaviour in ITSs
Mathews, M., Mitrović, T., & Thomson, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-70987-9_42 · Cited by 15 · see citing works
Classifying help seeking behaviour in online communities
Cross, S., Waters, Z., Kitto, K., & Zuccon, G. (2017) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3027385.3027442 · Cited by 14 · see citing works
Zou, S., Yan, X., & Fan, J. J. (2024) · Assessing Writing · doi:10.1016/j.asw.2024.100826 · Cited by 13 · see citing works
Mark-On: Encouraging Student Questions in Class
Jung, M., Kim, J., & So, H. (2016) · HCI Korea · doi:10.17210/hcik.2016.01.79 · Cited by 8 · see citing works
Question Asking During Collaborative Problem Solving in an Online Game Environment
Li, H., Duan, Y., Clewley, D., Morgan, B., Graesser, A. C., Shaffer, D. W., & Saucerman, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07221-0_80 · Cited by 7 · see citing works
Khoshnevis, I., & Parvinnejad, S. (2015) · International Journal of Learning and Development · doi:10.5296/ijld.v5i3.8271 · Cited by 5 · see citing works
Flagged first, then by citations
4 of the 80 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Mei, P., Brewis, D. N., Nwaiwu, F., Sumanathilaka, D., Alva-Manchego, F., & Demaree-Cotton, J. (2025) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2025.100140 · Cited by 50 · see citing works
“…produce ‘hallucinations ’ of entirely fictitious ideas presented as facts (Babl & Babl, 2023 who asked the GenAI to write academic abstracts), although the quality of the output has been shown to be enhanced through an iterative interaction with the user (Yang et al., 2025, Cheng et al., 2025).”
Fang, X., & Cai, Y. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70146 · Cited by 11 · see citing works
Fostering Human Agency in Age of AI
Viberg, O., Poquet, O., Kovanović, V., & Khosravi, H. (2025) · Journal of Learning Analytics · doi:10.18608/jla.2025.9485 · Cited by 11 · see citing works
Bacon, E. D., & Kraus, H. (2025) · rEFLections · doi:10.61508/refl.v32i1.280410 · Cited by 7 · see citing works
How to Use LLMs Ethically in Academic Writing?
Yu, T. (2025) · Education insights. · doi:10.70088/50wkze06 · Cited by 4 · see citing works
López, G. E. C., Reyes, J. U., & Reyes, E. C. (2025) · European Public & Social Innovation Review · doi:10.31637/epsir-2026-2155 · Cited by 3 · see citing works
Enhancing graduate students’ evaluative skills through AI-supported collaborative marking
Wu, D., Goh, T., Chen, D., & Yin, H. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1739222 · Cited by 3 · see citing works
Lee, Y., & Wu, J. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00615-8 · Cited by 3 · see citing works
Abdelghani, R., Murayama, K., Kidd, C., Sauzeon, H., & Oudeyer, P. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-29788-4_54 · Cited by 1 · see citing works
Grading Students via AI in Education: Opportunities and Challenges
Ateeq, A. A., Alfiras, M., Danish, F. M., Ibrahim, S. B., & Almuraqab, N. A. S. (2026) · Studies in systems, decision and control · doi:10.1007/978-3-032-04378-8_71 · Cited by 1 · see citing works
Li, Z., Tang, L., Xia, M., Li, X., Chen, N., Gašević, D., & Fan, Y. (2026) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3785022.3785130 · Cited by 1 · see citing works
Self-citationYan, W., Cui, Y., Chiu, T. K. F., Nakajima, T., & Kozima, H. (2026) · Australasian Journal of Educational Technology · doi:10.14742/ajet.11725 · Cited by 1 · see citing works
Çiftçi, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6375-2.ch010
Yau, K. W., Dong, T., Chai, C. S., Chiu, T. K. F., Meng, H. M. L., King, I., Wong, S. W. H., & Yeung, Y. A. M. (2025) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale66047.2025.11346635
Ariza, J. Á., Ariza, J. S. Á., & Hérnandez, C. H. (2025) · International Symposium on Accreditation of Engineering and Computing Education (ICACIT) · doi:10.1109/icacit66767.2025.11369724
Practical Applications of GenAI in TESOL
Savvidou, C. (2026) · Springer eBooks · doi:10.1007/978-3-032-14502-4_4
Talk, tech, and transformation: accountable discourse and generative AI in East Asian EMI classrooms
Yang, W., Yang, L.-Z., & Wang, M.-J. S. (2026) · Language Teaching Research · doi:10.1177/13621688261427680
How do Students Collaborate with GAI during Task Completion? An Exploratory Study
Wu, Y., Liu, J., & Li, H. (2026) · International Conference on Educational and Information Technology · doi:10.1109/iceit68991.2026.11521534
Chuang, C. K., Dai, W., Li, T., Lin, J., Li, X., Fan, Y., & Gašević, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29763-1_12
Self-citationSelf-regulated writing and follow-up questioning in GenAI-supported after-school EFL practice
Li, Z. (2026) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2026.2719851
Adopting GenAI-Assisted Writing Feedback in L2 Learning: A UTAUT Study of Medical Students
Ji, Z., Kaur, N., & Cui, H. (2026) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.25.8.10
Evaluating AI feedback: teacher expertise and hidden labour in writing instruction
Li, W., Sulaiman, N. A., & Matore, M. E. E. M. (2026) · English in Education · doi:10.1080/04250494.2026.2723772
Educational Paradigm Shifts in the Integration of Questioning and Prompting
Konstantinos, K. C., & Kotsanis, Y. (2026) · AI for Society · doi:10.3390/aisoc1010005
At the intersection of cognitive engagement and critical thinking in AI-assisted learning
Ding, L., Cheek, E., & May, T. (2026) · Computer-supported collaborative learning/The Computer-Supported Collaborative Learning Conference · doi:10.22318/cscl2026.416548
Who Holds Your Students’ Data? A Study on the Use of LLMs in Technology-Enhanced Learning
Arnau‐González, P., Gimeno-Gómez, D., Cánovas, A., & Katsigiannis, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37979-5_4
Sarkar, P. (2026) · International Journal of Social Science Research (IJSSR) · doi:10.70558/ijssr.2026.v3.i5.301390
Diversity Kompetenz in interkulturellen Teams
Gröschke, D. (2026) · Springer eBooks · doi:10.1007/978-3-658-08003-7_71-2
- Authors
- Cheng, Y., Fan, Y., Li, X., Chen, G., Gašević, D., & Swiecki, Z.
- Affiliations
- Monash University (AU)
- Peking University (CN)
- Corresponding author
- Cheng, Yixin
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2025-06
- Volume
- 8
- Pages
- 100374
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4408049766
- Cited by
- 21
- References
- 80
- Citations by year
- 2026: 14 · 2025: 7
- FWCI
- 8.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- Monash University Research Portal (Monash University) (publishedVersion, cc-by-nc-nd)
- HAL (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (62407001)
- Australian Research Council (DP240100069, DP220101209)
- Jacobs Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- generative AI, questioning, human tutoring, writing performance, epistemic network analysis, knowledge deficit, social coordination, common ground, knowledge gaps, AI-assisted learning
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
