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Students' perceptions of using ChatGPT in a physics class as a virtual tutor
Open accessAbstract
The latest development of Generative Artificial Intelligence (GenAI), particularly ChatGPT, has drawn the attention of educational researchers and practitioners. We have witnessed many innovative uses of ChatGPT in STEM classrooms. However, studies regarding students’ perceptions of ChatGPT as a virtual tutoring tool in STEM education are rare. The current study investigated undergraduate students’ perceptions of using ChatGPT in a physics class as an assistant tool for addressing physics questions. Specifically, the study examined the accuracy of ChatGPT in answering physics questions, the relationship between students’ ChatGPT trust levels and answer accuracy, and the influence of trust on students’ perceptions of ChatGPT. Our finding indicates that despite the inaccuracy of GenAI in question answering, most students trust its ability to provide correct answers. Trust in GenAI is also associated with students’ perceptions of GenAI. In addition, this study sheds light on students’ misconceptions toward GenAI and provides suggestions for future considerations in AI literacy teaching and research.
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“This underscores the impact of teaching prompt engineering, providing empirical support for calls for this type of instruction in the literature (Ding et al., 2023; Walter, 2024).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Ding et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Trust in these tools can lead to overreliance, discouraging users from exploring alternative perspectives and reducing the variability of ideas, which results in more homogeneous outputs (Ding et al., 2023;Doshi & Hauser, 2024).”
From “ ChatGPT can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of ChatGPT content into academic writing (2025) · cites this paper as Ding et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This is consistent with the findings of numerous studies that investigated student use and perceptions of ChatGPT, which found that students appreciated the technology's ability to provide personalized guidance, facilitate understanding of difficult concepts, and assist with writing tasks (Budhiraja et al. 2024;Ding et al. 2023;Li 2023;Mahapatra 2024;Malik et al. 2023;Yan 2023).”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Ding et al. 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Students appreciated Chat-GPT's ability to provide personalized guidance, facilitate understanding of difficult concepts, or assist with writing tasks (Budhiraja et al. 2024;Ding et al. 2023;Li 2023;Mahapatra 2024;Malik et al. 2023;Yan 2023).”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Ding et al. 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Many worried about the reliability and accuracy of ChatGPT's outputs, citing issues such as inaccuracies, inconsistencies, and the inability to handle complex subjects (Amoozadeh et al. 2024;Ding et al. 2023;Malik et al. 2023;Zhao et al. 2024).”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Ding et al. 2023
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For example, Ding et al. (2023) found that higher trust in ChatGPT did not necessarily correspond with receiving more accurate answers but did lead to more positive perceptions and increased usage intentions.”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Ding et al. (2023)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Nevertheless, it could also foster a dependency that might impede the development of HOTS.The ease of accessing information and problemsolving assistance through tools like ChatGPT could discourage students from engaging in thorough thinking or independent problem-solving (Chan & Hu, 2023;Ding et al., 2023).”
From Empowering ChatGPT with Guidance Mechanism in Blended Learning: Effect of Self-Regulated Learning, Higher-Order Thinking Skills, and Knowledge Construction (2024) · cites this paper as Ding et al., 2023
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Prompt engineering is a fundamental skill for eliciting useful responses from GenAI (Ding et al., 2023; Walter, 2024).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Ding et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“Without this explicit instruction, students tend to blindly trust GenAI output (Ding et al., 2023).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Ding et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“This framework was conceived in response to issues we, as EAP practitioners, were encountering on the ground, such as an increase in AI-related academic misconduct, a lack of understanding from students of how to interact with or prompt these tools effectively (Walter, 2024) and a tendency to blindly trust information output from GenAI (Ding et al., 2023).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Ding et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“This fi nding provides empirical evidence for Ding et al. ' s (2023), Farrelly and Baker ' s (2023) and Walter ' s (2024) suggestion that the integration of AI literacy into education should include activities which require students to analyse and assess GenAI output to foster critical thinking skills.”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Ding et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“However, some students also expressed concerns about the reliability and accuracy of ChatGPT's outputs, aligning with findings from other user studies (Amoozadeh et al. 2024;Ding et al. 2023;Malik et al. 2023;Zhao et al. 2024).”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Ding et al. 2023
The cited source says what the claim attributes to it.
In the citing paper
“In an introductory physics course, Ding et al (2023) found that nearly half of the students trusted ChatGPT's answers to physics problems regardless of their accuracy, with those expressing trust being more likely to report intentions of future ChatGPT use.”
From Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education (2025) · cites this paper as Ding et al (2023)
The cited source says what the claim attributes to it.
In the citing paper
“These findings provide preliminary evidence that genAI tools, if not used effectively, can short-circuit students' learning process.”
From Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education (2025) · cites this paper as Ding et al (2023)
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Citations
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Discovering Statistics Using SPSS
Field, A. P., & Miles, J. N. V. (2000) · Medical Entomology and Zoology · Cited by 28,269
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Hanrahan, G. (2008) · CRC Press eBooks · doi:10.1201/9781420067972-10 · Cited by 8,196
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) · doi:10.35542/osf.io/5er8f · Cited by 7,552
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×
“GenAI is a subclass of machine learning (ML) algorithms that can learn from text, images, audio, and video to produce new content based on trained data (Kasneci et al., 2023).”
BackgroundMethodInfluentialOn seeing human: A three-factor theory of anthropomorphism
Epley, N., Waytz, A., & Cacioppo, J. T. (2007) · Psychological Review · doi:10.1037/0033-295x.114.4.864 · Cited by 4,069 · cited 1×
“One factor contributing to these misconceptions is anthropomorphism, which involves attributing human-like characteristics to AI, such as feelings, mental states, or behavioral characteristics (Airenti, 2015; Epley et al., 2007).”
BackgroundBAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 3,939
(2018) · Cited by 3,510
Kaplan, A., & Haenlein, M. (2019) · Business Horizons · doi:10.1016/j.bushor.2018.08.004 · Cited by 3,140
Sallam, M. (2023) · Healthcare · doi:10.3390/healthcare11060887 · Cited by 3,126 · cited 1×
“Even worse, due to the linguistic proficiency of GenAI, it can disguise the misinformation as truth or facts and make them appear scientifically reasonable (Sallam, 2023).”
BackgroundBaidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
Students' voices on generative ai: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · arXiv · doi:10.48550/arxiv.2305.00290 · Cited by 2,504 · cited 2×
“On one hand, students acknowledge the benefits of AI and believe it can be used as a learning tool to provide personalized and immediate learning support (Chan & Hu, 2023).”
BackgroundStudents’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301 · cited 2×
“On one hand, students acknowledge the benefits of AI and believe it can be used as a learning tool to provide personalized and immediate learning support (Chan & Hu, 2023).”
BackgroundWhat Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024
Misconceptions Reconceived: A Constructivist Analysis of Knowledge in Transition
Smith, J. P., diSessa, A. A., & Roschelle, J. (1994) · Journal of the Learning Sciences · doi:10.1207/s15327809jls0302_1 · Cited by 1,718
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261
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 1×
“Accordingly, many discussions have been held on its potential to transform traditional teaching practices (e.g., Adiguzel et al., 2023; Baidoo-Anu & Owusu Ansah, 2023).”
BackgroundPavlik, J. V. (2023) · Journalism & Mass Communication Educator · doi:10.1177/10776958221149577 · Cited by 972 · cited 1×
“…educators have suggested that Chat-GPT can be used as a tool to support teaching across a wide range of subjects, including programming (Sun et al., 2022), engineering (Qadir, 2023), journalism and media (Pavlik, 2023), nursing education (O’Connor & ChatGPT, 2023), and business (Alshater, 2022).”
MethodCollaborating with ChatGPT in argumentative writing classrooms
Su, Y., Lin, Y., & Lai, C. (2023) · Assessing Writing · doi:10.1016/j.asw.2023.100752 · Cited by 760
Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning
Mhlanga, D. (2023) · doi:10.2139/ssrn.4354422 · Cited by 719 · cited 2×
“It is crucial to recognize that the objectivity and accuracy of artificial intelligence are contingent on the quality of the training data (Mhlanga, 2023).”
BackgroundResultOpen AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning
Mhlanga, D. (2023) · Sustainable Development Goals Series · doi:10.1007/978-3-031-37776-1_17 · Cited by 718
Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education
Qadir, J. (2023) · 2023 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon54358.2023.10125121 · Cited by 707 · cited 1×
“…educators have suggested that Chat-GPT can be used as a tool to support teaching across a wide range of subjects, including programming (Sun et al., 2022), engineering (Qadir, 2023), journalism and media (Pavlik, 2023), nursing education (O’Connor & ChatGPT, 2023), and business (Alshater, 2022).”
MethodOpen artificial intelligence platforms in nursing education: Tools for academic progress or abuse?
O’Connor, S., & ChatGPT (2023) · Nurse Education in Practice · doi:10.1016/j.nepr.2022.103537 · Cited by 585
Drawing a Scientist: What We Do and Do Not Know After Fifty Years of Drawings
Finson, K. D. (2002) · School Science and Mathematics · doi:10.1111/j.1949-8594.2002.tb18217.x · Cited by 584
Cheng, X., Zhang, X., Cohen, J., & Mou, J. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.102940 · Cited by 536
Belanche, D., Casaló, L. V., Schepers, J., & Flavián, C. (2021) · Psychology and Marketing · doi:10.1002/mar.21532 · Cited by 419
Seeking help in large college classes: A person-centered approach
Karabenick, S. A. (2003) · Contemporary Educational Psychology · doi:10.1016/s0361-476x(02)00012-7 · Cited by 396
Cultivating admiration in brands: Warmth, competence, and landing in the “golden quadrant”
Aaker, J. L., Garbinsky, E. N., & Vohs, K. D. (2012) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2011.11.012 · Cited by 355
Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information
Sundar, S. S., & Kim, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300768 · Cited by 347
Gong, B., Nugent, J. P., Guest, W., Parker, W., Chang, P. J., Khosa, F., & Nicolaou, S. (2019) · Academic Radiology · doi:10.1016/j.acra.2018.10.007 · Cited by 325
Alshater, M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4312358 · Cited by 304
Chatterjee, J., & Dethlefs, N. (2023) · Patterns · doi:10.1016/j.patter.2022.100676 · Cited by 263
A Review of Trust in Artificial Intelligence: Challenges, Vulnerabilities and Future Directions
Lockey, S., Gillespie, N. A., Holm, D., & Someh, I. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.664 · Cited by 233
Bisdas, S., Topriceanu, C.-C., Zakrzewska, Z., Irimia, A.-V., Shakallis, L., Subhash, J., Casapu, M.-M., Leon-Rojas, J., Pinto dos Santos, D., Andrews, D. M., et al. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.795284 · Cited by 232
Natural language processing in an intelligent writing strategy tutoring system
McNamara, D. S., Crossley, S. A., & Roscoe, R. D. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0258-1 · Cited by 232
Investigating Explainability of Generative AI for Code through Scenario-based Design
Sun, J., Liao, Q. V., Muller, M., Agarwal, M., Houde, S., Talamadupula, K., & Weisz, J. D. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511119 · Cited by 219 · cited 3×
“What makes it more distinguishable from other GenAI is its unique ability to not only provide a response but also generate related content based on subsequent questions and prompts derived from its initial responses (Sun et al., 2022).”
BackgroundMethodInfluentialUnderstanding User Trust in Artificial Intelligence-Based Educational Systems: Evidence from China
Qin, F., Li, K., & Yan, J. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.12994 · Cited by 204
Conversations with AutoTutor Help Students Learn
Graesser, A. C. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0086-4 · Cited by 202
Pizzi, G., Vannucci, V., Mazzoli, V., & Donvito, R. (2023) · Psychology & Marketing · doi:10.1002/mar.21813 · Cited by 194
Myths, mis- and preconceptions of artificial intelligence: A review of the literature
Bewersdorff, A., Zhaı, X., Roberts, J., & Nerdel, C. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100143 · Cited by 188
Mertala, P., Fagerlund, J., & Calderon, O. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100095 · Cited by 186
The Cognitive Bases of Anthropomorphism: From Relatedness to Empathy
Airenti, G. (2015) · International Journal of Social Robotics · doi:10.1007/s12369-014-0263-x · Cited by 181
ChatGPT in physics education: A pilot study on easy-to-implement activities
Bitzenbauer, P. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13176 · Cited by 181
Designing for Responsible Trust in AI Systems: A Communication Perspective
Liao, Q. V., & Sundar, S. S. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533182 · Cited by 165
Investigating Students’ Perceptions towards Artificial Intelligence in Medical Education
Buabbas, A. J., Miskin, B., Alnaqi, A. A., Ayed, A. K., Shehab, A. A., Syed-Abdul, S., & Uddin, M. (2023) · Healthcare · doi:10.3390/healthcare11091298 · Cited by 152
Should ChatGPT Be Biased? Challenges and Risks of Bias in Large Language Models
Ferrara, E. (2023) · doi:10.2139/ssrn.4627814 · Cited by 133
Kim, K., Kwon, K., Ottenbreit-Leftwich, A. T., Bae, H., & Glazewski, K. D. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11600-3 · Cited by 123
Hançer, E., & Karaboğa, D. (2016) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2016.06.004 · Cited by 122
Schmidt-Fajlik, R. (2023) · AsiaCALL Online Journal · doi:10.54855/acoj.231417 · Cited by 118
What to do about science “misconceptions”
Gil‐Pérez, D., & Carrascosa, J. (1990) · Science Education · doi:10.1002/sce.3730740504 · Cited by 110
González‐Jiménez, H. (2018) · Futures · doi:10.1016/j.futures.2018.01.004 · Cited by 83
Deep learning applications in manufacturing operations: a review of trends and ways forward
Sahoo, S., Kumar, S., Abedin, M. Z., Lim, W. M., & Jakhar, S. K. (2022) · Journal of Enterprise Information Management · doi:10.1108/jeim-01-2022-0025 · Cited by 81
Cognitive anatomy of tutor learning: Lessons learned with SimStudent.
Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Cohen, W. W., Stylianides, G. J., & Koedinger, K. R. (2013) · Journal of Educational Psychology · doi:10.1037/a0031955 · Cited by 80
Gillissen, A., Kochanek, T., Zupanic, M., & Ehlers, J. (2022) · Healthcare · doi:10.3390/healthcare10040723 · Cited by 74
What Students Can Learn About Artificial Intelligence – Recommendations for K-12 Computing Education
Michaeli, T., Romeike, R., & Seegerer, S. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-43393-1_19 · Cited by 73
Review and Discussion of Children’s Conceptions of Computers
Rücker, M. T., & Pinkwart, N. (2015) · Journal of Science Education and Technology · doi:10.1007/s10956-015-9592-2 · Cited by 70
Nazaretsky, T., Cukurova, M., Ariely, M., & Alexandron, G. (2021) · doi:10.35542/osf.io/dzqju · Cited by 60
Falode, O. C. (2018) · Malaysian Online Journal of Educational Technology · doi:10.17220/mojet.2018.03.005 · Cited by 19
Michaeli, T., Seegerer, S., & Romeike, R. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.06450 · Cited by 5
(Mis)conceptions and perceptions of artificial intelligence: A scoping reviewPreprint
Ding, L., Li, T., & Turkson, A. (2024) · doi:10.35542/osf.io/y627r · Cited by 3
Self-citation
Research works printed but not linked: 3
- Bingham, A. J., & Witkowsky, P. (2022). Deductive and inductive approaches to qualitative data analysis. In C. Vanover, P. Mihas, & J. Saldaña (Eds.), Analyzing and interpreting qualitative data: After the interview (pp. 133–146). SAGE Publications.
- Hu, K. (2023). ChatGPT sets record for fastest-growing user base. Reuters.
- Removed for blinded review.
Cited by
Sorted by citations
Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025) · Computers & Education · doi:10.1016/j.compedu.2024.105224 · Cited by 531
Wang, J., & Fan, W. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04787-y · Cited by 322
RetractedLee, 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
“The ease of accessing information and problem-solving assistance through tools like ChatGPT could discourage students from engaging in thorough thinking or independent problem-solving (Chan & Hu, 2023; Ding et al., 2023).”
BackgroundChatGPT in the higher education: A systematic literature review and research challenges
Baig, M. I., & Yadegaridehkordi, E. (2024) · International Journal of Educational Research · doi:10.1016/j.ijer.2024.102411 · Cited by 208
Νικολοπούλου, Κ. (2024) · International Journal of Changes in Education · doi:10.47852/bonviewijce42022489 · Cited by 208
Metadata
- Authors
- Ding, L., Li, T., Jiang, S., & Gapud, A.
- Affiliations
- University of South Alabama (US)
- Ball State University (US)
- North Carolina State University (US)
- Corresponding author
- Ding, Lu
- Venue
- International Journal of Educational Technology in Higher Education
- Published
- 2023-12-22
- Volume
- 20
- Issue
- 1
- ISSN
- 2365-9440 (ISSN-L)
- OpenAlex
- W4390082902
- Cited by
- 208
- References
- 59
- Citations by year
- 2026: 67 · 2025: 97 · 2024: 44
- FWCI
- 48.82
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal of Educational Technology in Higher Education (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- ChatGPT, generative AI, online tutoring, physics education, student perceptions, trust in AI, answer accuracy, AI literacy, misconceptions about AI, undergraduate students
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