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.
“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
The Implications section (page unavailable) recommends teaching prompt engineering, drawing on the finding that ChatGPT changed answers after students asked additional questions. But the Procedure and Materials sections describe students using ChatGPT, not receiving prompt-engineering instruction; the paper therefore does not demonstrate the impact of teaching it. (LLM, conf=0.87)
Experimental — accuracy not yet measuredTrust in generative AI tools can lead to overreliance, discouraging users from exploring alternative perspectives and reducing variability of ideas, resulting in more homogeneous outputs.
“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 “ <scp>ChatGPT</scp> can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of <scp>ChatGPT</scp> content into academic writing (2025) · cites this paper as Ding et al., 2023
The Implications section reports that many students blindly trusted ChatGPT’s answers and heavily relied on them, supporting the overreliance part of the claim. The Accuracy and accountability of AI section discusses algorithms excluding alternative perspectives, but does not link that to users’ trust. The paper does not report reduced variability of ideas or more homogeneous outputs. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe pre-workshop survey results that students used ChatGPT for various purposes are consistent with findings of numerous studies that investigated student use and perceptions of ChatGPT.
“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
The Conclusion (page unavailable) reports a study of undergraduates using ChatGPT as a virtual tutor for physics questions and describes students' trust and perceptions, supporting the citation as an example of research on student use and perceptions. It does not report that these students appreciated personalized guidance, better understanding of difficult concepts, or help with writing tasks; the AI in education section discusses writing assistance in other studies, not as this study's finding. (LLM, conf=0.86)
Experimental — accuracy not yet measuredStudents appreciated ChatGPT's ability to provide personalized guidance, facilitate understanding of difficult concepts, or assist with writing tasks.
“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
The Conclusion and survey tables (page unavailable) report that students used ChatGPT as a virtual tutor for physics questions and rated its usefulness for learning, which supports the general point that they valued its learning assistance. The paper does not report that these students appreciated personalized guidance or help with writing tasks; its Introduction and AI in education section discuss those uses as possibilities or findings from other work. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMany students worried about the reliability and accuracy of ChatGPT's outputs, citing issues such as inaccuracies, inconsistencies, and the inability to handle complex subjects.
“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
The Analyses and results section reports inaccurate answers and instances in which ChatGPT changed an answer after further questioning. But it does not establish that many students worried about reliability or cited an inability to handle complex subjects; its Conclusion says almost half trusted ChatGPT's answers regardless of accuracy. No page numbers are available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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.
“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)
In Analyses and results, answer accuracy did not differ significantly between the Trust and Partial Trust groups, and the Trust group rated ChatGPT significantly easier to use. Its higher future-use intention was only borderline significant (p = 0.059). The study reports associations between trust and perceptions, not evidence that trust caused more positive perceptions or increased usage intentions. (LLM, conf=0.88)
Experimental — accuracy not yet measuredChatGPT can foster student dependency that may impede the development of higher-order thinking skills.
“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
The Implications section reports that many students blindly trusted ChatGPT and heavily relied on its answers, and recommends strengthening critical thinking. It does not establish that this reliance impeded the development of higher-order thinking skills or discouraged independent problem-solving. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPrompt engineering is a fundamental skill for eliciting useful responses from GenAI.
“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
Without explicit instruction, students tend to blindly trust GenAI output.
“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 AIAP framework was conceived in response to issues EAP practitioners were encountering, including an increase in AI-related academic misconduct, a lack of student understanding of how to interact with or prompt AI tools effectively, and a tendency to blindly trust GenAI output.
“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
This finding provides empirical evidence for Ding et al.'s, Farrelly and Baker's and Walter's 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.
“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)
Some students expressed concerns about the reliability and accuracy of ChatGPT's outputs.
“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
Ding et al (2023) found that nearly half of students in an introductory physics course trusted ChatGPT's answers to physics problems regardless of their accuracy, and those expressing trust were more likely to report intentions of future ChatGPT use.
“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)
GenAI tools, if not used effectively, can short-circuit students' learning process.
“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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- 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
- Physical Sciences › Computer Science › Information Systems › 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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