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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
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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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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
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 ›
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