The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index; none of the 24 sources its claims cite is checked for support yet.
What ChatGPT means for universities: Perceptions of scholars and students
Open accessAbstract
This study investigates the implications of ChatGPT, an AI-powered language model, for students and universities by examining the perceptions of scholars and students. The responses of seven scholars and 14 PhD students from four countries – Turkey, Sweden, Canada and Australia – are analysed using a thematic content analysis approach. Nine key themes emerge from the findings. According to their frequency of recurrence, these themes are: “Evolution of learning and education systems”, “changing role of educators”, “impact on assessment and evaluation”, “ethical and social considerations”, “future of work and employability”, “personalized learning”, “digital literacy and AI integration”, “AI as an extension of the human brain”, and “importance of human characteristics”. The potential benefits of AI in education as well as the challenges and barriers that may arise from its integration are discussed in the context of existing literature. Based on these findings, suggestions for future research include further exploration of the ethical implications of AI for education, the development of strategies to manage privacy concerns, and the investigation of how educational institutions can best prepare for the integration of AI technologies. The paper concludes by emphasizing the importance of understanding the potential opportunities and challenges associated with AI in higher education and the need for continued research in this area.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This study uses thematic content analysis of responses from seven scholars and 14 PhD students to examine perceptions of ChatGPT’s implications for students and universities. Nine themes emerged, with the most frequent being “evolution of learning and education systems,” “changing role of educators,” and “impact on assessment and evaluation.”
Key findings
- Nine key themes emerged from 24 comments by 21 participants: “Evolution of learning and education systems,” “changing role of educators,” “impact on assessment and evaluation,” “ethical and social considerations,” “future of work and employability,” “personalized learning,” “digital literacy and AI integration,” “AI as an extension of the human brain,” and “importance of human characteristics.”
- The most repeated themes were “evolution of learning and education systems” with 16 frequencies, “changing role of educators” with 13 frequencies, and “impact on assessment and evaluation” with 11 frequencies.
- Participants generally agreed that AI will significantly impact traditional learning methods, shift focus toward skills and competencies, and redefine the roles of educational institutions, while also recognizing challenges and expressing optimism for AI in education.
- The findings are discussed as consistent with existing literature on benefits and challenges, including personalized learning, changing educator roles, assessment concerns, digital literacy, ethics, and responsible implementation.
Methods
- Qualitative study using thematic content analysis of responses to an open-ended question about the potential impact of ChatGPT on students and universities.
- Data were collected from seven scholars via ResearchGate and 14 PhD students via Google Form, totaling 21 participants from Turkey, Sweden, Canada, and Australia; all PhD students were from Turkey, and most participants were from education-related fields.
- The resulting data set contained 24 comments from 21 participants; analysis followed a six-step thematic content analysis approach.
Limitations
- The paper states that the research is limited by a sample size of 21 scholars and PhD students and by using a single open-ended question for data collection.
- The paper states that it assumed participants understood the English question correctly because PhD programmes require adequate English, and it calls for including participants from more diverse backgrounds, countries, and academic fields.
- Evident from the text: all PhD students were from Turkey and the majority of participants were from education-related fields, which limits the breadth of perspectives; the paper also notes future research could benefit from additional qualitative and quantitative methods, including longitudinal and experimental designs.
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Claims the paper makes
17 Not checked yet · 8 Own interpretation · 5 Own result · 3 No basis given · 2 Own method
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These three themes show that scholars and students think that AI technologies will change our habits regarding the implementation and evaluation of education by looking at the capabilities of ChatGPT, one of the leading AI applications.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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In the paper
“However, integrating AI in education also raises concerns about assessment and evaluation, as traditional methods may become obsolete in the face of AIgenerated answers (Rudolph et al., 2023).”
What the sources say
- Not checked yetcites Rudolph et al., 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The changing role of educators, as discussed by Fırat (2023), Bozkurt (2023), and Sengupta and Chakraborty (2020), supports the idea that AI tools can increase student engagement and satisfaction by relieving university staff of routine tasks and allowing them to focus on higher-order skills and mentoring.”
What the sources say
- Not checked yetcites Fırat (2023)
- Not checked yetcites Bozkurt (2023)
- Not checked yetcites Sengupta and Chakraborty (2020)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Probably not a substantial claim. 28
- The quote only partly states this claim. 1
- It may state two separate claims as one. 18
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As can be seen in Figure 1, the most repeated themes were”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“According to their frequency of recurrence, these themes are:”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nine key themes emerge from the findings.”
- Also stated in: § Findings · plain
“Nine themes emerged from the analysis of the comments on the question”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
- Also stated in: § Findings · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Participants also recognize the challenges and potential issues that may arise in the process, but they express optimism for the future of AI in education.”
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The comments of scholars and PhD students reflect the diverse opinions and concerns of the participants regarding integrating Chat GPT and AI into education.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The COPE (Committee on Publication Ethics) guidelines have been followed in this research.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The study found that students had a positive attitude towards the chatbot and perceived it as a useful tool for language learning.”
What the sources say
- Not checked yetcites Xiong et al. (2021)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The introduction of AI technologies in education has the potential to revolutionize traditional educational practices, promote personalized learning experiences, and foster the development of soft skills (Fırat, 2023).”
What the sources say
- Not checked yetcites Fırat, 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The study also highlighted that chatbots could reduce the workload of university staff by answering frequently asked questions.”
What the sources say
- Not checked yetcites Sengupta and Chakraborty (2020)
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, as of early April 2023, there is an insufficient number of studies addressing the perspectives of scholars and students on the rapid use of ChatGPT.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Sullivan et al. (2023) found that ChatGPT has raised both academic integrity concerns and the potential for enhanced learning in higher education.”
What the sources say
- Not checked yetcites Sullivan et al. (2023)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, it is not yet possible to assert a consensus among educators, specifically concerning ChatGPT.”
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using the thematic content analysis method, the study was able to extract valuable insights and opinions from the collected responses, providing a deeper understanding of the potential implications of Chat GPT and AI integration in the context of students and universities.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“Recent advancements in AI, such as GPT-3.5 and GPT-4, have further implications for education as these models become increasingly capable of understanding and generating human-like text (Adiguzel et al., 2023).”
What the sources say
- Not checked yetcites Adiguzel et al., 2023
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For students and universities, this research highlights the transformative potential of AI technologies such as ChatGPT.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, this shift brings about challenges in assessment, digital literacy, and ethical considerations.”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The main differences between GPT-3.5 and GPT-4 are the parameter size of the models (GPT-3.5 has 175 billion parameters while GPT-4 has much more context length, the ability to use images as input in addition to text, and the use of Rule-Based Reward Models in its training (Koubaa, 2023;OpenAI, 2023).”
What the sources say
- Not checked yetcites Koubaa, 2023
- Not checked yetcites OpenAI, 2023
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“GPT-3, an auto-regressive language model, was found to perform strongly on many NLP datasets, such as answering questions and completing missing words in the training process (Brown et al. 2020).”
What the sources say
- Not checked yetcites Brown et al. 2020
boosted- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This development supports the theme of”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“They found that the advantages of ChatGPT include personalized learning, the encouragement of interactive learning, and the potential for formative assessment that supports teaching and learning and provides continuous feedback.”
What the sources say
- Not checked yetcites Baidoo-Anu and Owusu Ansah, (2023)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the consensus is that AI will significantly impact traditional learning methods, shifting the focus on skills and competencies and redefining the roles of educational institutions.”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Their content analysis of 100 news articles revealed mixed responses, with an emphasis on academic integrity and innovative assessment design.”
What the sources say
- Not checked yetcites Sullivan et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The findings from the thematic content analysis are consistent with the existing literature, highlighting the potential benefits and challenges of integrating AI, such as ChatGPT, into education.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The themes related to digital literacy, ethical and social considerations, and the importance of human-specific features are also evident and strongly emphasized in the related literature.”
What the sources say
- Not checked yetcites Willems (2023)
- Not checked yetcites Halaweh (2023)
- Not checked yetcites Crawford et al. (2023)
boosted- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In conclusion, integrating AI in education offers numerous opportunities to enhance learning experiences, personalize instruction, and transform the role of educators.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“However, the study also noted that the potential benefits for disadvantaged students and students' perspectives remain underrepresented in media discussions.”
What the sources say
- Not checked yetcites Sullivan et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“In a similar vein, Alotaibi et al. (2020) found that chatbots can improve student performance and knowledge retention, which supports the theme of personalized learning found in the analysis of this study.”
What the sources say
- Not checked yetcites Alotaibi et al. (2020)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Research has demonstrated that chatbot technologies can enhance student interaction and learning processes (D'Mello et al., 2014), enrich learning experiences by impacting student success in higher education (Winkler & Söllner, 2018), and potentially improve student motivation, engagement, and learning outcomes (Deng & Yu, 2023).”
What the sources say
- Not checked yetcites D'Mello et al., 2014
- Not checked yetcites Winkler & Söllner, 2018
- Not checked yetcites Deng & Yu, 2023
boostedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the integration of AI in education also raises critical questions about the potential challenges and obstacles that may emerge as a result of this technological shift.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“However, ChatGPT has been found to have issues of misinformation generation, bias in data training and privacy issues.”
What the sources say
- Not checked yetcites Baidoo-Anu and Owusu Ansah, (2023)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“GPT-4 is more reliable, more creative, and can handle much more nuanced instructions than GPT-3.5 (OpenAI, 2023).”
What the sources say
- Not checked yetcites OpenAI, 2023
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This research is limited with a sample size of 21 scholars and PhD students and an open-ended question for data collection.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The authors state this as an assumption.
PhD students and scholars understood the question asked in English correctly.
“Since PhD programmes require an adequate level of English, it was assumed that students and scholars understood the question asked in English correctly.”
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Applying AI technologies can facilitate analyzing students' learning procedures, performance, and needs, providing instructors with timely feedback, and assisting them in delivering adaptive instruction.”
From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Firat, 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
“Ethical concerns in instruction include incorrect or fabricated information, accessibility, and algorithm biases (Firat, 2023).”
From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Firat, 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
“Firat (2023) also mentioned overreliance on AI, data privacy, and unequal access to AI tools as challenges.”
From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Firat (2023)
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Firat (2023) indicated that implementing AI may require educators to change their role from being instructors to guides or facilitators.”
From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Firat (2023)
In the citing paper
“ChatGPT has the potential to offer personalized learning experiences and support an effective learning process by providing students with customized feedback and explanations tailored to their needs and abilities”
From A systematic literature review of empirical research on ChatGPT in education (2024) · cites this paper as [27]
In the citing paper
“Previous studies indicate that students are optimistic about the potential of ChatGPT to be used in universities [22,23].”
From ChatGPT for Academic Purposes: Survey Among Undergraduate Healthcare Students in Malaysia (2024) · cites this paper as [22]
In the citing paper
“They believe that ChatGPT can be used to help them with their studies, such as providing personalized feedback and answering questions.”
From ChatGPT for Academic Purposes: Survey Among Undergraduate Healthcare Students in Malaysia (2024) · cites this paper as [22]
In the citing paper
“Students are concerned about the potential for ChatGPT to be used for academic dishonesty and they are aware of the limitations of ChatGPT and the need for careful consideration before it is widely adopted.”
From ChatGPT for Academic Purposes: Survey Among Undergraduate Healthcare Students in Malaysia (2024) · cites this paper as [22]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Literature review
“Okuyama and Suzuki (2023) proposed a new training methodology for GPT-4 that leverages large-scale semantic discrimination tasks to improve the model's ability to understand the meaning of a text.”
§ References
“18. Okuyama, K., & Suzuki, K. (2023). Correlators of double scaled SYK at one-loop. arXiv preprint. arXiv:2303.07552.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Introduction
“(Haque et al., 2023;Sardana et al., 2023)”
§ References
“12. Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (2022).”
Citations
Cites
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 · cited 1×
“GPT-3, an auto-regressive language model, was found to perform strongly on many NLP datasets, such as answering questions and completing missing words in the training process (Brown et al. 2020).”
BackgroundChatGPT 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 1×
“For example, Kasneci et al. (2023) examined the potential benefits and risks of ChatGPT for education, while Willems (2023) discussed the wider ethical implications of using such models in universities.”
BackgroundBAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 3,939
Sallam, M. (2023) · Healthcare · doi:10.3390/healthcare11060887 · Cited by 3,126 · cited 2×
“While some consider that this AI’s pioneering application will create a paradigm shift in various fields, including education (Bozkurt, 2023; Sallam, 2023), others emphasize the possible ethical challenges of ChatGPT and consider it a disruptive technology (Haque et al., 2023; Sardana et al., 2023).”
BackgroundBaidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806 · cited 2×
“Prior to the advent of ChatGPT, a study by Sengupta and Chakraborty (2020) investigated the use of chatbots in higher education and found that they can be an effective tool for improving student engagement and satisfaction.”
BackgroundMethodChatGPT is fun, but not an author
Thorp, H. H. (2023) · Science · doi:10.1126/science.adg7879 · Cited by 1,437 · cited 4×
“In a study that criticizes the use of ChatGPT in education, Thorp (2023) emphasized that this application may be fun but has serious consequences in the world of science and academia.”
BackgroundInfluentialRevolutionizing 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×
“5 and GPT-4, have further implications for education as these models become increasingly capable of understanding and generating human-like text (Adiguzel et al., 2023).”
BackgroundChatGPT in higher education: considerations for academic integrity and student learning
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.17 · Cited by 853 · cited 2×
“Willems (2023) discussed the ethical implications of using large language models like ChatGPT in universities, while Halaweh (2023) and Crawford et al.”
Background
Research works printed but not linked: 3
- Biswas, S. (2023, February). Role of ChatGPT in education. https://ssrn.com/abstract=4369981
- Smith, K., Jones, L., & Davis, B. (2023). GPT-4 and beyond: Exploring the limits of language AI. Frontiers in Artificial Intelligence, 6, 12345. https://doi.org/10.3389/ frai.2023.12345 · DOI
- Mehmet Firat. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Cited by
Sorted by citations
The role of ChatGPT in higher education: Benefits, challenges, and future research directions
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.29 · Cited by 561
“…to aid in higher education by providing assistance with research, analysis, and writing tasks, concerns surrounding potential bias and falsified information need to be addressed to ensure its use is ethical and reliable (Dwivedi et al., 2023; Firat, 2023; Gatzemeier, 2021; Silberg & Manyika, 2019).”
BackgroundChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis
Zheng, Z., Zhang, O., Borgs, C., Chayes, J., & Yaghi, O. M. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c05819 · Cited by 590
Impact of the Implementation of ChatGPT in Education: A Systematic Review
Montenegro-Rueda, M., Fernández-Cerero, J., Fernández-Batanero, J. M., & López-Meneses, E. (2023) · Computers · doi:10.3390/computers12080153 · Cited by 509
“The role of the teacher in the context of the use of artificial intelligence is crucial for improving educational practice and fostering new teaching methodologies [23].”
BackgroundA Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00468-z · Cited by 493
“Thus, critical thinking, creativity, and problem-solving, as well as AI and digital literacy, should be explicitly incorporated into the learning outcomes of course designs (Chan, 2023; Firat, 2023).”
InfluentialImran, M., & Almusharraf, N. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13605 · Cited by 421
Metadata
- Authors
- Fırat, M.
- Affiliations
- Anadolu University (TR)
- Corresponding author
- Mehmet Fırat
- ORCID
- Mehmet Fırat
- Venue
- Journal of Applied Learning & Teaching
- Publisher
- Open Access Publishing Association
- Published
- 2023-04-19
- Volume
- 6
- Issue
- 1
- ISSN
- 2591-801X (ISSN-L)
- OpenAlex
- W4366422585
- Semantic Scholar
- 6f9f5e84221b991756500676031e0ebb4dc00b0d
- Cited by
- 431
- References
- 27
- Citations by year
- 2026: 55 · 2025: 131 · 2024: 185 · 2023: 60
- FWCI
- 100.90
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Applied Learning & Teaching (publishedVersion, cc-by)
- 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 › Ethics and Social Impacts of AI
- Keywords
- ChatGPT, large language models, artificial intelligence in education, higher education, student perceptions, doctoral students, role of educators, assessment and evaluation, AI ethics, digital literacy, personalized learning, employability, future of work, privacy concerns, AI integration in universities
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.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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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