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.
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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
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“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
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.23 · Cited by 421
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100221 · Cited by 402
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Lo, C. K., Hew, K. F., & Jong, M. S.-Y. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105100 · Cited by 392
Chang, D. H., Lin, M. P.-C., Hajian, S., & Wang, Q. Q. (2023) · Sustainability · doi:10.3390/su151712921 · Cited by 370
“In particular, research has been conducted to investigate the methods and impacts of chatbot implementation in education in recent years [25,32,33].”
BackgroundWang, H., Dang, A., Wu, Z., & Mac, S. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100326 · Cited by 272
“…the ethical use of GenAI in education, numerous studies are calling for further research on principles, strategies, and resources to harness GenAI as a value-driven opportunity to enhance learning (Ali et al., 2023; Firat, 2023; Lee et al., 2023; Michel-Villarreal et al., 2023; Van Wyk, 2024).”
BackgroundHigher education students’ perceptions of ChatGPT: A global study of early reactions
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., et al. (2025) · PLOS ONE · doi:10.1371/journal.pone.0315011 · Cited by 376
Generative AI and Teachers’ Perspectives on Its Implementation in Education
Kaplan-Rakowski, R., Grotewold, K., Hartwick, P., & Papin, K. (2023) · Journal of Interactive Learning Research · doi:10.70725/815246mfssgp · Cited by 284
“Further, the rapid emergence and prevalence of ChatGPT have prompted educators to carefully examine this AI tool (Firat, 2023; Lampropoulos et al., 2023; Reuters, 2023).”
BackgroundThe use of ChatGPT in teaching and learning: a systematic review through SWOT analysis approach
Mai, D. T. T., da, C. A. N., & Hanh, N. V. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1328769 · Cited by 363
“The integration of ChatGPT in teaching and learning opens up many opportunities to enhance the learning journey, customize personalized instruction methods, and revolutionize the role of educators (Firat, 2023).”
BackgroundKarataş, F., Abedi, F. Y., Ozek Gunyel, F., Karadeniz, D., & Kuzgun, Y. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12574-6 · Cited by 252
“…as an efficient tool to provide useful information and enables the students to access information effectively, to generate lesson plans and save time by creating teaching materials, worksheets, and assessments (Farrokhnia et al., 2023; Fauzi et al., 2023; Firat, 2023; Stepane-chko & Kozub, 2023).”
Tossell, C. C., Tenhundfeld, N. L., Momen, A., Cooley, K., & de Visser, E. J. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3355015 · Cited by 218
“These technologies also have the potential to make completing mundane tasks more efficient [9], including grading, answering common student queries, virtual tutoring, and developing course plans and materials [10].”
BackgroundThe use of ChatGPT in the digital era: Perspectives on chatbot implementation
Limna, P., Kraiwanit, T., Jangjarat, K., Klayklung, P., & Chocksathaporn, P. (2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.32 · Cited by 294
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Baig, M. I., & Yadegaridehkordi, E. (2024) · International Journal of Educational Research · doi:10.1016/j.ijer.2024.102411 · Cited by 208
Effects of Generative Chatbots in Higher Education
Ilieva, G., Yankova, T., Klisarova-Belcheva, S., Dimitrov, A., Bratkov, M., & Angelov, D. (2023) · Information · doi:10.3390/info14090492 · Cited by 258
“Firat [52] has investigated the implications of ChatGPT for students and universities by examining the perceptions of scholars and students.”
BackgroundA systematic literature review of empirical research on ChatGPT in education
Albadarin, Y., Saqr, M., Pope, N., & Tukiainen, M. (2024) · Discover Education · doi:10.1007/s44217-024-00138-2 · Cited by 191
Kelly, A., Sullivan, M., & Strampel, K. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.6.12 · Cited by 177
“Firat (2023) gathered a small number of student perspectives on the implications of ChatGPT for universities, though these responses were from postgraduate students enrolled in doctoral programs.”
ChatGPT—A Challenging Tool for the University Professors in Their Teaching Practice
Kiryakova, G., & Angelova, N. (2023) · Education Sciences · doi:10.3390/educsci13101056 · Cited by 171
Student Attitudes towards Chat GPT: A Technology Acceptance Model Survey
Yilmaz, H. S., Maxutov, S., Baitekov, A., & Balta, N. (2023) · International Educational Review · doi:10.58693/ier.114 · Cited by 211
“ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI
Baek, C., Tate, T., & Warschauer, M. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100294 · Cited by 306
Exploring the ethical considerations of using Chat GPT in university education
Huallpa, E. A. (2023) · Periodicals of Engineering and Natural Sciences (PEN) · doi:10.21533/pen.v11i4.3770 · Cited by 145
“Students can interact with a conversational bot powered by artificial intelligence to ask questions, get clarifications, and get tailored responses [6].”
BackgroundOu, A. W., Stöhr, C., & Malmström, H. (2024) · System · doi:10.1016/j.system.2024.103225 · Cited by 224
“Despite the extensive attention devoted to the ethical implications of ChatGPT and similar chatbots in higher education, empirical evidence remains scarce (an exception is Firat, 2023).”
BackgroundWill artificial intelligence drive the advancements in higher education? A tri-phased exploration
Kumar, S., Rao, P., Singhania, S., Verma, S., & Kheterpal, M. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123258 · Cited by 131
Al-Abdullatif, A. M., & Alsubaie, M. A. (2024) · Behavioral Sciences · doi:10.3390/bs14090845 · Cited by 126
“Since its introduction, the usage statistics for ChatGPT have been nothing short of impressive [5,6].”
Opportunities, Challenges, and Strategies for Using ChatGPT in Higher Education: A Literature Review
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“Indeed, students ’ learning outcomes may be restricted if they are solely dependent upon ChatGPT for producing their written work and fail to engage in reflection and critical thinking (Firat, 2023; Rasul et al., 2023).”
BackgroundAhmed, Z., Shanto, S. S., Rime, M. H. K., Morol, M. K., Fahad, N., Hossen, M. J., & Abdullah-Al-Jubair, M. (2024) · IEEE Access · doi:10.1109/access.2024.3461874 · Cited by 123
Liang, J., Stephens, J. M., & Brown, G. T. L. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1522841 · Cited by 112
“Ethical concerns in instruction include incorrect or fabricated information, accessibility, and algorithm biases (Firat, 2023).”
InfluentialLavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., Komis, V., & Karacapilidis, N. I. (2024) · Information · doi:10.3390/info15060314 · Cited by 181
Nguyen, T. T. H. (2023) · International Journal of Language Instruction · doi:10.54855/ijli.23231 · Cited by 179
Salifu, I., Arthur, F., Arkorful, V., Nortey, S. A., & Osei-Yaw, R. S. (2024) · Cogent Social Sciences · doi:10.1080/23311886.2023.2300177 · Cited by 127
“…the advantages of ChatGPt, accounting for its rapid spread in higher education institutions (heis) (talan & Kalinkara, 2023). as a recent study by Firat (2023) reported, ChatGPt allows for online human interaction and offers quick feedback, support, and individualised learning opportunities.”
Cai, L., Msafiri, M. M., & Kangwa, D. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13112-0 · Cited by 104
Chen, Y., Li, Q., & Liu, J. (2024) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.337606 · Cited by 102
“5 in visual question answering tasks has become a popular topic of research (Firat, 2023).”
BackgroundDoes ChatGPT Help With Introductory Programming?An Experiment of Students Using ChatGPT in CS1
Xue, Y., Chen, H., Bai, G. R., Tairas, R., & Huang, Y. (2024) · Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training · doi:10.1145/3639474.3640076 · Cited by 96
Nikolic, S., Wentworth, I., Sheridan, L., Moss, S., Duursma, E., Jones, R. A., Ros, M., & Middleton, R. (2024) · Australasian Journal of Educational Technology · doi:10.14742/ajet.9643 · Cited by 102
“Most of these studies revealed somewhat or largely favourable attitudes towards the adoption of AI or GenAI in teaching (e.g., Alnasib, 2023; Barrett & Pack, 2023; Firat, 2023; Guo & Wang, 2023; Kiryakova & Angelova, 2023; Livberber & Ayvaz.”
Adapting to the Future: ChatGPT as a Means for Supporting Constructivist Learning Environments
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“Through-out the history of education, the emergence of new technologies has transformed the way students and educators interact with information and learning (Chen et al., 2023; Deng & Yu, 2023; Firat, 2023).”
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“Additionally, this cluster addresses students ’ attitudes towards generative AI and their willingness to use it, refereeing studies by Chan and Lee (2023), and Firat (2023).”
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“Moreover, AI in education, as an increasingly prominent feature (Bearman et al., 2023 ), not only develops administrative tasks but also redefines the educators’ roles (Bearman et al., 2023; Bozkurt, 2023; Firat, 2023 ).”
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García-López, I. M., González, C. S. G., Soledad, M., & Espinosa, J. M. M. (2024) · International Journal of Educational Research Open · doi:10.1016/j.ijedro.2024.100401 · Cited by 133
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Msambwa, M. M., Wen, Z., & Daniel, K. (2025) · European Journal of Education · doi:10.1111/ejed.12909 · Cited by 76
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“En [20], se analizan las implicaciones de ChatGPT en la educación superior, enfocándose en las percepciones de académicos y estudiantes.”
Saihi, A., Ben-Daya, M., Hariga, M., & As'ad, R. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100274 · Cited by 75
Use of AI-Based GPTs in Experimental, Empirical, and Exploratory Research Methods
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(2024) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2024.7.1.22 · Cited by 70
Araújo, J. L., & Saúde, I. (2024) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00745 · Cited by 92
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ChatGPT for Academic Purposes: Survey Among Undergraduate Healthcare Students in Malaysia
George Pallivathukal, R., Kyaw Soe, H. H., Donald, P. M., Samson, R. S., & Hj Ismail, A. R. (2024) · Cureus · doi:10.7759/cureus.53032 · Cited by 68
“Previous studies indicate that students are optimistic about the potential of ChatGPT to be used in universities [22,23].”
BackgroundArtificial Intelligence Generative Tools and Conceptual Knowledge in Problem Solving in Chemistry
Daher, W. M., Diab, H., & Rayan, A. (2023) · Information · doi:10.3390/info14070409 · Cited by 85
Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module
Ngo, T. N., & Hastie, D. (2025) · English for Specific Purposes · doi:10.1016/j.esp.2024.09.001 · Cited by 72
“…students have broadly positive attitudes and perceptions towards GenAI predicated on knowledge of the many educational affordances GenAI can offer (Chan & Zhou, 2023; Firat, 2023), and this base positivity appears to be common across different cultures and backgrounds (Yusuf et al., 2024).”
Exploring student and teacher perceptions of ChatGPT use in higher education: A Q-Methodology study
Espartinez, A. S. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100264 · Cited by 116
Daniel, K., Msambwa, M. M., & Wen, Z. (2025) · European Journal of Education · doi:10.1111/ejed.70036 · Cited by 71
Spasić, A. J., & Jankovic, D. S. (2023) · International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) · doi:10.1109/icest58410.2023.10187269 · Cited by 63
Large Language Models in Education: A Systematic Review
Dong, B., Bai, J., Xu, T., & Zhou, Y. (2024) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste62025.2024.00031 · Cited by 82
“Students can also use them to support learning activities [4], and this assistance has the potential to enhance learner engagement, satisfaction, and academic performance [5].”
BackgroundLiang, W., & Wu, Y. (2024) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2024.101645 · Cited by 105
Rethinking online assessment strategies: Authenticity versus AI chatbot intervention
Ifelebuegu, A. O. (2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.2.2 · Cited by 74
“Concerns exist regarding factual bias and information falsification by these chatbots (Dwivedi et al., 2023; Firat, 2023).”
BackgroundChatGPT and higher education assessments: More opportunities than concerns?
Gamage, K. A. A., Dehideniya, S. C. P., Xu, Z., & Tang, X. (2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.2.32 · Cited by 84
AlGhamdi, R. A. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12594-2 · Cited by 80
“However, it also reveals the necessity for human oversight to address the nuances and emotional aspects of feed-back reception, aligning with the views of Chan and Hu (2023) and Firat (2023) on the role of human elements in AI-assisted education.”
Chen, G., Fan, J., & Azam, M. (2024) · Journal of Librarianship and Information Science · doi:10.1177/09610006241269189 · Cited by 73
“…such as literature searches, quick access to research resources, answer scholarly questions, data cleaning, visualization, data interpretation, citation formatting, grant writing, peer review and more for scholars to focus on higher-level thinking and analysis (Firat, 2023; Shahet al., 2023).”
AbuSahyon, A. S. E., Alzyoud, A., AlShorman, O., & Al-Absi, B. (2023) · International Journal of Membrane Science and Technology · doi:10.15379/ijmst.v10i1.2829 · Cited by 102
“Additionally, the application of chatbots technology can give learners ongoing feedback and support, that make learning environment more interactive and immersive [102].”
BackgroundThe Good and Bad of AI Tools in Novice Programming Education
Zviel-Girshin, R. (2024) · Education Sciences · doi:10.3390/educsci14101089 · Cited by 55
ChatGPT for Teaching and Learning: An Experience from Data Science Education
Zheng, Y. (2023) · Conference on Information Technology Education · doi:10.1145/3585059.3611431 · Cited by 75
“Various functions by ChatGPT have been identified to assist and improve education [2, 5, 14, 16], such as personalized learning, concept understanding, coding generation and explanations, educational assessment, etc.”
BackgroundArtificial intelligence chatbots for the nutrition management of diabetes and the metabolic syndrome
Naja, F., Taktouk, M., Matbouli, D., Khaleel, S., Maher, A., Uzun, B., Alameddine, M., & Nasreddine, L. (2024) · European Journal of Clinical Nutrition · doi:10.1038/s41430-024-01476-y · Cited by 59
Xu, X., Su, Y., Zhang, Y., Wu, Y., & Xin-yu, X. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26239 · Cited by 86
“Firat ’ s [23] research, encompassing views from scholars and students in four countries, highlighted themes such as the evolution of educational systems and the need for research on AI ’ s ethical implications.”
BackgroundCong-Lem, N., Tran, T. N., & Nguyen, T. T. (2024) · Teaching English With Technology · doi:10.56297/fsyb3031/mxnb7567 · Cited by 64
Students’ use of generative artificial intelligence for proving mathematical statements
Yoon, H., Hwang, J. J., Lee, K. W., Roh, K. H., & Kwon, O. N. (2024) · ZDM · doi:10.1007/s11858-024-01629-0 · Cited by 54
“…that genAI tools such as ChatGPT have the potential to transform educational support by providing personalized and interactive learning environments that align with individual students’ preferences and skill levels (Baidoo-Anu & Ansah, 2023; Firat, 2023; Lo, 2023; Qadir, 2023; Su & Yang, 2023).”
Public perceptions towards ChatGPT as the Robo-Assistant
Jangjarat, K., Kraiwanit, T., Limna, P., & Sonsuphap, R. (2023) · Online Journal of Communication and Media Technologies · doi:10.30935/ojcmt/13366 · Cited by 63
Budhiraja, R., Joshi, I., Challa, J. S., Akolekar, H. D., & Kumar, D. (2024) · Proceedings of the 26th Australasian Computing Education Conference · doi:10.1145/3636243.3636257 · Cited by 52
Exploring the influence of ChatGPT on student academic success and career readiness
Dahri, N. A., Yahaya, N., & Al-Rahmi, W. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13148-2 · Cited by 69
“Firat (2023) explored the perceptions of scholars and students on the implications of ChatGPT for universities, revealing that while both groups saw potential benefits in terms of efficiency and accessibility, they also expressed concerns about academic integrity, the commodification of education,…”
Applications of Artificial Intelligence in Medical Education: A Systematic Review
Hallquist, E., Gupta, I., Montalbano, M. J., & Loukas, M. (2025) · Cureus · doi:10.7759/cureus.79878 · Cited by 58
An Evaluation of the Use of Artificial Intelligence Applications in Online Education
Tonbuloğlu, B. (2023) · Journal of Educational Technology and Online Learning · doi:10.31681/jetol.1335906 · Cited by 53
“Fırat (2023) emphasized that AI technologies have the potential to greatly improve the organization of online learning and investigated how AI technologies can be integrated into learning management systems.”
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Wang, C., Aguilar, S. J., Bankard, J. S., Bui, E., & Nye, B. (2024) · Education Sciences · doi:10.3390/educsci14090976 · Cited by 49
“The qualitative analysis of participants’ responses identified themes such as the changing role of educators, enabling personalized learning, and impact on assessment and evaluation [19].”
Gamifying Learning with AI: A Pathway to 21st-Century Skills
Niño, J. R. G., Arias-Delgado, L., Chiappe, A., & González, E. O. (2024) · Journal of Research in Childhood Education · doi:10.1080/02568543.2024.2421974 · Cited by 44
“As Firat (2023) observes, teachers are evolving from knowledge transmitters to learning facilitators.”
Enough of the chit-chat: A comparative analysis of four AI chatbots for calculus and statistics
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.2.22 · Cited by 50
Inteligencia artificial generativa en la educación superior: usos y opiniones de los profesores
Vega, J. E. P., Rodríguez, J. A. S., & Román-Méndez, A. D. (2024) · European Public & Social Innovation Review · doi:10.31637/epsir-2024-593 · Cited by 108
The role of generative AI in education: Perceptions of Saudi students
Aldossary, A., Aljindi, A. A., & Alamri, J. M. (2024) · Contemporary Educational Technology · doi:10.30935/cedtech/15496 · Cited by 70
Sánchez, F. Á. B., & Cruz, J. (2024) · Education Sciences · doi:10.3390/educsci14050484 · Cited by 59
“AI chatbots have the potential to enrich students’ learning experiences and improve their engagement and motivation [32–34].”
BackgroundInfluentialGenerative AI and Educators: Partnering in Using Open Digital Content for Transforming Education
van den Berg, G. (2024) · Open Praxis · doi:10.55982/openpraxis.16.2.640 · Cited by 61
Enhancing students’ learning experience in mathematics class through ChatGPT
Gouia‐Zarrad, R., & Gunn, C. L. (2024) · International Electronic Journal of Mathematics Education · doi:10.29333/iejme/14614 · Cited by 71
Evaluating generative AI integration in Saudi Arabian education: a mixed-methods study
Alammari, A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1879 · Cited by 65
“Furthermore, the rapid rise and widespread adoption of ChatGPT have motivated educators to closely evaluate this AI tool, as noted by Firat (2023) and Lampropoulos, Ferdig & Kaplan-Rakowski (2023) .”
MethodExploring students’ experiences and perceptions of human-AI collaboration in digital content making
Hwang, Y., & Lee, J. H. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00542-0 · Cited by 50
“With academic staff and PhD students from four countries, Firat (2023) explored their perceptions of ChatGPT and AI integration, using the survey as the instrument and thematic content analysis as the method of analysis.”
Artificial intelligence in higher education. A protocol paper for a systematic literature review
Ismail, F. M. M., Tan, E., Rudolph, J., Crawford, J., & Tan, S. (2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.2.34 · Cited by 39
Decoding dietary myths: The role of ChatGPT in modern nutrition
Arslan, S. (2024) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2024.02.022 · Cited by 35
Van Wyk, M. M. (2024) · Journal of Applied Learning and Teaching · doi:10.37074/jalt.2024.7.1.15 · Cited by 35
Kim, D., Majdara, A., & Olson, W. (2024) · International Journal of Technology in Education · doi:10.46328/ijte.691 · Cited by 34
“ChatGPT can benefit college students by helping them learn complex skills (Ranjbaran et al., 2023), providing personalized learning experiences (Firat, 2023; Farrokhnia et al., 2023), and assisting them with their test preparation and writing tasks (Ray, 2023; Elkhodr et al., 2023).”
Generative AI and the future of connectivist learning in higher education
Liang, E. S., & Bai, S. (2025) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2386085 · Cited by 34
The Potentials of ChatGPT for Language Learning: Unpacking its Benefits and Limitations
Nugroho, A., Putro, N. H. P. S., & Syamsi, K. (2023) · Register Journal · doi:10.18326/register.v16i2.224-247 · Cited by 68
“This concern is especially relevant in language education, given that students come from diverse cultural backgrounds and may not realize that ChatGPT and other AI-driven tools are not culturally neutral (Firat, 2023).”
BackgroundInfluentialCastillo-Segura, P., Alario‐Hoyos, C., Kloos, C. D., & Fernández-Panadero, C. (2023) · World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) · doi:10.1109/weef-gedc59520.2023.10344098 · Cited by 38
“One of the industries that is most likely to suffer from the transformation initiated by ChatGPT and generative AI is education [10].”
BackgroundEnhancing critical thinking skills in ChatGPT-human interaction: a scoping review
Raitskaya, L., & Tikhonova, E. (2025) · Journal of Language and Education · doi:10.17323/jle.2025.27387 · Cited by 30
ChatGPT and other AIs: Personal relief and limitations among mathematics-oriented learners
Remoto, J. P. (2023) · Environment and Social Psychology · doi:10.54517/esp.v9i1.1911 · Cited by 48
“The ChatGPT model has been identified as having certain concerns pertaining to the generation of misinformation, potential biases in its training data, and privacy-related considerations [18] .”
BackgroundBandyopadhyay, A., Oks, M., Sun, H., Prasad, B., Rusk, S., Jefferson, F., Malkani, R. G., Haghayegh, S., Sachdeva, R. C., Hwang, D., et al. (2024) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.11132 · Cited by 32
Evaluating the quality of responses generated by ChatGPT
Mandić, D., Miščević, G., & Bujišić, L. (2024) · Metodicka praksa · doi:10.5937/metpra27-51446 · Cited by 27
Köhler, C., & Hartig, J. (2024) · Contemporary Educational Technology · doi:10.30935/cedtech/15144 · Cited by 88
Zhang, Y., Lai, C., & Gu, M. M. (2025) · System · doi:10.1016/j.system.2025.103746 · Cited by 34
Examining students' attitudes and intentions towards using ChatGPT in higher education
Haq, M. Z. U., Cao, G., & Abukhait, R. M. Y. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13582 · Cited by 26
Emerging AI individualism: how young people integrate social AI into everyday life
Brandtzaeg, P. B., Følstad, A., & Skjuve, M. (2025) · Communication and Change · doi:10.1007/s44382-025-00011-2 · Cited by 30
A Systematic Literature Review of Empirical Research on ChatGPT in EducationPreprint
Albadarin, Y., Tukiainen, M., Saqr, M., & Pope, N. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4562771 · Cited by 26
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.
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