Cited by 1,080Artificial Intelligence in EducationTeaching and Learning ProgrammingArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index; none of the 9 sources its claims cite is checked for support yet.

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

Open access

Rahman, Md. Mostafizer, Watanobe, Yutaka·2023·Applied Sciences

DOI 10.3390/app13095783

Published version of a preprint

Open this paper in CoChat — free account Open PDF

Abstract

In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increased accessibility, interactive conversations, lesson preparation, evaluation, and new ways to teach complex concepts. However, ChatGPT poses different threats to the traditional education and research system, including the possibility of cheating on online exams, human-like text generation, diminished critical thinking skills, and difficulties in evaluating information generated by ChatGPT. This study explores the potential opportunities and threats that ChatGPT poses to overall education from the perspective of students and educators. Furthermore, for programming learning, we explore how ChatGPT helps students improve their programming skills. To demonstrate this, we conducted different coding-related experiments with ChatGPT, including code generation from problem descriptions, pseudocode generation of algorithms from texts, and code correction. The generated codes are validated with an online judge system to evaluate their accuracy. In addition, we conducted several surveys with students and teachers to find out how ChatGPT supports programming learning and teaching. Finally, we present the survey results and analysis.

AI snapshot

Written from the paper’s full text.

TL;DR

This paper examines ChatGPT’s opportunities and threats for education and research, with a focus on programming learning and teaching. It combines coding experiments on an online judge system with surveys of students and teachers, finding generally useful support but also accuracy, integrity, and over-reliance concerns.

Key findings

In code-generation experiments, ChatGPT’s generated code was correct about 95.83% of the time on a basic compiler and about 75% on the Aizu Online Judge (AOJ), with an average accuracy of 85.42%.
Among surveyed students, 78.8% had used ChatGPT for programming problems, 86.7% said its suggestions helped solve programming problems, and 92.9% found it useful for learning programming; most student satisfaction ratings were 4/5 (36.6%) or 5/5 (20%).
About 60% of surveyed teachers had used ChatGPT for teaching programming, but about 50% rated satisfaction as 3/5, suggesting limited trust; one teacher said ChatGPT “is still not perfect to answer some exams of my Java class.”
The paper identifies threats including cheating on online exams and assignments, diminished critical thinking, difficulty evaluating AI-generated text/code, ethical implications and bias, and blind reliance on generative AI; proposed strategies include clear assignment instructions, plagiarism detection, proctoring, comparing responses, and follow-up questions.

Methods

The authors conducted coding-related experiments with ChatGPT: code generation from problem descriptions, pseudocode generation, code correction/debugging, and code optimization.
For code generation, they used eight random “Algorithms and Data Structures” problems from AOJ, generated code three times per problem, and validated results on both a basic compiler and the AOJ platform.
They also conducted surveys with undergraduate, Master’s, and doctoral students and with teachers about ChatGPT’s support for programming learning and teaching; the paper reports percentages but not the total number of respondents.

Limitations

The paper states that experiments were conducted on ChatGPT during active development, so results may vary with new ChatGPT versions, different questions, different problem descriptions, and different solution codes for optimization.
The paper notes that although ChatGPT generated about 85.42% correct code on average, adjustments may still be necessary for error-free compilation and acceptance, and it failed an elementary counting task involving the digit “8.”
Evident from the text: the survey sample size is not reported, and the coding experiments are limited to eight AOJ problems with three generations each, which restricts generalizability.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/app13095783

Cites

71 of 71 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 9 cited sources checked · 0 supported · 0 flagged

Show all 33 claims

It may state two separate claims as one. 18
Probably not a substantial claim. 31

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 suggestions proved to be interesting to the programmers and helpful in resolving the code errors.”
§ ChatGPT for Education and Research: Opportunities, Threats, and Strategies > 4.3. Error Checking and Debugging in Code
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
- Rests on an assumption: The authors' assessment that ChatGPT's debugging suggestions were helpful reflects actual programmer benefit.
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
“it can be difficult to hold anyone accountable for the responses generated”
§ ChatGPT for Education and Research: Opportunities, Threats, and Strategies > Ethical implications and potential biases:
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Rests on an assumption: ChatGPT generates responses without human intervention.
In the paper
“Cotton et al. [57] presented several strategies for recognizing texts from LLMs such as ChatGPT, including language inconsistencies, a lack of proper citations, factual errors, ambiguity, and poor context awareness.”
§ ChatGPT for Education and Research: Opportunities, Threats, and Strategies > -Sam Altman, CEO of OpenAI
What the sources say
- Not checked yetcites [57] Cotton, D.R., Cotton, P.A., and Shipway, J.R. (2023). Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT. Innov. Educ. Teach. Int., 1–12. source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The answers and texts generated by ChatGPT are becoming indistinguishable from human-generated answers and texts.”
§ ChatGPT for Education and Research: Opportunities, Threats, and Strategies > -Sam Altman, CEO of OpenAI
What the sources say
- Not checked yetcites [57] Cotton, D.R., Cotton, P.A., and Shipway, J.R. (2023). Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT. Innov. Educ. Teach. Int., 1–12. source
- Not checked yetcites [60] Elkins, 2020, Can GPT-3 pass a Writer’s turing test?, J. Cult. Anal.
- Not checked yetcites [61] Gao, C.A., Howard, F.M., Markov, N.S., Dyer, E.C., Ramesh, S., Luo, Y., and Pearson, A.T. (2022). Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. bioRxiv. source
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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
“In short, ChatGPT threatens the fairness and validity of online exams and assignments.”
§ ChatGPT for Education and Research: Opportunities, Threats, and Strategies > 6. Threats and Strategies
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
- Rests on an assumption: ChatGPT can generate human-like text for academic topics.

40 of 40

The paper relies on this without support.
The eight randomly selected AOJ problems and three code generations per problem are representative of ChatGPT's general code-generation performance for programming learning.
“We leveraged the problem descriptions of eight random problems and generated codes three times for each problem on the basis of the same description.”
The paper relies on this without support.
The survey respondents are representative of students and teachers who use ChatGPT for programming learning and teaching.
“We conducted a comprehensive survey on how ChatGPT supports programming learning and teaching among undergraduate, Master's, and doctoral students and teachers.”
The paper relies on this without support.
Students' self-reported helpfulness of ChatGPT suggestions reflects actual improvement in solving programming problems.
“By asking the question “Do ChatGPT's suggestions help you in solving programming problems?”,we tried to assess how ChatGPT suggestions help students.We found that about 86.7% of the students answered “Yes”.”
The paper relies on this without support.
The authors' judgment that ChatGPT-generated explanations and pseudocode are easy to understand and useful reflects learners' actual understanding.
“The results demonstrate that ChatGPT can generate easy-to-understand explanations and pseudocode that are useful for learners to understand algorithmic concepts.”
The paper relies on this without support.
The authors' assessment that ChatGPT's debugging suggestions were helpful reflects actual programmer benefit.
“The suggestions proved to be interesting to the programmers and helpful in resolving the code errors.”
The paper relies on this without support.
ChatGPT can generate human-like text for academic topics.
“As ChatGPT can generate human-like text for academic topics”
The paper relies on this without support.
LLMs rely heavily on training data, and when the data contain biases or anomalies, it could lead to unfair results.
“Typically, LLMs rely heavily on training data, and when the data contain biases or anomalies, it could lead to unfair results.”
The paper relies on this without support.
The internal mechanisms and processes of ChatGPT are not sufficiently open and transparent to users about how they work.
“Furthermore, the internal mechanisms and processes are not sufficiently open and transparent to users about how they work.”
The paper relies on this without support.
ChatGPT generates responses without human intervention.
“Because ChatGPT generates responses without human intervention”

9 of 9

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?

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.

Table 1 names a different lead author for reference 25
Minor
Table 1 attributes reference 25 to Simon et al., while the reference list identifies Frieder as its lead author.

1 of 1

1 retracted

Retracted: Chat With ChatGPT on Intelligent Vehicles: An IEEE TIV Perspective
Du, H., Teng, S., Chen, H., Ma, J., Wang, X., Gou, C., Li, B., Ma, S., Miao, Q., Na, X., et al. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3253281 · Cited by 164 · cited 1×
“Moreover, ChatGPT has recently been applied to tasks in different domains such as software engineering [36], medical education [37], data augmentation [38], ﬁfth industrial revolution [39], code generation [40], intelligent vehicles [41], and solving AI tasks [42].”
Method
Retracted
Attention Is All You Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · cited 1×
“Therefore, the Transformer architecture was introduced [4] to alleviate this problem.”
Method
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×
“Generative pretrained transformer (GPT)-3 is a large language model (LLM) based on the Transformer architecture [5,6] that has achieved signiﬁcant success in NLP tasks.”
Background
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 3×
“Furthermore, several studies [9–12 Frieder et al. [25] tested the mathematical capabilities of ChatGPT on GHOSTS and handcrafted datasets.”
BackgroundMethod
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · cited 2×
“Finally, the resulting model is called InstructGPT [8].”
BackgroundMethod
Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education Using Large Language Models
Kung, T. H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., et al. (2022) · doi:10.1101/2022.12.19.22283643 · Cited by 4,115
Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · cited 1×
“Kung et al. [26] evaluated the performance of ChatGPT on the US Medical Licensing Exam, which includes three exams: Step 1, Step 2CK, and Step 3.”
Background
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087 · cited 3×
“Students can be given clear instructions on assignments and online exams on how to structure their assignments and answer their questions online [57].”
Background
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079

Flagged first, then by citations

Show all 58 references

Research works printed but not linked: 3

Rahman, M.M. (2022). Data Analysis and Code Assessment Using Machine Learning Techniques for Programming Activities. [Ph.D. Thesis, The University of Aizu].
Avila-Chauvet, L., Mejía, D., and Acosta Quiroz, C.O. (2023). Chatgpt as a Support Tool for Online Behavioral Task Programming. SSRN, Available online: https://ssrn.com/abstract=4329020.
Jiao, W., Wang, W., Huang, J.t., Wang, X., and Tu, Z. (2023). Is ChatGPT a good translator? A preliminary study. arXiv.

Sorted by citations

Items 201–300 of 1,004

Charting the Growth and Structure of Early ChatGPT-Education Research: A Bibliometric Study
Watrianthos, R., Ahmad, S. T., & Muskhir, M. (2023) · Journal of Information Technology Education Innovations in Practice · doi:10.28945/5221 · Cited by 47
“ChatGPT encourages self - directed learning by allowing students to independently discover and expand knowledge through open dialogues (Karakose, 2023; Rahman & Watanobe, 2023).”
Background
Making a revolution in physics learning in high schools with ChatGPT: A case study in UAE
Alarbi, K., Halaweh, M., Tairab, H., Alsalhi, N. R., Annamalai, N., & Aldarmaki, F. (2024) · Eurasia Journal of Mathematics, Science and Technology Education · doi:10.29333/ejmste/14983 · Cited by 28
“After the user has entered the prompt, ChatGPT will review it and produce an answer based on the text it operates on (Almusaed et al., 2023; Rahman & Watanobe, 2023).”
BackgroundMethod
Bard, ChatGPT and 3DGPT: a scientometric analysis of generative AI tools and assessment of implications for mechanical engineering education
Mustapha, K. B., Mustapha, K. B., Yap, E. H., & Abakr, Y. A. (2024) · Interactive Technology and Smart Education · doi:10.1108/itse-10-2023-0198 · Cited by 33
GenAI in the classroom: Customized GPT roleplay for process safety education
Honig, C. D. F., Desu, A., & Franklin, J. (2024) · Education for Chemical Engineers · doi:10.1016/j.ece.2024.09.001 · Cited by 27
Artificial intelligence in computer programming education: A systematic literature review
Manorat, P., Tuarob, S., & Pongpaichet, S. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100403 · Cited by 27
Unlocking Insights: Exploring Data Analytics and AI Tool Performance Across Industries
Mohapatra, H., & Mishra, S. R. (2024) · Studies in big data · doi:10.1007/978-981-97-0448-4_13 · Cited by 27
Experimental evidence for the efficacy of generative AI in improving students' writing skills
Ironsi, C. S., & Solomon Ironsi, S. (2025) · Quality Assurance in Education · doi:10.1108/qae-04-2024-0065 · Cited by 28
Reacting to Generative AI: Insights from Student and Faculty Discussions on Reddit
Wu, C., Wang, X., Carroll, J. M., & Rajtmajer, S. M. (2024) · ACM Web Science Conference (WebSci) · doi:10.1145/3614419.3644014 · Cited by 32
“These concerns are also prevalent in papers that rely on expert opinions and literature review [24, 49].”
Background
Generative AI in higher education: Perspectives of students, educators and administrators
Kutty, S., Chugh, R., Perera, P., Neupane, A., Jha, M., Li, L., Gunathilake, W., & Perera, N. C. (2024) · Journal of Applied Learning and Teaching · doi:10.37074/jalt.2024.7.2.27 · Cited by 45
Impact of generative AI dialogic feedback on different stages of programming problem solving
Gong, X., Li, Z., & Qiao, A. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13173-1 · Cited by 26
“In addition, GenAIDF can create explanations and step-by-step solutions for given problems, which develops students’ problem-solving skills and creative thinking (Rahman & Watanobe, 2023 ).”
Influential
Enhancing Accessibility in Software Engineering Projects with Large Language Models (LLMs)
Aljedaani, W., Eler, M. M., & Parthasarathy, P. D. (2025) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3641554.3701841 · Cited by 26
Integration of Artificial Intelligence (AI) into Primary School Students’ Writing Skills: The Impact of ChatGPT on Creative Writing and Writing Self-Efficacy
Kızıltaş, Y. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251365187 · Cited by 35
“In recent years, the advancement of arti ﬁ cial intelligence technologies has had a profound impact on many ﬁ elds, including education and research (Rahman & Watanobe, 2023).”
Influential
Harnessing the benefits of ChatGPT for radiography education: A discussion paper
Amedu, C., & Botwe, B. O. (2023) · Radiography · doi:10.1016/j.radi.2023.11.009 · Cited by 28
Does ChatGPT Help Novice Programmers Write Better Code? Results From Static Code Analysis
Haindl, P., & Weinberger, G. (2024) · IEEE Access · doi:10.1109/access.2024.3445432 · Cited by 45
AI chatbots: A disguised enemy for academic integrity?
Niloy, A. C., Hafiz, R., Hossain, B. M., Gulmeher, F., Sultana, N., Islam, K. F., Bushra, F., Islam, S., Hoque, S. I., Rahman, M., et al. (2024) · International Journal of Educational Research Open · doi:10.1016/j.ijedro.2024.100396 · Cited by 26
Self-citation
Exploring Factors Influencing Continuance Intention of Pre-Service Teachers in Using Generative Artificial Intelligence
Zheng, W., Ma, Z., Sun, J., Wu, Q., & Hu, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2433300 · Cited by 40
“Generative artificial intelligence (GAI) is a cutting-edge technology that leverages large language models and other advanced algorithms to generate content across various media, including text, code, images, audios and videos (Aydin & Karaarslan, 2023; Rahman & Watanobe, 2023).”
Do LLMs Have Visualization Literacy? An Evaluation on Modified Visualizations to Test Generalization in Data Interpretation
Hong, J., Seto, C., Fan, A., & Maciejewski, R. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3536358 · Cited by 25
“A S GENERATIVE Pretrained Transformers (GPT) and other Large Language Models (LLMs) continue to advance [35], their impact on the ﬁeld of visualization is becoming increasingly profound.”
Attitude Mining Toward Generative Artificial Intelligence in Education: The Challenges and Responses for Sustainable Development in Education
Wen, Y., Zhao, X., Li, X., & Zang, Y. (2025) · Sustainability · doi:10.3390/su17031127 · Cited by 30
Correcting Students' Misconceptions in Physics Using Experiments Designed by ChatGPT
Kotsis, K. Τ. (2024) · European Journal of Contemporary Education and E-Learning · doi:10.59324/ejceel.2024.2(2).07 · Cited by 25
“To optimise ChatGPT's potential influence on student learning, educators must get training on how to apply it in the classroom (Rahman & Watanobe, 2023).”
Background
Potentials and implications of ChatGPT for ESL writing instruction
Ibrahim, K., & Kirkpatrick, R. (2024) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v25i3.7820 · Cited by 28
“ChatGPT could also offer L2 educators rich L2 learning resources that support autonomous and collaborative L2 instruction and learning, including practice activities, grammar worksheets, and vocabulary illustration (Rahman & Watanobe, 2023).”
BackgroundMethodInfluential
An Investigation into the Utility of Large Language Models in Geotechnical Education and Problem Solving
Chen, L., Tophel, A., Hettiyadura, U., & Kodikara, J. K. (2024) · Geotechnics · doi:10.3390/geotechnics4020026 · Cited by 24
College Teachers’ Behavioral Intention to Adopt Artificial Intelligence-Assisted Teaching Systems
Zhang, W., & Hou, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3445909 · Cited by 33
“For instance, GPT can tailor learning content and assessments based on individual student needs, offering The associate editor coordinating the review of this manuscript and approving it for publication was Meriel Huggard . personalized educational experiences [6], [7].”
Background
Catalyst for future education: An empirical study on the Impact of artificial intelligence generated content on college students’ innovation ability and autonomous learning
Wang, D., Liu, Y., Jing, X., Liu, Q., & Lu, Q. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13209-6 · Cited by 36
“In addition, although the sample selection is non-probabilistic, it is thought to be representative of a technically skilled student population that is more likely to use AIGC tools in daily learning and academic activities (Rahman and Watanobe, 2023).”
Integration of ChatGPT in mathematical story-focused 5E lesson planning: Teachers and pre-service teachers' interactions with ChatGPT
ŞİMŞEK, N. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13258-x · Cited by 37
Facilitating pre-service teachers’ instructional design and higher-order thinking with generative AI: an integrated approach with the peer assessment and concept map
Liu, C., Wang, D., Gu, X., Hwang, G., Tu, Y., & Wang, Y. (2025) · Journal of Research on Technology in Education · doi:10.1080/15391523.2025.2474528 · Cited by 33
“This finding aligns with research emphasizing the lack of personalization in ChatGPT-generated content (Rahman & Watanobe, 2023).”
Effects of AI chatbots on EFL students’ critical thinking skills and intrinsic motivation in argumentative writing
Zhang, Q., Siraj, S. B., & Razak, R. A. (2025) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2025.2515111 · Cited by 39
“Furthermore, by guiding students to consider multiple perspectives and offering structural support in argument development, AI chatbots help students construct more coherent and persuasive arguments (Rahman & Watanobe, 2023).”
Adopting ChatGPT to Enhance Educational Experiences
Murad, I. A., Surameery, N. M. S., & Shakor, M. Y. (2023) · International Journal of Information technology and Computer Engineering · doi:10.55529/ijitc.35.20.25 · Cited by 34
Transforming Education and Learning through Chat GPT: A Systematic Literature Review
Prananta, A. W., Ruth Rize Paas Megahati, S., Susanto, N., & Raule, J. H. (2023) · Jurnal Penelitian Pendidikan IPA · doi:10.29303/jppipa.v9i11.5468 · Cited by 40
“…(Haluza & Jungwirth, 2023); (Ivanov & Soliman, 2023); (Kanbach et al., 2023); (Surameery & Shakor, 2023) Customer Support (Baskara, 2023); (Rahman & Watanobe, 2023); (Tlili et al., 2023); (Paul et al., 2016) Learning and Experimentation Using GPT Chat has several benefits, including…”
Examining the relationship between the L2 motivational self system and technology acceptance model post ChatGPT introduction and utilization
Huang, J., & Mizumoto, A. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100302 · Cited by 42
Exercise Generation and Student Cognitive Ability Research Based on ChatGPT and Rasch Model
Niu, Y., & Xue, H. (2023) · IEEE Access · doi:10.1109/access.2023.3325741 · Cited by 23
“Immediate efforts should be made to address these disadvantages to ensure fairness for all [4].”
Background
Investigating students' behavioral intention to use ChatGPT for educational purposes
Sun, P., Li, L., Hossain, M. S., & Zabin, S. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.100531 · Cited by 23
ChatGPT’s Impact on Education and Healthcare: Insights, Challenges, and Ethical Consideration
Hamad, Z. T., Jamil, N., & Belkacem, A. N. (2024) · IEEE Access · doi:10.1109/access.2024.3437374 · Cited by 27
“It also presents researchers with various research options, notably regarding individualized learning support [88].”
Background
Application of large language models in engineering education: A case study of system modeling and simulation courses
Liu, C., & Yang, S. (2024) · International Journal of Mechanical Engineering Education · doi:10.1177/03064190241272728 · Cited by 22
“4 The application of ChatGPT in programming education and the development of thinking skills shows signi ﬁ cant potential.”
Navigating the New Frontier: Recommendations to Address the Crisis and Potential of AI in the Classroom
Slade, J. J., Byers, S. M., Becker‐Blease, K. A., & Gurung, R. A. R. (2024) · Teaching of Psychology · doi:10.1177/00986283241276098 · Cited by 37
“Student AI use is recognized to have great positive potential, with LLMs able to serve as learning tutors capable of providing immediate feedback (Rahman & Watanobe, 2023).”
Effects of Large Language Model–Based Offerings on the Well-Being of Students: Qualitative Study
Selim, R., Basu, A., Anto, A., Foscht, T., & Eisingerich, A. B. (2024) · JMIR Formative Research · doi:10.2196/64081 · Cited by 27
“The extant literature has explored functional and cognitive aspects of user-AI interaction [9,47,48], but the emotional and psychological components are less explored, and critical questions remain about the effects of the use of LLM-based offerings, such as ChatGPT, on users’ mental well-being.”
How do pre-service language teachers perceive generative AIs’ affordance?:A case of ChatGPT
Butler, Y. G., & Jiang, S. (2025) · System · doi:10.1016/j.system.2025.103606 · Cited by 34
Application of ChatGPT-Based Digital Human in Animation Creation
Lan, C., Wang, Y., Wang, C., Song, S., & Gong, Z. (2023) · Future Internet · doi:10.3390/fi15090300 · Cited by 32
ChatGPT in early childhood STEM education: Can it be an innovative tool to overcome challenges?
Uğraş, H., & Uğraş, M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12960-0 · Cited by 23
“This opportunity can contribute to the dissemination of education by providing students with access to opportunities to learn STEM-related topics regardless of time and place (Rahman & Watanobe, 2023).”
Enhancing Statistical Education in Chemistry and STEAM Using JAMOVI. Part 1: Descriptive Statistics and Comparing Independent Groups
de Souza, R. S., Sequeira, C. A., & Borges, E. M. (2024) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c00563 · Cited by 22
Proof of Concept of ChatGPT as a Virtual Tutor
Cain, J. J., & Rajan, A. S. (2024) · American Journal of Pharmaceutical Education · doi:10.1016/j.ajpe.2024.101333 · Cited by 27
Artificial Intelligence Chatbots as Virtual Patients in Dental Education: A Constructivist Approach to Classroom Implementation
Jones, B., Desu, A., & Honig, C. D. F. (2026) · European Journal of Dental Education · doi:10.1111/eje.13135 · Cited by 21
“Much of the literature has explored opportunities and risks associated with GenAI, particularly concerning academic integrity within tertiary education [2, 3, 5, 8, 9].”
Exploring researchers’ artificial intelligence (AI) literacy: The mediating role of digital literacy and data literacy between 21st century skills and AI literacy
Seker, O., Kwon, K., & Kocak, O. (2025) · Information Development · doi:10.1177/02666669251336368 · Cited by 21
Automated Test Creation Using Large Language Models: A Practical Application
Hadzhikoleva, S., Rachovski, T., Ivanov, I., Hadzhikolev, E. N., & Dimitrov, G. P. (2024) · Applied Sciences · doi:10.3390/app14199125 · Cited by 26
“The lack of critical thinking skills and difficulties in evaluating information generated by ChatGPT are other issues [34].”
The desire path: unleashing expectations, discussing apprehensions, and proposing a way forward for GAI use in higher education
Koroleva, D. O., & Jogezai, N. A. (2024) · Information and Learning Sciences · doi:10.1108/ils-10-2023-0137 · Cited by 27
Transforming Education with ChatGPT: Advancing Personalized Learning, Accessibility, and Ethical AI Integration
Asy’ari, M., & Шаров, С. (2024) · International Journal of Essential Competencies in Education · doi:10.36312/ijece.v3i2.2424 · Cited by 38
“For instance, frameworks for ethical AI deployment emphasize transparency and accountability in AI-generated content, ensuring that these technologies complement human teaching efforts without undermining them (Rahman & Watanobe, 2023).”
Engineering students' perceptions and actual use of AI-based math tools for solving mathematical problems
Garcia, K. F., Ong, A. K. S., Gumasing, M. J. J., & Reyes, C. R. V. D. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105004 · Cited by 31
The Automation Trap
Karamuk, E. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0122-8.ch007 · Cited by 26
Conceptualizing pre-service teachers’ artificial intelligence readiness and examining its relationship with various variables: The role of artificial intelligence literacy, digital citizenship, artificial intelligence-enhanced innovation and perceived threats from artificial intelligence
Özüdoğru, G., & Durak, H. Y. (2025) · Information Development · doi:10.1177/02666669251335657 · Cited by 40
“AI technologies threaten the validity and integrity of online exams and assignments that have become common in higher education (Rahman and Watanobe, 2023).”
Whether and When Could Generative AI Improve College Student Learning Engagement?
Guo, F., Zhang, L., Shi, T., & Coates, H. B. (2025) · Behavioral Sciences · doi:10.3390/bs15081011 · Cited by 21
“GenAI, in its current state, should be viewed not as a pedagogical or learning replacement, but as a contingent supplement (Rahman & Watanobe, 2023)—its value residing not in its standalone capacity, but in how well it is integrated within human-centered educational systems.”
Generative AI chatbots in higher education: Student experiences and perceived ethical challenges
Hadinejad, N., Sperling, K., & McGrath, C. (2025) · Computers and Education Open · doi:10.1016/j.caeo.2025.100311 · Cited by 22
A Testing Framework for AI Linguistic Systems (testFAILS)
Kumar, Y., Morreale, P. A., Sorial, P., Delgado, J., Li, J. J., & Martins, P. (2023) · Electronics · doi:10.3390/electronics12143095 · Cited by 19
How ChatGPT shapes the future labour market situation of software engineers: A Finnish Delphi study
Komp, K. (2024) · Futures · doi:10.1016/j.futures.2024.103382 · Cited by 34
Academic Evolution in the Age of ChatGPT: An In-depth Qualitative Exploration of its Influence on Research, Learning, and Ethics in Higher Education
Isiaku, L., Kwala, A. F., Sambo, K. U., & Isiaku, H. H. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/7egat807 · Cited by 21
“It empowers students by facilitating access to information and fostering personalised interactions, feedback, and support tailored to their individual abilities (Rahman & Watanobe, 2023).”
BackgroundMethodInfluential
Effects of ChatGPT on Korean EFL Learners’ Main-Idea Reading Comprehension via Top-Down Processing
Kim, R. (2024) · 어학연구 · doi:10.30961/lr.2024.60.1.83 · Cited by 34
“…empirical studies have highlighted the pedagogical potential of artificial intelligence (AI) within English as a Foreign Language (EFL) or English as a Second Language (ESL) context (Bengio et al., 2003; Cho et al., 2014; Lo, 2023; Rahman & Watanobe, 2023; Tlili et al., 2023; Vaswani et al., 2017).”
Análisis cuantitativo de la percepción del uso de inteligencia artificial ChatGPT en la enseñanza y aprendizaje de estudiantes de pregrado del caribe colombiano
Solano-Barliza, A. D., Ojeda, A. D., & Aarón-Gonzalvez, M. (2024) · Formación universitaria · doi:10.4067/s0718-50062024000300129 · Cited by 21
The Role of AI in Transforming Assessment Practices in Education
Vetrivel, S. C., Vidhyapriya, P., & Arun, V. P. (2024) · Advances in educational marketing, administration, and leadership book series · doi:10.4018/979-8-3693-5443-8.ch003 · Cited by 24
Teaching English and Artificial Intelligence: EFL Teachers’ Perceptions and Use of ChatGPTPreprint
Dilzhan, B. (2024) · doi:10.35542/osf.io/fwy92 · Cited by 27
ChatGPT and Cybersecurity: Risk Knocking the Door
Elsadig, M. A. (2023) · Journal of Internet Services and Information Security · doi:10.58346/jisis.2024.i1.001 · Cited by 27
“In other words, it aids code generation (C. Liu et al., 2023; Rahman & Watanobe, 2023).”
Developing scientific entrepreneurship and complex thinking skills: creating narrative scripts using ChatGPT
George-Reyes, C. E., Vilhunen, E., Martínez, R. A., & López-Caudana, E. O. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1378564 · Cited by 28
A corpus-driven comparative analysis of AI in academic discourse: Investigating ChatGPT-generated academic texts in social sciences
Tudino, G., & Qin, Y. (2024) · Lingua · doi:10.1016/j.lingua.2024.103838 · Cited by 32
Understanding Student Perception Regarding The Use of ChatGPT in Their Argumentative Writing: A Qualitative Inquiry
Esmaeil, A. A. A. H., Dzulkifli, D. N. A. K., Maakip, I., Mantaluk, O. O., & Marshall, S. (2023) · Jurnal Komunikasi Malaysian Journal of Communication · doi:10.17576/jkmjc-2023-3904-08 · Cited by 39
Harnessing large language models to auto-evaluate the student project reports
Du, H., Jia, Q., Gehringer, E. F., & Wang, X. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100268 · Cited by 25
Promoting active learning with ChatGPT: A constructivist approach in Sri Lankan higher education
(2024) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2024.7.2.26 · Cited by 30
A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model
Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22
Generative or Degenerative?! Implications of AI Tools in Pre-Service Teacher Education and Reflections on Instructors’ Professional Development
Estaiteyeh, M., & McQuirter, R. (2024) · Brock Education Journal · doi:10.26522/brocked.v33i3.1176 · Cited by 28
“Rahman and Watanobe (2023) maintain that ChatGPT can serve students as a virtual tutor, helping them by providing practice questions and study schedules.”
The effect of university students' self-efficacy on problem-solving disposition: The chained dual mediating role of metacognition disposition and critical thinking disposition
Wang, X.-M., Huang, X.-T., & Zhou, W.-Q. (2024) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2024.101658 · Cited by 34
A Hybrid SEM-ANN Approach for Predicting the Impact of Psychological Needs on Satisfaction with Generative AI Use
Arpacı, İ., & Kuşci, İ. (2025) · Technology Knowledge and Learning · doi:10.1007/s10758-025-09817-x · Cited by 23
“ChatGPT, renowned for generating outputs based on user inputs (Rahman & Watanobe, 2023), can serve as a valuable asset in higher education, aiding in enhanced writing through text generation, summarizing facts, and crafting outlines, and thereby, enhancing work quality and saving time.”
The ethical security of large language models: A systematic review
Liu, F., Jiang, J., Lu, Y., Huang, Z., & Jiang, J. (2025) · Frontiers of Engineering Management · doi:10.1007/s42524-025-4082-6 · Cited by 23
“This may foster excessive reliance on LLMs, potentially impairing students’ critical thinking and problem-solving skills and resulting in rigid thinking patterns and a lack of creativity (Agathokleous et al., 2023; Rahman and Watanobe, 2023).”
The Challenges of Applying ChatGPT in the Academic Writing of Postgraduate Students in English Major at IUH
Nguyen, T. Y. P., Nguyen, N. T., & Phan, N. K. H. (2025) · International journal of AI in language education. · doi:10.54855/ijaile.25212 · Cited by 25
Affordances, constraints, and implications of ChatGPT in education from a social-ecological perspective: A data mining approach
Zhong, Y., Lian, J., Huang, H., & Deng, H. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13237-2 · Cited by 32
“For instance, Rahman and Watanobe (2023) pointed out that ChatGPT’s effectiveness in education stemmed from its various pivotal features: improving literacy skills by offering syntactic and grammatical guidance, designing exercises and quizzes across a range of topics including mathematics and…”
Systematic exploration and in-depth analysis of ChatGPT architectures progression
Banik, D., Pati, N., & Sharma, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10832-0 · Cited by 24
“…al. 2023), finds applications (Abdul-lah et al. 2022) across numerous domains such as customer support, education (Adesh-ola and Adepoju 2023), healthcare (Biswas 2023), content creation, language translation, research (Rahman and Watanobe 2023), virtual assistants, and legal and compliance, etc.”
What an AI-Embracing Software Engineering Curriculum Should Look Like: An Empirical Study
Randall, N., Wäckerle, D., Stein, N., Goßler, D., & Bente, S. (2024) · IEEE Software · doi:10.1109/ms.2023.3344682 · Cited by 18
“5 Because of the newness of accessible generative AI technologies, experiments incorporating students have only utilized basic AI recommender systems.”
Background
ChatGPT and generation 'Z': a study on the usage rates of ChatGPT
Babu, M. A., Yusuf, K. M., Eni, L. N., Jaman, S. M. S., & Sharmin, M. R. (2024) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2024.101163 · Cited by 19
ChatGPT as a Digital Tool in the Transformation of Digital Teaching Competence: A Systematic Review
Cerero, J. F., Rueda, M. M., Graván, P. R., & Fernández‐Batanero, J. M. (2025) · Technologies · doi:10.3390/technologies13050205 · Cited by 25
“These advantages have prompted studies exploring how ChatGPT can improve student productivity and performance [58].”
Exploring the Potential of Generative AI in English Language Teaching
Ironsi, C. S. (2023) · Advances in higher education and professional development book series · doi:10.4018/979-8-3693-0487-7.ch007 · Cited by 24
AI-based feedback tools in education: A comprehensive bibliometric analysis study
Donmez, M. (2024) · International Journal of Assessment Tools in Education · doi:10.21449/ijate.1467476 · Cited by 33
Unleashing the power of ChatGPT in finance research: opportunities and challenges
Feng, Z., Hu, G., Li, B., & Wang, J. (2025) · Financial Innovation · doi:10.1186/s40854-025-00770-3 · Cited by 40
“…technologies, such as ChatGPT (Chatbot-based Generative Pre-trained Transformer), has facilitated innovation across diverse sectors (e.g., Lin 2023; Rahman and Watanobe 2023) in the field of finance being no exception (e.g., Alshater 2022; Dowling and Lucey 2023; Zaremba and Demir 2023; Ali and…”
Modeling Chinese EFL learners’ intention to use generative AI for L2 writing through an integrated model of the TAM and TTF
Xing, H., & Wen, G. J. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13505-9 · Cited by 25
“The most used AI tool among the participants was ChatGPT, including third-party applications (56.6%), followed by Ernie Bot (30.6”
Influential
The use of artificial intelligence (AI) in research: a review of author guidelines in leading journals across eight social science disciplines
Goyanes, M., Lopezosa, C., & Piñeiro-Naval, V. (2025) · Scientometrics · doi:10.1007/s11192-025-05377-0 · Cited by 22
“Furthermore, they play a significant role in refining style, and improving the clarity of texts (Duan et al., 2019; Rahman & Watanobe, 2023).”
“ChatGPT and Digital Inequality: A Rising Concern”
Khan, I. A., & Paliwal, N. W. (2023) · Scholars Journal of Applied Medical Sciences · doi:10.36347/sjams.2023.v11i09.010 · Cited by 17
“Thus, ChatGPT has brought forward new challenges and threats to education [7].”
Using an Artificial-Intelligence-Generated Program for Positive Efficiency in Filmmaking Education: Insights from Experts and Students
Yang, W., Lee, H., Wu, R., Zhang, R., & Pan, Y. (2023) · Electronics · doi:10.3390/electronics12234813 · Cited by 26
An Empirical Study on How Large Language Models Impact Software Testing Learning
Mezzaro, S., Gambi, A., & Fraser, G. (2024) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3661167.3661273 · Cited by 17
“Such tools can support learners and educators by providing immediate explanations on unclear topics and generating exercises and other lecture materials, possibly tailor-made to students’ needs [12].”
Background
Generative AI in K-12: Opportunities for Learning and Utility for Teachers
Laak, K.-J., & Aru, J. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-64315-6_49 · Cited by 17
“This dissertation intricately explores…”: ChatGPT's shell noun use in rephrasing dissertation abstracts
Huang, L., & Deng, J. (2024) · System · doi:10.1016/j.system.2024.103578 · Cited by 16
The effect of generative AI use on doctoral students’ academic research progress: the moderating role of hedonic gratification
Ringo, D. S. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2475268 · Cited by 18
“Additionally, studies by Fauzi et al. (2023), Rahman and Watanobe (2023) and Javaid et al. (2023) demonstrate that ChatGPT can assist students in organizing their research more effectively.”
ChatGPT for Operating Systems: Higher-Order Thinking in Focus
Kofahi, M. A., & Husain, A. (2025) · Journal of Information Technology Education Research · doi:10.28945/5382 · Cited by 16
“S everal limitations to using ChatGPT were observed, including potential biases, the inability to process visual information, privacy and security issues, and misuse of ChatGPT by obtaining ready solutions without understanding (Fiialka et al., 2023; Rahman & Watanobe, 2023).”
Students’ perceptions about the opportunities and challenges of ChatGPT in higher education: a cross-sectional survey based in China
Cao, X., Lin, Y.-J., Zhang, J.-H., Tang, Y.-P., Zhang, M.-P., & Gao, H.-Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13339-5 · Cited by 22
“The ChatGPT model can be used as a professional tutor for developing language skills, programming, report writing, project management, and technical (e.g., medical, legal, and IT) report writing (Rahman & Watanobe, 2023).”
Influential
Leveraging ChatGPT to Support Self-Regulated Learning in Online Courses
Zhu, M. (2025) · TechTrends · doi:10.1007/s11528-025-01075-z · Cited by 23
“…Yilmaz & Yilmaz, 2023a, b), impeding learning (Husain, 2024; Kim & Adlof, 2024; Kosar et al., 2024), data privacy (Hsu & Ching, 2023; Husain, 2024; Rahman & Watanobe, 2023), biases (Hsu & Ching, 2023; Kasneci et al., 2023; Warr et al., 2023) and copyright concerns (Hsu & Ching, 2023; Humble et…”
ChatGPT and AI Chatbots in Education: An Umbrella Review of Systematic Reviews, Scoping Reviews, and Meta-Analyses
Milakis, E. D., Argyrakou, C. C., Melidis, A., & Vrettaros, J. (2025) · International Journal of Education and Information Technologies · doi:10.46300/9109.2025.19.11 · Cited by 28
Factors influencing intention to adopt an AI chatbot for learning in higher education: An integrated PLS-SEM, IPMA, and ANN approach
Guo, H., & Erdenebold, T. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100477 · Cited by 24
A systematic review of the impact of GenAI on learning performance, AI hallucinations, and problem-solving in computer science education
Adejumo, A. A., Oyelere, S. S., Sanusi, I. T., & Suhonen, J. T. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100570 · Cited by 24
Text mining applied to distance higher education: A systematic literature review
Takaki, P., & Dutra, M. L. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12235-0 · Cited by 16
“Despite this, Large Language Models (LLMs) have many applications in higher education (Rahman & Watanobe, 2023) and have great potential for further development.”
Constructing a teacher portrait for the artificial intelligence age based on the micro ecological system theory: A systematic review
Hu, X., Sui, H., Geng, X., & Zhao, L. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12513-5 · Cited by 24
“Rahman and Watanobe (2023) pointed out that educators can use ChatGPT for effective teaching and research, such as creating a course plan for a specific course, providing personalized learning support for students, and evaluating students’ homework and tests by using models, but teachers need to be…”
Medical knowledge of ChatGPT in public health, infectious diseases, COVID-19 pandemic, and vaccines: multiple choice questions examination based performance
Meo, S. A., Alotaibi, M. S., Meo, M. Z. S., Meo, M. O. S., & Hamid, M. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1360597 · Cited by 15
The use of ChatGPT to find similar institutions for institutional benchmarking
Bornmann, L., & Lepori, B. (2024) · Scientometrics · doi:10.1007/s11192-024-05039-7 · Cited by 16
“…studies has been the object of several studies in the informetric community (see Farhat et al., 2024; Lund & Wang, 2023; Ray, 2023; for a review) and a debate has emerged on its advantages and problems (Gordijn & ten Have, 2023; Korzynski et al., 2023; Rahman & Watanobe, 2023; Sandnes, 2024).”
The use of ChatGPT to generate experimentally testable hypotheses for improving the surface passivation of perovskite solar cells
Chen, C., Maqsood, A., Zhang, Z., Wang, X., Duan, L., Wang, H., Chen, T., Liu, S., Li, Q., Luo, J., et al. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102058 · Cited by 17
Advanced large language models and visualization tools for data analytics learning
Valverde-Rebaza, J., González, A., Navarro-Hinojosa, O., & Noguez, J. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1418006 · Cited by 22
“Similar concerns, and additional recommendations, have been raised after the introduction of ChatGPT (Baidoo-Anu and Ansah, 2023; Chinonso et al., 2023; Kasneci et al., 2023; Rahman and Watanobe, 2023).”
Background
StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions
Chen, Z., Wang, J., Xia, M., Shigyo, K., Liu, D., Zhang, R., & Qu, H. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456363 · Cited by 37
“Existing research on LLMs in education primarily focuses on educational ethics and potential application scenarios, such as using LLMs as automated tools for evaluating student essays [10,38,39,55,59].”
Method
Technological Innovation in Architectural Design Education: Empirical Analysis and Future Directions of Midjourney Intelligent Drawing Software
Xu, C. Q., & Huang, Y. (2024) · Buildings · doi:10.3390/buildings14103288 · Cited by 19
Exploring the AI–Human Interface for Personalized Learning in a Chemical Context
Clark, T. M., & Tafini, N. (2024) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c00967 · Cited by 16

Authors: Rahman, M. M., & Watanobe, Y.
Affiliations: University of Aizu (JP)
Corresponding author: Rahman, Md. Mostafizer
ORCID: Rahman, Md. Mostafizer
Watanobe, Yutaka
Venue: Applied Sciences
Publisher: MDPI AG
Published: 2023-05-08
Volume: 13
Issue: 9
Pages: 5783
ISSN: 2076-3417 (ISSN-L)
DOI: 10.3390/app13095783
OpenAlex: W4381982883
Semantic Scholar: 3b69ec0fc17c5134c9b002bebe283e0189bfdae5
Cited by: 1,080
References: 58
Citations by year: 2026: 193 · 2025: 338 · 2024: 425 · 2023: 124
FWCI: 253.56
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Applied Sciences (publishedVersion, cc-by)
Preprints.org (acceptedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Japan Society for the Promotion of Science (JSPS) KAKENHI (23H03508)
Topics: Social Sciences › Education › Artificial Intelligence in Education
Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: ChatGPT, large language models, artificial intelligence in education, academic dishonesty, critical thinking, programming education, code generation, code correction, student and teacher perceptions

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 ›

Are you an author, or do you see a problem with this page? Request a review

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

Abstract

Overview

AI snapshot

TL;DR

Key findings

Methods

Limitations

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks