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

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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

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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.

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Oral, M., Alboga, Ö., Aydınlı, S., & Erdiş, E. (2025) · Engineering Construction & Architectural Management · doi:10.1108/ecam-08-2024-1143 · Cited by 15
Enhancing Programming Learning with LLMs: Prompt Engineering and Flipped Interaction
Cowan, B., Watanobe, Y., & Shirafuji, A. (2023) · Asia Service Sciences and Software Engineering Conference (ASSE) · doi:10.1145/3634814.3634816 · Cited by 17
Self-citation
Acceptance and Use of ChatGPT Among Accounting and Finance Higher Education Students
Ziemba, E. W., Maruszewska, E. W., Grabara, D., & Renik, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-66761-9_16 · Cited by 17
Unraveling the Impact of ChatGPT as a Knowledge Anchor in Business Education
George, A., Storey, V. C., & Hong, S. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3705734 · Cited by 17
Subject-Specialized Chatbot in Higher Education as a Tutor for Autonomous Exam Preparation: Analysis of the Impact on Academic Performance and Students’ Perception of Its Usefulness
Vera, F. S. (2024) · Education Sciences · doi:10.3390/educsci15010026 · Cited by 22
Generative AI as a Coach to Help Students Enhance Proficiency in Question Formulation
Kim, P., Wang, W., & Bonk, C. J. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251314222 · Cited by 14
“Generative AI presents educators with many new opportunities as well as challenges in transforming teaching and learning as we know it (Adeshola & Adepoju, 2023; Baidoo-Anu & Ansah, 2023; Chan & Hu, 2023; Lo, 2023; Meyer et al., 2023; Rahman & Watanobe, 2023; Yu & Guo, 2023).”
An Effectiveness Study of Generative Artificial Intelligence Tools Used to Develop Multiple-Choice Test Items
May, T. A., Fan, Y., Stone, G. E., Koskey, K. L. K., Sondergeld, C. J., Folger, T. D., Archer, J. N., Provinzano, K., & Johnson, C. C. (2025) · Education Sciences · doi:10.3390/educsci15020144 · Cited by 12
Can Generative AI and ChatGPT Break Human Supremacy in Mathematics and Reshape Competence in Cognitive-Demanding Problem-Solving Tasks?
Kaya, D., & Yavuz, S. (2025) · Journal of Intelligence · doi:10.3390/jintelligence13040043 · Cited by 22
“Research suggests that ChatGPT can enhance student learning experiences by explaining abstract concepts, reducing cognitive load, and adapting to individual proficiency levels (Rahman and Watanobe 2023).”
Influential
Exploring the Role of LLMs Like ChatGPT in Pharmacy Education for Supporting Students' Therapeutic Decision-making
Carou‐Senra, P., Delgado-Taboada, I., Alvarez‐Lorenzo, C., Díaz‐Rodríguez, P., & Goyanes, Á. (2025) · American Journal of Pharmaceutical Education · doi:10.1016/j.ajpe.2025.101462 · Cited by 12
Transforming Online Learning Management: Generative Models on ChatGPT to Enhance Online Student Engagement Scale (OLE)
Yakin, A. M. A., Muthmainnah, M., Apriani, E., Obaid, A. J., & Elngar, A. A. (2023) · Idarah (Jurnal Pendidikan dan Kependidikan) · doi:10.47766/idarah.v7i2.1514 · Cited by 25
“Chatbot ChatGPT, an artificial intelligence chat tool, is ready to answer any questions, similar to Rahman and Watanobe's research (Rahman & Watanobe, 2023).”
Method
A pedagogical approach: toward leveraging mathematical modeling and AI to support integrating humanities into STEM education
Clark, Q. M. (2025) · Frontiers in Education · doi:10.3389/feduc.2024.1396104 · Cited by 13
The scientific knowledge of three large language models in cardiology: multiple-choice questions examination-based performance
Altamimi, I., Alhumimidi, A. S., Alshehri, S., AlRumayan, A. F., Al-khlaiwi, T., Meo, S. A., & Temsah, M.-H. A. (2024) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000002120 · Cited by 11
“However, their integration into traditional educational and research frameworks also poses challenges, particularly concerning the potential erosion of critical thinking skills [7,8] .”
Background
Automated Evaluation Techniques and AI-Enhanced Methods
Ahmed, S., Zaki, A., & Bentley, Y. N. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2145-4.ch001 · Cited by 12
Towards Integrating Emerging AI Applications in SE Education
Vierhauser, M., Groher, I., Antensteiner, T., & Sauerwein, C. (2024) · IEEE International Conference on Software Engineering Education and Training (CSEE&T) · doi:10.1109/cseet62301.2024.10663045 · Cited by 11
“Solutions to programming exercises are automatically generated by students using, for example, ChatGPT and pose further challenges for educators when conducting online exams [3].”
Background
Effects of Perceived Ease of Use and Perceived Usefulness of Technology Acceptance Model on Intention to Continue Using Generative AI: Focusing on the Mediating Effect of Satisfaction and Moderating Effect of Innovation Resistance
Jeong, S., Kim, S., & Lee, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-75599-6_7 · Cited by 11
Towards informed policy decisions: assessing student perceptions and intentions to use ChatGPT for academic performance in higher education
Hussain, F., & Anwar, M. A. (2024) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2422917 · Cited by 12
“Despite these challenges, studies have emphasized the potential benefits of ChatGPT in higher education, including personalized feedback, improved accessibility, interactive learning, assistance with lesson planning and assessments, and innovative teaching techniques (M. M. Rahman & Watanobe, 2023).”
Influential
Using ChatGPT in Undergraduate Computer Science and Software Engineering Courses: A Students' Perspective
Andersen-Kiel, N., & Linos, P. K. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10892934 · Cited by 12
“Md. Mostafizer Rahman and Yutaka Watanobe delve into ChatGPT's multifaceted role in education and research, emphasizing its capabilities in facilitating research and improving educational practices [21].”
Evolutionary Trends in Decision Sciences Education Research from Simulation and Games to Big Data Analytics and Generative Artificial Intelligence
Akpan, I., Razavi, R., & Akpan, A. A. (2025) · Big Data · doi:10.1089/big.2024.0128 · Cited by 11
Nurturing Code Quality: Leveraging Static Analysis and Large Language Models for Software Quality in Education
AlOmar, E. A. (2025) · ACM Transactions on Computing Education · doi:10.1145/3722229 · Cited by 18
“This technology has sparked a debate about its potential benefits and drawbacks for student learning [38].”
A Bibliometric Exposition and Review on Leveraging LLMs for Programming Education
Amos, J. P., Amodu, O. A., Mahmood, R. A. R., AbdulQudus, A. B., Zakaria, A. F., Iyanda, A. R., Bukar, U. A., & Hanapi, Z. M. (2025) · IEEE Access · doi:10.1109/access.2025.3554627 · Cited by 17
“The authors [24] investigate the use of ChatGPT in helping students improve their programming skills.”
ChatGPT: A reliable assistant for the evaluation of students’ written texts?
Atasoy, A., & Arani, S. M. N. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13553-1 · Cited by 19
“AI has the potential to tailor education to each learner, as described by researchers like Ngo (2023) and Rahman and Watanobe (2023), but this study makes it clear that AI on its own might not be ready to fully take on the detailed, personal parts of language that humans can rate so well.”
ChatGPT and Cheat Detection in CS1 Using a Program Autograding System
Pang, A., & Vahid, F. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653558 · Cited by 15
“These tools have raised concerns among college educators who worry students will let the tool do their homework for them, such as writing essays, or in computer science courses, writing programs [2, 3, 4].”
Background
ChatGPT-4o for English language teaching and learning: Features, applications, and future prospectsPreprint
Pang, S., Nol, E., & Heng, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4837988 · Cited by 11
Developing an integrated model to explore key factors influencing university students' behavioral intentions to use ChatGPT in enhancing higher education in the hail region
Jazim, F., Al‐Mamary, Y. H., & Abubakar, A. A. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105445 · Cited by 16
ChatGPT-Assisted Lesson Planning for Children’s STEAM Arts Education: An Experimental Study on Benefits, Challenges, Methods, and a Prompt Framework
Luo, Z., & Tahir, R. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00513-6 · Cited by 10
“For instance, Rahman and Watanobe (2023) emphasized ChatGPT’s versatility in generating lesson plans across a range of disciplines such as mathematics, science, and engineering.”
A systematic review of generative AI in education: Empirical insights from a human– AI interaction perspective
Liang, Z., Yang, K., Sha, L., Gašević, D., Yan, L., & Chen, G. (2026) · British Journal of Educational Technology · doi:10.1111/bjet.70055 · Cited by 22
Yapay Zeka Uygulamalarının Kimya Eğitiminde Kullanımı Alternatif Araçlar
Buluş, B., & Elmas, R. (2024) · Turkiye Kimya Dernegi Dergisi Kisim C Kimya Egitimi · doi:10.37995/jotcsc.1366999 · Cited by 14
From Play to Understanding: Large Language Models in Logic and Spatial Reasoning Coloring Activities for Children
Tapia-Mandiola, S., & Araya, R. (2024) · AI · doi:10.3390/ai5040093 · Cited by 11
Evaluating the Impact of AI Dependency on Cognitive Ability among Generation Z in Higher Educational Institutions: A Conceptual Framework
Tamrin, S. I., Omar, N. F., Kamaruzaman, K. N., Zaghlol, A. K., & Aziz, M. R. A. (2024) · Information Management and Business Review · doi:10.22610/imbr.v16i3s(i)a.4191 · Cited by 17
“ChatGPT allows students to obtain quick and direct answers by simply asking questions, which may enhance their academic performance in the short term (Rahman & Watanobe, 2023).”
Background
AI chatbots and political learning
Taylor, J. B., & Richey, S. (2024) · Journal of Information Technology & Politics · doi:10.1080/19331681.2024.2422929 · Cited by 10
“Almost simultaneously with ChatGPT’s release, scholars assessed this new technology’s potential to affect human education and learning (see Lo, 2023; Rahman & Watanobe, 2023).”
Prompting for pedagogy? Australian F-10 teachers’ generative AI prompting use cases
Crosthwaite, P. R., Smala, S., & Spinelli, F. (2024) · The Australian Educational Researcher · doi:10.1007/s13384-024-00787-0 · Cited by 20
“GAI applications have demonstrated the capacity to produce detailed and high-quality multimodal educational materials (e.g., Rahman & Watanobe, 2023), with educators able to utilise ChatGPT to generate exemplary written texts and oral dialogues across a variety of genres (Crosthwaite & Baisa, 2023)…”
Towards Generative AI for Course Content Production: Expert Reflections
Ullmann, T., Edwards, C. B., Bektik, D., Herodotou, C., & Whitelock, D. M. (2024) · European Journal of Open Distance and E-Learning · doi:10.2478/eurodl-2024-0013 · Cited by 17
“…course content; generating engaging video scripts based on existing written content, including suggesting on-screen graphics; and generating scenarios or case studies based on existing content (Herft, 2023; Liu et al., 2023; McInnes, 2023; Michel-Villarreal et al., 2023; Rahman & Watanobe, 2023).”
On Continually Tracing Origins of LLM-Generated Text and Its Application in Detecting Cheating in Student Coursework
Wang, Q., & Li, H. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9030050 · Cited by 13
“In the context of education, the threats are particularly severe as students can use LLMs like ChatGPT to complete coursework, online exams, and even graduation theses, which could undermine academic integrity and diminish students’ critical thinking and problem-solving skills [9,10].”
The Sentiments and the Impact of ChatGPT on Computer Programming Learning: Data Mining From Comments on YouTube Videos
Zhu, M. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70013 · Cited by 12
Exploring the difference and quality of AI-generated versus human-written texts
Tengler, K., & Brandhofer, G. (2025) · Discover Education · doi:10.1007/s44217-025-00529-z · Cited by 21
“AI-supported applications are used to answer questions, solve problems, explain complex topics, provide virtual tutoring, practice languages and write essays [3].”
Influential
Facilitating student engagement through ChatGPT
Meşe, E., Özge, K. U. R. U., & Atay, D. (2025) · The Journal of Educational Research · doi:10.1080/00220671.2025.2510394 · Cited by 10
“ChatGPT also poses threats such as a reduced use of cognitive skills such as critical thinking (Rahman & Watanobe, 2023).”
Exploring the impact of AIGC on learning performance: a moderated mediation analysis between system-paced and self-paced video learning
Jing, B., Yang, H., Pi, Z., Zhou, Y., & Ma, H. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2514090 · Cited by 12
“AIGC tools can serve as multidisciplinary teaching assistants in areas such as mathematics (Urhan et al., 2024), programming tutoring (Rahman & Watanobe, 2023), and second-language learning (Yeh, 2024).”
Assessing Student Readiness and Perceptions of ChatGPT in Learning: A Case Study in Indonesian Higher Education
Martha, A. S. D., Widowati, S., & Rahayu, D. P. (2025) · Journal of Information Technology Education Research · doi:10.28945/5599 · Cited by 24
“A Friend or A Foe” Determining Factors Contributed to the Use of ChatGPT among University Students
Yifan, W., Mengmeng, Y., & Omar, M. K. (2023) · International Journal of Academic Research in Progressive Education and Development · doi:10.6007/ijarped/v12-i2/17400 · Cited by 13
Application of Chatgpt in Higher Education and Research – a Futuristic AnalysisPreprint
Aithal, P. S., & Aithal, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4674364 · Cited by 10
Perspectives of University Educators Regarding ChatGPT: A Validation Study Based on the Technology Acceptance ModelPreprint
Barakat, M. M., Salim, N. A., & Sallam, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3919524/v1 · Cited by 10
Do large language models have a legal duty to tell the truth?Preprint
Wachter, S., Mittelstadt, B., & Russell, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4771884 · Cited by 10
Cognitive Offloading in the Age of Generative AI: What Does It Mean for Students With Learning Disabilities?
Seung, Y., & Basham, J. D. (2026) · Learning Disability Quarterly · doi:10.1177/07319487261439132 · Cited by 12
A FOUR-STAGE MODEL OF CHATGPT ADOPTION: APPLICATION IN TOURISM, HEALTHCARE, AND EDUCATION INDUSTRY
(2023) · Issues in Information Systems · doi:10.48009/3_iis_2023_121 · Cited by 11
“Adaptability: ChatGPT has many limitations, such as a lack of common sense, potential bias, difficulty with complex reasoning, and inability to process visual information (Rahman and Watanobe, 2023).”
Digital Learning Management using OpenAI ChatGPT: A Systematic Literature Review
Faruq, M. S. S. A., Sunoko, A., Ibda, H., & Wahyudi, K. (2023) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.22.12.2 · Cited by 16
Boundaries and Future Trends of ChatGPT Based on AI and Security Perspectives
Alsumayt, A., Alfawaer, Z. M., El-Haggar, N., Alshammari, M., Alghamedy, F. H., Aljameel, S. S., Alabbad, D. A., & Aldossary, M. I. (2024) · HighTech and Innovation Journal · doi:10.28991/hij-2024-05-01-010 · Cited by 10
“ Unauthorized code execution: Exploiting the natural language prompts, attackers can execute malicious code, actions, or commands on the system by leveraging the capabilities of LLMs [30].”
Background
Using Artificial Intelligence for Essay Writing
Jen, S. L., & Salam, A. R. (2024) · Arab World English Journal · doi:10.24093/awej/chatgpt.5 · Cited by 11
“This is further supported by Chiu et al. (2023), AbdAlgane and Jabir Othman (2023), Aydın and Karaarslan (2022), Farrelly and Baker (2023), Jarrah et al. (2023) and Rahman and Watanobe (2023) who asserted that AI has significantly affected education and classroom practice recently.”
Result
Exploring the challenges and affordances of integrating ChatGPT into language classrooms from teachers’ points of view: An ecological perspective
Kamali, J., Paknejad, A., & Poorghorban, A. (2024) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2024.7.2.8 · Cited by 12
Assessing the Impact of Large Language Models on Cybersecurity Education: A Study of ChatGPT's Influence on Student Performance
Ohm, M., Bungartz, C., Boes, F., & Meier, M. (2024) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3664476.3670446 · Cited by 9
“RahmanandWatanobe[14]identifyvariousopportunitiesand threats associated with using ChatGPT in higher education.”
AI-Assisted Academia: Navigating the Nuances of Peer Review With ChatGPT 4
Biswas, S. S. (2024) · The Journal of Pediatric Pharmacology and Therapeutics · doi:10.5863/1551-6776-29.4.441 · Cited by 9
Enhancing Human-Computer Interaction in Socially Inclusive Contexts: Flow Heuristics and AI Systems in Compliance with DIN EN ISO 9241 Standards
Vindigni, G. (2024) · European Journal of Contemporary Education and E-Learning · doi:10.59324/ejceel.2024.2(4).10 · Cited by 19
“LLMs optimize the programming process with advanced support functions for writing and debugging and expand the capacities for advanced data analyses through sentiment analysis and entity recognition (Topsal & Akinci, 2023; Rahman & Watanobe, 2023).”
Method
Digital Tools in Language Learning: Optimizing Memory and Attention for College Students
Gu, J. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2400384 · Cited by 17
“Going forward, educators will play a key role by providing clear recommendations and guidance on the appropriate and ethical use of AI tools in assessment (Rahman & Watanobe, 2023).”
Examining the Accuracy of AI Detection Software Tools in Education
Halaweh, M., & Refae, G. A. E. (2024) · International Conference on Intelligent Data Science Technologies and Applications (IDSTA) · doi:10.1109/idsta62194.2024.10747004 · Cited by 13
“It has the ability to consistently maintain a coherent dialog style, effectively engage users, and furnish authentic and pertinent responses, thereby avoiding irrelevant answers to individual queries [2].”
Background
Unlocking the potential of ChatGPT in academic libraries
Hussain, A. (2024) · IP Indian Journal of Library Science and Information Technology · doi:10.18231/j.ijlsit.2024.015 · Cited by 15
A First Look at Financial Data Analysis Using ChatGPT-4o
Chou, W.-H., Feng, Z., Li, B., & Feng, L. (2025) · Journal of risk and financial management · doi:10.3390/jrfm18020099 · Cited by 20
Integration of the SECI model and ChatGPT in higher education
Barreto, U., & Abarca, Y. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e42814 · Cited by 9
“The studies by Rahman and Watanobe [17] and Romero-Rodríguez et al. [9] support these findings, emphasizing that the innovative features of ChatGPT, such as personalized feedback and an accessible interface, motivate students to engage more actively in educational settings.”
PyChatAI: Enhancing Python Programming Skills—An Empirical Study of a Smart Learning System
Alanazi, M., Soh, B., Samra, H., & Li, A. (2025) · Computers · doi:10.3390/computers14050158 · Cited by 15
Qualitative evaluation of a ChatGPT-integrated transcultural nursing course for a diverse group
Gümüş, E. Ç., & Dolu, İ. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104491 · Cited by 11
From algorithms to annotations: Rethinking feedback practices in academic writing through AI-human comparison
Kashiha, H. (2025) · Journal of Second Language Writing · doi:10.1016/j.jslw.2025.101254 · Cited by 13
ChatGPT in Programming Education: An Empirical Study on Its Impact on Student Performance, Creativity, and Teamwork
Stoyanova, D., Stoyanova-Petrova, S. V., Shotarova, S., Lyubomirov, S. Y., & Mileva, N. S. (2025) · Education Sciences · doi:10.3390/educsci16010019 · Cited by 13
The Rise of ChatGPT and the Demise of Bloom's Taxonomy of Learning Stages
Damaševičius, R. (2023) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-0205-7.ch006 · Cited by 9
The Usage of Artificial Intelligence in Education in Light of the Spread of ChatGPT
Hassan, A. (2023) · Contributions to management science · doi:10.1007/978-981-99-6101-6_50 · Cited by 10
The Role of ChatGPT in Higher Education: Some Reflections from Public Administration Students
Aristovnik, A., Umek, L., Brezovar, N., Keržič, D., & Ravšelj, D. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8255-4_22 · Cited by 13
Exploring ChatGPT's Potential and Concerns in Higher Education
Tick, A. (2024) · 2024 IEEE 22nd Jubilee International Symposium on Intelligent Systems and Informatics (SISY) · doi:10.1109/sisy62279.2024.10737595 · Cited by 8

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

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

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