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 901–1,000 of 1,004

The Impact of Artificial Intelligence on Programmer Jobs: Threat or Opportunity?
Hananto, A., Hasibuan, N. S., Olivia, S., Tukino, T., & Novalia, E. (2025) · JUSIFO (JURNAL SISTEM INFORMASI) · doi:10.19109/jusifo.v11i1.25082 · Cited by 1
Factors Affecting Pre-Service Teachers’ Adoption of AIGC in Learning—A Psychological Stress Perspective
刘, 骞. (2025) · Advances in Education · doi:10.12677/ae.2025.1571377
Use ChatGPT in Teaching and Learning: Opportunity and Challenge
Ren, L. (2024) · Advances in Social Behavior Research · doi:10.54254/2753-7102/11/2024105
Harnessing Higher Generative Artificial Intelligence for Educational Pedagogy
Emakporuena, D., Oluwasegun, V. O., Esegbona-Isikeh, O. M., Ugbomeh, W. O., Nwaiku, M., Bunmi, E. E., Oyedemi, O. A., & Matthew, U. O. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9841-8.ch002
Investigating Undergraduate Students' Acceptance of ChatGPT for Learning: A Technology Acceptance Model Perspective
Chai, S., & He, Z. (2025) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste64638.2025.11092270
“ChatGPT may also impede the development of critical thinking and interpersonal skills [11], [12], [13].”
A Descriptive Bibliometric Analysis and Review of ChatGPT in Programming Education
Hernandez, H. E., Nacianceno, M. C. B., Canlas, R. B., Loyola, L. G., Salenga, J. L., Yambao, J. A., Grume, J. C., Austria, J. A. P., & Miranda, J. P. P. (2025) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste64638.2025.11091950
AI and ChatGPT: Will It Build Business Education’s Future?
Sayari, K., Jalagat, R. C., & Belgacem, M. A. B. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83708-432-620251009
Challenges and Solutions for Large Language Models in Metaphor Translation of Political Texts
Chen, S., & Wang, Z. (2025) · Open Journal of Applied Sciences · doi:10.4236/ojapps.2025.158158
A systematic literature review on academic writing tools
Reeta, R., Madhusudhan, M., & Pandey, P. (2025) · Journal of Knowledge & Communication Management · doi:10.5958/2277-7946.2025.00002.4
Are Japanese University EFL Classrooms Ready for ChatGPT? Advantages Disadvantages, and Considerations
Enomoto, A. (2025) · Glovento Journal of Integrated Studies · doi:10.63665/gjis.v1.15
Determining the Role of Artificial Intelligence-Supported Applications in Reliability of Measurement Activities: Awareness and Precautions
Kılıç, S. (2025) · Uludağ Üniversitesi Eğitim Fakültesi Dergisi · doi:10.19171/uefad.1565847
The Potential of Artificial Intelligence to Personalize Learning Within the Evolving Landscape of Open Distance Learning
van den Berg, G. (2025) · Future education and learning spaces · doi:10.1007/978-981-96-7937-9_6
The Effect of Employing ChatGPT to Enhance the Searchability among Female Researchers
Owda, M. F. A., & Zaqout, Y. (2025) · Creative Education · doi:10.4236/ce.2025.169080
Empowering Educational Equity: The Impact of PASS on Student Achievement in Schools Academically Challenged in Trinidad and Tobago
John, Y. J. (2025) · Futurity Education · doi:10.57125/fed.2025.06.25.11
“…particularly in a post-pandemic context, is essential to identify how such tools may further enhance educational equity and overall effectiveness; this aligns with emerging trends that emphasize AI technologies transforming learning environments (Rahman et al., 2023; Rzo Eńca et al., 2024).”
Applied Media Psychology and its Contribution to Sustainability
Hennighausen, C., & Eller, E. (2025) · SDG - Forschung, Konzepte, Lösungsansätze zur Nachhaltigkeit · doi:10.1007/978-3-658-47879-7_14
Mechanisms of academic stress affecting AI-assisted cheating behaviour in college students: a mixed methods study
Wang, G., Tian, L., & Xing, X. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2565684
“Students with high self-efficacy are more likely to rely on their own abilities to solve problems, whereas students with low self-efficacy may be more inclined to resort to external tools (Luo et al., 2023; Rahman & Watanobe, 2023).”
GenAI in Higher Education: A Cross-Country Study on Performance Expectancy and Sustainable Education
Ziemba, E. W., & Karmańska, A. M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06611-4_22
Prospective Teachers' Perceived Affordances and Constraints of Using ChatGPT in Mathematical Problem Posing
Ulusoy, F. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1998-8.ch007
Exploring the Potential of AI-Driven Chatbots With a Focus on SkyTutor in Aviation English
Dinçer, N., & Çetinok, M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7574-8.ch009
Impact of Artificial Intelligence on the Educational Process
Yanev, N., Getova, I. D., & Mihailova, E. (2025) · International Scientific Conference Electronics (ET) · doi:10.1109/et66806.2025.11204097
“In their paper “ChatGPT for Education and Research: Opportunities, Threats, and Strategies,” Rahman and Watanobe [9] explore ChatGPT’s implications from both student and faculty perspectives, analyzing potential risks through coding experiments validated by online judge systems.”
Evaluation of the Teacher Quality Improvement Training Program for Junior High School Level Using the CIPP Model
Usman, U., Wijaya, C., & Dalimunte, M. (2025) · Journal Evaluation in Education (JEE) · doi:10.37251/jee.v6i4.2232
How Do Mathematics Teachers Use ChatGPT in Their Classes?
Dertli, Z. G., & Kaya, Y. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7729-2.ch007
The Impact of Communicative Strategies on ChatGPT Interaction and Text Quality: A Comparative Study
Palenova, V. V., & Воронин, А. Н. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.578
Transforming Creative Problem-Solving with Generative AI Tools
Urban, M., & Urban, K. (2025) · Signals and communication technology · doi:10.1007/978-3-031-96855-6_17 · Cited by 2
Guidelines for managing and utilizing generative artificial intelligence writing tools
Mufeti, T. K., Amakali, J. M. L., & Greenlaw, R. (2025) · NAWA Journal of Language and Communication · doi:10.59677/njllcs.v19i1.121
Decoding the Raters’ mind: A metadiscourse investigation of ChatGPT vs EFL instructors in Business English Writing Assessment
Zhu, Y., & Dong, M. (2025) · Studies In Educational Evaluation · doi:10.1016/j.stueduc.2025.101541 · Cited by 1
Research on AI-Empowered Pedagogical Implementation Methods for Data Structure
Liu, C. (2025) · International Conference on AI-enabled Education · doi:10.1145/3768421.3768498
“Neverthe-less, significant challenges persist in implementing AI-empowered pedagogy [7-10], necessitating further scholarly investigation.”
Towards AI Didactics: Call for Teachers to Train Large Language Models for Research Writing in Higher Arts Education
Väkevä, L., & Partti, H. (2025) · Springer eBooks · doi:10.1007/978-3-031-99804-1_9
Exploring applications of artificial intelligence in enhancing the quality of medical education: a mixed methods research synthesis
Mahdi, R., Keykha, A., Kaliisa, R., & Darabi, F. (2025) · Journal of Medical Education Development · doi:10.61882/edcj.18.4.119
ADAPTIVE LEARNING SYSTEMS FOR MULTIMEDIA DESIGN EDUCATION
Sharma, A., Kaushik, N., Srivastav, A., Verma, S. K., Khurana, J., & Khurana, J. (2025) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v6.i2s.2025.6701 · Cited by 3
Maintaining Academic Integrity in the Era of Large Language Models
Hidayati, N. N., Santosa, A., Nurfadhilah, E., Latief, A. D., Komariah, K. S., Jarin, A., Pebiana, S., Wabula, Y., Fajri, R., & Sampurno, T. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0250-8.ch005
Enhancing Self-Regulated Learning and Learning Motivation Through Generative Learning: An Empirical StudyPreprint
Chen, Y.-C., Chu, H.-C., & Chueh, C.-Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5526737
An Empirical Evaluation for LLMs Performance on AI Question and Answer in Bengali
Koner, S., Goswami, S., Chakrabarti, A., & Chakraborty, B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6537-2_7
User-Chatbot Similarity in Communication Style and Avatar Appearance for Enhanced Learning in Education
Pepping, J., de Wit, J., Kamoen, N., & Liebrecht, C. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2600535
“…interactions with promising results, yet also raising concerns about, for example, students’ diminished ability to engage in critical thinking (Rahman & Watanobe, 2023) A recent meta-analysis by Laun and Wolff (2025) synthesized empirical studies to map CML’s educational applications and…”
A Systematic Review of Empirical Studies Fostering Generative AI Literacy in Higher Education
Xu, L., Ng, D. T. K., Deng, H., & Leung, J. K. L. (2025) · Bridging human and machine: future education with intelligence · doi:10.1007/978-981-95-0950-8_8
The Research Status and Hotspot Analysis of the Effect of Generative Artificial Intelligence Technology on Education at Home and Abroad: Visual Analysis Based on CiteSpace
Guo, Y. (2026) · Lecture notes in educational technology · doi:10.1007/978-981-95-0872-3_3
Use and Misuse of ChatGPT Among Tertiary-Level Students
Tutor, B. O. D., Jimenez, A. S., Villamin, A. A., Justiniani, M. V., Olarte, M. L., & Lirio, E. J. D. (2026) · Lecture notes in educational technology · doi:10.1007/978-981-95-2521-8_15
Artificial Intelligence in Content Development: A Competency-Based Framework for Media and Education Professionals
Cevallos, E. P. V., & Cruz-Silva, J. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-09911-2_43
Empowering Educators: A Framework for Maintaining Course Integrity in the Age of Artificial Intelligence
Green, A., & Johns, J. D. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-12773-0_5 · Cited by 1
An AI-Powered Chatbot Using a Transformer-Based Language Model for Graduate Educational Schemes Recommendation
Chowdary, S. V. P., Charan, A. S., Satapathy, A., Prasanna, C. S. S., & Thottempudi, K. (2025) · Lecture notes in information systems and organisation · doi:10.1007/978-3-032-06725-8_16
WIP: Tutoring System Using a Large Language Model
Rao, B., Renji, N. M., & Lipizzi, C. (2025) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie63693.2025.11328211
“Rahman et al. discuss at length the opportunities present in using generative AI in educational settings but then draw attention to some of the major issues such as the integrity of students in completing and working on assignments without the help of AI [29].”
A Simulation-Based Approach to Enhance Conflict Resolution Through Non-Violent Communication and Large Language Models in Teamwork
Feijoo‐Garcia, M. A., Cristancho, J., Dworkin, J. B., & Magana, A. J. (2025) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie63693.2025.11328298
“However, while LLMs can assist students in shaping their arguments/questions, they might overlook emotional human factors that naturally arise in human interactions and emotional engagement [15], [21].”
WIP: Structured AI Tutoring in Engineering Education
Cruz, A., Yatawara, A., Misha, M., & Wang, J. (2025) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie63693.2025.11328293
“However, concerns remain about their unstructured use [7, 8, 9] and the need for instructors to provide adequate scaffolding for AI-assisted lessons [10].”
Bibliometric and Comparative Analysis of Generative Artificial Intelligence in Education Research
Moresi, E. A. D., Burneo, P. S., Machado, L. B., de Pinho, I. G., Freitas, F., & Costa, A. P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-12885-0_12
Utilizing Large Language Models to Assist in the Teaching of Fundamentals of Computer Courses
Qiao, Y., & Ren, J. (2025) · Journal of big data and computing. · doi:10.62517/jbdc.202501316
The Impact of Generative Artificial Intelligence on College Students’ Computer Thinking in the Task of Complex Computer Programming: Based on Social Cognitive Theory
Yin, W. (2026) · Creative Education · doi:10.4236/ce.2026.171007 · Cited by 2
Who Wrote This Code? Investigating Human and AI Programming Patterns
Nitika, & Narwal, R. (2025) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip68302.2025.11346257
“Rahman and Watanobe, 2023 [5] discussed the advantages and disadvantages of utilizing ChatGPT in learning environments, especially for programming education.”
“The Use of Artificial Intelligence in Written Discourse Production: A Case Study in Upper Secondary School Essay Writing (11th Grade)”
Bourou, E. (2026) · International Journal of Applied Sciences & Development · doi:10.37394/232029.2026.5.3
Gender and AI in Flipped Classrooms: Unpacking the Impact of Chatbots on Engagement, Self-Efficacy, Writing Performance and Technology Affinity
Suciati, S., Silitonga, L. M., & Anggara, A. A. (2026) · SAGE Open · doi:10.1177/21582440261420014
“…the findings align with studies demonstrating that AI Chatbots like ChatGPT can serve various functions, including answering questions, generating content, solving problems, providing tutoring, and supporting language learning and research (Boudouaia et al., 2024; Rahman & Watanobe, 2023).”
Understanding Factors Influencing Students Intention to Use ChatGPT for Learning Programming with Gender and IT Experience as Moderators Based on AdoptGPT Prog Conceptual ModelPreprint
Alshammari, F. (2026) · Research Square · doi:10.21203/rs.3.rs-8564643/v1
AST-MLEC: AST-Enhanced Multi-Label Error Classifier for Programming Education Support
Matsumoto, T., & Editorial Office (2026) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2026.p0242
Integrating AI Literacy in Programming Education: Enhancing Object-Oriented Design and Problem-Solving Skills
Ong, Y. C., Singh, U., Alwi, S. S. E., Usop, N. S. M., Muda, R., & Bongsu, R. H. R. (2025) · IEEE International Conference on Engineering Education (ICEED) · doi:10.1109/iceed66794.2025.11400487
“Nevertheless, the effective integration of AI into education demands more than technical proficiency; students must develop AI literacy – the ability to critically interpret, evaluate, and responsibly engage with AI-generated content [5], [6].”
The Translation of ChatGPT Capabilities in Higher Education Institutions
Bure, R., Iyamu, T., & Ncubukezi, T. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2721-1.ch001
References
(2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83608-186-920251023
Editorial: Generative AI in business education: some future research directions
Nguyen, N. (2026) · Journal of Trade Science · doi:10.1108/jts-03-2026-076 · Cited by 1
Generative Artificial Intelligence in Education: A Systematic Literature Review on Instructional Materials Development
Nuryadin, A., Karlimah, K., & Nugraha, M. R. (2026) · Participatory Educational Research · doi:10.17275/per.26.27.13.2
A framework for integrating ChatGPT in wireless communication engineering course from basic concepts to Scilab coding
Ponnapalli, V. A. S., Baluguri, S. B., Bantikatla, H., Chaitanya, M. K., Zubairuddin, M., & Tamboli, S. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08502-7
Analysis the impact of using artificial intelligence (AI) ChatGPT on student productivity and creativity
Putra, V. A. P., & Minsih (2026) · AIP conference proceedings · doi:10.1063/5.0318647
Ethical documentation of open-source AI models: a multivocal literature review and large-scale analysis of model cards
García-Barceló, C., Tomás, D., & Mazón, J.-N. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132062
Construindo trilhas de aprendizagem em inteligência artificial generativa
Muniz, A. B. X., Reis, G. C., & Kohls-Santos, P. (2026) · ETD - Educação Temática Digital · doi:10.20396/etd.v28i00.8677351
Design Thinking for AI Integration in a Leadership Course
Kim, Y., Chaivisit, S. “., Ibukun, A., & Thanh, D. (2025) · Quarterly review of distance education · doi:10.1108/qrde-08-2025-0005
Intensive online programming courses: a review, reflections, and suggestions, for teaching and learning
Brustad, T. F. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1794564
Random Knowledge Verification as a Scalable Assessment Strategy in AI Rich Higher EducationPreprint
Duc, T. M. (2026) · Research Square · doi:10.21203/rs.3.rs-9103013/v1
Biology Meets Generative AI
Jeong, D.-K., & Kwon, Y.-J. (2026) · The American Biology Teacher · doi:10.1525/abt.2026.88.3.167
生成式人工智能赋能物理教学的特征比较与路径审思—基于中美比较研究的视角
马, 文., 华, 丽., & 陈, 欣. (2026) · 教育发展论坛 · doi:10.65976/3080-0374.2026.05.059
CaseMaster: Designing and Evaluating a Probe for Oral Case Presentation Training with LLM Assistance
Ouyang, Y., Xu, Y., Jiang, C., Jin, Y., Jiang, H., & Li, Q. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790304 · Cited by 2
LingoLift: Supporting Educators in Personalized Oral Language Teaching for Autistic Children through Content Generation
Zhang, J., Pan, D. P., Wang, L., Hui, P., & Tong, X. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790284 · Cited by 1
Explore factors affecting students’ acceptance of AI resurrection and ethical dilemmas in AI resurrection technology among Chinese college students: a cross-sectional study
Cheng, N., Huang, W., & Sun, M. T. (2026) · BMC Psychology · doi:10.1186/s40359-026-04496-4
“They are early adopters of digital technology, frequently engage with AI in educational and personal contexts, and will likely shape the ethical and societal norms surrounding future AI use [10, 11].”
MODELLING STUDENTS’ PERCEPTIONS OF ARTIFICIAL INTELLIGENCE (AI) TOOL USAGE IN LEARNING STATISTICS: AN EMPIRICAL STUDY
Zin, S. H. H. M., Hassim, N. H., Chang, K. L., & Taha, F. N. M. (2026) · International Journal of Modern Education · doi:10.35631/ijmoe.829069
LearnIT: AI-Powered Learning Companion in Education Setting Using Text Segmentation and Contextual Condensing (TSCC)
Ramillano, A. R. B., & Recario, R. N. C. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11467566
“Emerging technologies (ETs) have transformed education through adaptable, context-aware tools such as Web 2.0, AR, VR, and learning analytics [13]–[15].”
Influential
Multidimensional Perspective of Generative Artificial Intelligence Ethics
王, 艺. (2026) · Advances in Philosophy · doi:10.12677/acpp.2026.154176
Shaping Mathematics Activities with Generative AI: Prompt Types, Models and Pedagogical Outcomes
Aydoğdu, M. Z., ERGENE, B. Ç., & Ergene, Ö. (2026) · Kuramsal Eğitimbilim · doi:10.30831/akukeg.1795773
AI training for EFL teachers in Indonesia: Improvements on teachers’ knowledge, attitude, and practices
Rohmah, Z., & Roiba, A. N. L. (2026) · Learning Research and Practice · doi:10.1080/23735082.2026.2660718
“…English as a Foreign language (EFL), AI-powered applications, including chatbots, automated feedback systems, and adaptive content generators, have demonstrated potential to enhance material development and support learner-centred instruction (Dizon et al., 2022; Lo, 2023; Rahman & Watanobe, 2023).”
The Role of ChatGPT as an Emerging Technology in University Learning in the Era of Generative Artificial Intelligence
Matari, A. S. A., Kiyumi, U. N. A. A., udhubi, K. H. S. A., Barwani, I. S. A., Sawafi, N. A. N. A., Abri, J. S. A., & Jahdhami, Y. A. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-15806-2_24
Enhancing self-regulated learning in online environments: challenges and opportunities for recommender system algorithms
Zhang, L., Hew, K. F., & Maimaiti, G. (2026) · Educational Technology Research and Development · doi:10.1007/s11423-026-10618-x · Cited by 1
“They can offer personalized feed-back (Rahman & Watanobe, 2023), adjust learning materials to meet individual needs (Gligorea et al., 2023), and enhance student engagement (Almusaed et al., 2023).”
Artificial Intelligence as a Catalyst for Innovation in Startups and Finance
Kumari, A., & Thakur, P. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898812973126010007
Opportunities and Challenges of AI Chatbots in English Language Learning: A Cross-University Study of Learner Perceptions
Kadhim, S. N., & Hameed, H. H. (2026) · European Journal of Contemporary Education and E-Learning · doi:10.59324/ejceel.2026.4(3).01
Design Thinking Meets AI
Al‐Romeedy, B. S., Hussein, H. S., & Emam, M. E. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7102-3.ch001 · Cited by 1
A Transformer-Based Aspect-Level Sentiment Analysis Framework for Analyzing Public Discourse on Generative AI
Martutty, N. M., Rizal, M. N., & Putra, G. D. (2026) · International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML) · doi:10.1109/aaiml67890.2026.11498208
“Despite these advantages, growing concerns have been raised regarding potential academic dishonesty, biases embedded in training data [4], and the risk of excessive reliance on GenAI systems, which may undermine students’ critical thinking and problem-solving abilities [5].”
“ChatGPT!, Finally, a Colleague Who Understands Me!”
Heesen, R., & Taconis, R. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-16153-6_9
Game-Changer or Hype? A Longitudinal Study of GenAI Opportunities, Challenges, and Teaching–Learning Activities
Levy‐Nadav, L., Shamir‐Inbal, T., & Blau, I. (2026) · Education Sciences · doi:10.3390/educsci16050744
ARTIFICIAL INTELLIGENCE TOOLS, EDUCATIONAL APPROACHES, AND LEARNING OUTCOMES IN ENVIRONMENTAL EDUCATION
Kuş, Ö. A. (2026) · Journal of Baltic Science Education · doi:10.33225/jbse/26.25.298 · Cited by 1
Assessing ChatGPT’s logical reasoning quality in physics problem solving
Meşe, E., Gür, R., & Korkmaz, S. D. (2026) · Discover Education · doi:10.1007/s44217-026-01464-3 · Cited by 1
Pre-service Teachers’ Experiences in Instructional Design with Artificial Intelligence Tools
Ayaz, E., & Ekinci, V. (2026) · Springer eBooks · doi:10.1007/978-3-032-18030-8_8
Assessment of dependence on artificial intelligence and associated factors among medical students
Zencirci, S. A. (2026) · Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Halk Sağlığı Dergisi · doi:10.35232/estudamhsd.1915530
The Theory and Application of University Practice Teaching Based on Multi-Teacher Agent Collaboration
Gao, H., & Zheng, J. (2026) · International Conference on Educational and Information Technology · doi:10.1109/iceit68991.2026.11521840
“…evaluation system to boost collaborative learning outcomes and participation enthusiasm [2]; AI real-time guidance that provides instant answers to students 'practical questions through AI interactions, offering precise instruction to effectively alleviate teachers' instructional workload [6][7].”
Sentiment Analysis of Generative AI Usage in Indonesia using LSTM and BERT Models
Atmojo, A. P. A., Kusuma, D. J., Arienaldi, N. A., & Achmad, S. (2026) · International Conference on Current Research in Artificial Intelligence and Data Science (ICCRAIDS) · doi:10.1109/iccraids67816.2026.11519758
“In the past when Google first emerged, questions were raised about how the search engine will change the way people think, read, and remember [3].”
ÜRETKEN YAPAY ZEKADA SİBER GÜVENLİK VE ETİK: CHATGPT ÖRNEĞİ
Gürsoy, S., & Yalçın, N. (2026) · Güvenlik Bilimleri Dergisi · doi:10.28956/gbd.1707204
К проблеме внедрения технологий искусственного интеллекта и их влиянии на интеллект и здоровье обучающихся
Морозов, А. В. (2026) · Человеческий капитал. · doi:10.25629/hc.2026.05.11
Student-AI collaboration in literacy: Evaluating ChatGPT-driven text personalization
Bulut, A. (2026) · The Journal of Educational Research · doi:10.1080/00220671.2026.2664179 · Cited by 1
تأثير تطبيقات الذكاء الاصطناعي في تنمية مهارات التواصل اللغوي لدى الناطقين بغير العربية: تطبيق ChatGPT أنموذجا
عودة, م. خ. م. (2026) · مجلة العلوم التربوية و الدراسات الإنسانية · doi:10.55074/awx80506
Big Data Engineering Practices and Generative AI—Students’ Usage in Programming and Its Impact on Learning
周, 启. (2026) · Creative Education Studies · doi:10.12677/ces.2026.145366
Large language models in software development learning
Hernández, S. C., Cruz, M. P. T., Sánchez, J. M. E., Luna-Gómez, A. A., Orozco, A. M. P., & Medina, R. H. (2026) · Discover Education · doi:10.1007/s44217-026-01750-0
“ChatGPT in education within the scientific literature stands out as a disruptive element since its emergence due to the advancements and new capabilities added in each version [2, 74, 111, 129, 136].”
Penggunaan Chatgpt Dalam Penyelesaian Tugas Akademik: Analisis Deskriptif Technology Acceptance Model Pada Mahasiswa Informatika Medis Stikes St. Elisabeth Keuskupan Maumere
Davinsi, G. R., I, M. C. Y. W., & Kuwa, M. K. R. (2026) · Indonesian Journal of Multidisciplinary on Social and Technology · doi:10.69693/ijmst.v4i2.9477
Generative AI in Assessments: A Mixed-Methods Approach to Balancing Opportunity and Academic Integrity
Hlongwa, N. S., & Sithole, M. P. (2025) · Journal of Educational Studies · doi:10.59915/jes.2025.24.4.2
Factors Influencing University Students’ Intention to Use LLM Agents for Effective Self-Directed Learning: An Extension of the UTAUT Model
Fan, J., Huang, M., Zhao, X., Fang, Y., Zhang, H., & Lu, J. (2026) · TechTrends · doi:10.1007/s11528-026-01188-z · Cited by 1
“…in higher education by supporting personalized academic tasks such as mathematical proofs (Park & Manley, 2024), automated question answering (Urban et al., 2024), paper editing (Rahman & Watanobe, 2023), letter writing (Li et al., 2024a, 2024b) and student text evaluation (Tossell et al., 2024).”
Shaping Pedagogy for the AI Generation: Active Learning Techniques and Theoretical Foundations for Public Service Education
Allgood, M., & Frandell, A. (2026) · Journal of Public Affairs Education · doi:10.1177/15236803261450478
Opportunities for learning amidst concerns of misuse: Secondary teachers' uses and perceptions of artificial intelligence
Tashmeer, M., Davies, M. J., & Stephens, J. M. (2026) · British Educational Research Journal · doi:10.1002/berj.70208
GamifiedLM: Co-Designing an LLM-Driven Gamified Learning App with University Students to Mitigate Learning Difficulties
Tang, K., Chen, K.-W., Gopi, S., Xie, L., Quinlan, M., & Cho, Y. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812916 · Cited by 2
“Effectively integrating LLMs into educational practice, while leveraging their advantages and mitigating potential drawbacks, and aligning their use with beneficial learning theories, is increasingly being recognised as a significant area for research [65, 67].”
"Are you biased right now, AI?": Investigating Supporting Youths' Systematic Evaluation of GenAI
Solyst, J., Walker, L., Handa, S., Nurdin, F., & Shapiro, R. B. (2026) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3773077.3806125
“Younger youth in K-12 students already interact GenAI for schoolwork [11, 13, 25, 28, 43, 68], creative production [2, 4, 8], and social interaction [1, 21, 42, 64].”

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