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.

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Integrity signals

Registry

Crossref · 10.3390/app13095783

Cites

71 of 71 references verified0 citation errors · 0 not checkable

Statistics

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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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Retracted
Addressing Challenges and Seizing Opportunities with ChatGPT in Computer Programming Education
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Influential
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“The wide availability of GPT systems and other AI tools creates the temptation to get answers without independent thought (Rahman & Watanobe, 2023).”
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Influential
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A STUDY ON THE ROLE OF TURNITIN AND CHATGPT IN THE FIELD OF EDUCATION
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Kepintaran Buatan Dalam Pengurusan Panitia Mata Pelajaran
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“Better Than Human” in Partnership With AI
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Faceless Adversary, Feckless Colleague: The Many Sides of ChatGPT
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The effectiveness of the academic support tool (ChatGPT) on stimulating learning for A'Sharqiyah University students in the Sultanate of Oman from their point of view
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USO DO CHATGPT POR ESTUDANTES ITALIANOS NA UNIVERSIDADE DEGLI STUDI DI PERUGIA-UNIPG
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Pedagogical Design
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“These concerns are applicable to ChatGPT, a technology with both considerable potential and significant risks [9], [10].”
Perception of ChatGPT’s Effectiveness in Learning
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Generative KI im universitären Kontext – Die Perspektive der Studierenden
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“Aktuelle Studien, die sich mit GenKI im Hochschulkontext auseinandersetzen, machen auf zahlreiche damit verbundene Chancen und Risiken aufmerksam (vgl. z. B. Abdaljaleel et al., 2024; Chan, 2023; Lo, 2023; Montenegro-Rueda et al., 2023; Rahman & Watanobe, 2023).”

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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