Cited by 115Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationIntelligent Tutoring Systems and Adaptive Learning

Can ChatGPT Pass High School Exams on English Language Comprehension?

Open access
de Winter, Joost C.F.·2024·International Journal of Artificial Intelligence in Education

Abstract

Launched in late November 2022, ChatGPT, a large language model chatbot, has garnered considerable attention. However, ongoing questions remain regarding its capabilities. In this study, ChatGPT was used to complete national high school exams in the Netherlands on the topic of English reading comprehension. In late December 2022, we submitted the exam questions through the ChatGPT web interface (GPT-3.5). According to official norms, ChatGPT achieved a mean grade of 7.3 on the Dutch scale of 1 to 10—comparable to the mean grade of all students who took the exam in the Netherlands, 6.99. However, ChatGPT occasionally required re-prompting to arrive at an explicit answer; without these nudges, the overall grade was 6.5. In March 2023, API access was made available, and a new version of ChatGPT, GPT-4, was released. We submitted the same exams to the API, and GPT-4 achieved a score of 8.3 without a need for re-prompting. Additionally, employing a bootstrapping method that incorporated randomness through ChatGPT’s ‘temperature’ parameter proved effective in self-identifying potentially incorrect answers. Finally, a re-assessment conducted with the GPT-4 model updated as of June 2023 showed no substantial change in the overall score. The present findings highlight significant opportunities but also raise concerns about the impact of ChatGPT and similar large language models on educational assessment.

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Crossref · 10.1007/s40593-023-00372-z

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • ChatGPT achieves an average of 71.8% correctness, comparable to the average score of all participating students.

    “de Winter [32] showed that ChatGPT achieves an average of 71.8% correctness, which is comparable to the average score of all participating students.”

    From A Survey on Evaluation of Large Language Models (2024) · cites this paper as [32]

  • GPT-4 achieved a score of 8.33.

    “Subsequently, the evaluation was conducted using GPT-4, and it achieved a score of 8.33.”

    From A Survey on Evaluation of Large Language Models (2024) · cites this paper as [32]

  • Leveraging bootstrapping that combines randomness via the 'temperature' parameter is effective in diagnosing incorrect answers.

    “Furthermore, this evaluation showed the effectiveness of leveraging bootstrapping that combines randomness via the 'temperature' parameter in diagnosing incorrect answers.”

    From A Survey on Evaluation of Large Language Models (2024) · cites this paper as [32]

  • ChatGPT-4 has been shown to outperform the average student in a variety of verbally-oriented tests and exams.

    “The latest version, ChatGPT-4, has been shown to outperform the average student in a variety of verbally-oriented tests and exams (De Winter, 2023; Katz et al., 2023; Nori et al., 2023; OpenAI, 2023a).”

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References 36 · Cited by 115

Authors
de Winter, J. C. F.
Venue
International Journal of Artificial Intelligence in Education
Publisher
Elsevier BV
Published
2024-09
Volume
34
Issue
3
Pages
915-930
ISSN
1560-4292 (ISSN-L)
OpenAlex
W4386718985
Cited by
115
References
36
Citations by year
2026: 13 · 2025: 23 · 2024: 44 · 2023: 35
FWCI
26.99
Type
journal article
Language
en
Open access
Locations
  • International Journal of Artificial Intelligence in Education (publishedVersion, cc-by)
  • Research Repository (Delft University of Technology) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
ChatGPT, GPT-4, large language models, English reading comprehension, educational assessment, artificial intelligence in education, temperature parameter, bootstrapping

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