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Cited by 113Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationAI in Service Interactions

Large Language Model‐Based Chatbots in Higher Education

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Yigci, Defne, Eryilmaz, Merve, Yetisen, Ail K., Tasoglu, Savas, Ozcan, Aydogan·2025·Advanced Intelligent Systems
DOI 10.1002/aisy.202400429
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Abstract

Large language models (LLMs) are artificial intelligence (AI) platforms capable of analyzing and mimicking natural language processing. Leveraging deep learning, LLM capabilities have been advanced significantly, giving rise to generative chatbots such as Generative Pre‐trained Transformer (GPT). GPT‐1 was initially released by OpenAI in 2018. ChatGPT's release in 2022 marked a global record of speed in technology uptake, attracting more than 100 million users in two months. Consequently, the utility of LLMs in fields including engineering, healthcare, and education has been explored. The potential of LLM‐based chatbots in higher education has sparked significant interest and ignited debates. LLMs can offer personalized learning experiences and advance asynchronized learning, potentially revolutionizing higher education, but can also undermine academic integrity. Although concerns regarding AI‐generated output accuracy, the spread of misinformation, propagation of biases, and other legal and ethical issues have not been fully addressed yet, several strategies have been implemented to mitigate these limitations. Here, the development of LLMs, properties of LLM‐based chatbots, and potential applications of LLM‐based chatbots in higher education are discussed. Current challenges and concerns associated with AI‐based learning platforms are outlined. The potentials of LLM‐based chatbot use in the context of learning experiences in higher education settings are explored.

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  • The results align with prior research advocating GenAI chatbots in language learning, particularly for promoting interactive, adaptive engagement.

    “These results align with prior research advocating GenAI chatbots in language learning, particularly for promoting interactive, adaptive engagement (Kohnke et al., 2023;Yigci et al., 2024;Zhang & Huang, 2024).”

    From Integrating ChatGPT for vocabulary learning and retention: A classroom-based study of Saudi EFL learners (2025) · cites this paper as Yigci et al., 2024

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Authors
Yigci, D., Eryilmaz, M., Yetisen, A. K., Tasoglu, S., & Ozcan, A.
Affiliations
  • Koç University (TR)
  • Department of Electrical & Computer Engineering University of California Los Angeles (UCLA) Los Angeles CA 90095 USA
  • Imperial College London (GB)
  • Department of Mechanical Engineering Koç University 34450 Sariyer Istanbul Turkey
ORCID
Venue
Advanced Intelligent Systems
Publisher
Wiley
Published
2024-08-11
Volume
7
Issue
3
ISSN
2640-4567 (ISSN-L)
DOI
10.1002/aisy.202400429
OpenAlex
W4401497789
Cited by
113
References
106
Citations by year
2026: 47 · 2025: 61 · 2024: 5
FWCI
42.49
Type
journal article
Language
en
Open access
Licence
Locations
  • Advanced Intelligent Systems (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Bilim Akademisi (Young Scientist Awards Program (BAGEP))
  • Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (118C391)
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 › AI in Service Interactions
Keywords
large language models, chatbots, higher education, generative AI in education, ChatGPT, academic integrity, personalized learning, asynchronous learning, misinformation, algorithmic bias, AI ethics, AI-generated content accuracy

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.

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 ›

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