Publication:
Large language model-based chatbots in higher education

dc.contributor.coauthorEryilmaz, Merve
dc.contributor.coauthorYetisen, Ail K.
dc.contributor.coauthorOzcan, Aydogan
dc.contributor.departmentDepartment of Mechanical Engineering
dc.contributor.departmentDepartment of Mechanical Engineering
dc.contributor.kuauthorYığcı, Defne
dc.contributor.kuauthorTaşoğlu, Savaş
dc.contributor.schoolcollegeinstituteSchool of Medicine
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2024-12-29T09:36:08Z
dc.date.issued2024
dc.description.abstractLarge 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. The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation of LLM-based tools in real-world educational settings.
dc.description.indexedbyWoS
dc.description.indexedbyScopus
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuTÜBİTAK
dc.description.sponsorsS.T. acknowledges Tubitak 2232 International Fellowship for Outstanding Researchers Award (118C391), Alexander von Humboldt Research Fellowship for Experienced Researchers, Marie Sklodowska-Curie Individual Fellowship (101003361), and Royal Academy Newton-Katip Celebi Transforming Systems Through Partnership award (120N019) for financial support of this research. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the TUEBITAK. This work was partially supported by Science Academy's Young Scientist Awards Program (BAGEP), Outstanding Young Scientists Awards (GEBIP), and Bilim Kahramanlari Dernegi the Young Scientist Award.
dc.identifier.doi10.1002/aisy.202400429
dc.identifier.eissn2640-4567
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85200892504
dc.identifier.urihttps://doi.org/10.1002/aisy.202400429
dc.identifier.urihttps://hdl.handle.net/20.500.14288/21955
dc.identifier.wos1288219400001
dc.keywordsChatGPT
dc.keywordsEducation
dc.keywordsHigher education
dc.keywordsPedagogical approaches
dc.keywordsTeaching and learning
dc.languageen
dc.publisherWiley
dc.sourceAdvanced Intelligent Systems
dc.subjectAutomation and control systems
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.subjectRobotics
dc.titleLarge language model-based chatbots in higher education
dc.typeJournal article
dc.type.otherEarly access
dspace.entity.typePublication
local.contributor.kuauthorYığcı, Defne
local.contributor.kuauthorTaşoğlu, Savaş
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relation.isOrgUnitOfPublication.latestForDiscoveryba2836f3-206d-4724-918c-f598f0086a36

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