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Application of Large Language Models to Enhance Student Support Services in the Context of University Autonomy

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Title: Application of Large Language Models to Enhance Student Support Services in the Context of University Autonomy
Language: English
Authors: Anh Tuan Nguyen (ORCID 0009-0005-8745-7592)
Source: International Journal of Research in Education and Science. 2026 12(1):230-242.
Availability: International Society for Technology, Education, and Science. e-mail: ijresoffice@gmail.com; Web site: https://www.ijres.net/index.php/ijres
Peer Reviewed: Y
Page Count: 13
Publication Date: 2026
Document Type: Journal Articles
Information Analyses
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Natural Language Processing, Computer Uses in Education, College Students, Individualized Instruction, Ethics, Educational Research, Man Machine Systems, College Instruction
ISSN: 2148-9955
Abstract: This systematic review analyzes research on the application of Large Language Models (LLMs) to enhance the quality of learner support in the context of university autonomy. The study aims to evaluate the current applications of LLMs in providing personalized and adaptive learning paths, identify ethical challenges, and analyze the role of prompt engineering and human-in-the-loop supervision. The research method involves a systematic analysis of scientific works published up to mid-2024. The findings indicate that LLMs significantly enhance personalized feedback and adaptive tutoring, thereby promoting self-regulated learning and student engagement. However, challenges related to feedback accuracy and ethical issues persist, requiring robust governance frameworks. The conclusion emphasizes that effective LLM integration requires combining technological power with pedagogical expertise and human oversight to optimize the educational experience and successfully support autonomous learners.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1494248
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Application of Large Language Models to Enhance Student Support Services in the Context of University Autonomy
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  Data: <searchLink fieldCode="AR" term="%22Anh+Tuan+Nguyen%22">Anh Tuan Nguyen</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0005-8745-7592">0009-0005-8745-7592</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Research+in+Education+and+Science%22"><i>International Journal of Research in Education and Science</i></searchLink>. 2026 12(1):230-242.
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  Data: International Society for Technology, Education, and Science. e-mail: ijresoffice@gmail.com; Web site: https://www.ijres.net/index.php/ijres
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  Data: This systematic review analyzes research on the application of Large Language Models (LLMs) to enhance the quality of learner support in the context of university autonomy. The study aims to evaluate the current applications of LLMs in providing personalized and adaptive learning paths, identify ethical challenges, and analyze the role of prompt engineering and human-in-the-loop supervision. The research method involves a systematic analysis of scientific works published up to mid-2024. The findings indicate that LLMs significantly enhance personalized feedback and adaptive tutoring, thereby promoting self-regulated learning and student engagement. However, challenges related to feedback accuracy and ethical issues persist, requiring robust governance frameworks. The conclusion emphasizes that effective LLM integration requires combining technological power with pedagogical expertise and human oversight to optimize the educational experience and successfully support autonomous learners.
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      – SubjectFull: Natural Language Processing
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      – SubjectFull: Computer Uses in Education
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      – SubjectFull: College Students
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      – SubjectFull: College Instruction
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