Application of Large Language Models to Enhance Student Support Services in the Context of University Autonomy
Saved in:
| Title: | Application of Large Language Models to Enhance Student Support Services in the Context of University Autonomy |
|---|---|
| Language: | English |
| Authors: | Anh Tuan Nguyen (ORCID |
| 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 |
| 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. |
|---|---|
| ISSN: | 2148-9955 |
