An LLM-Driven Chatbot in Higher Education for Databases and Information Systems
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| Title: | An LLM-Driven Chatbot in Higher Education for Databases and Information Systems |
|---|---|
| Language: | English |
| Authors: | Alexander Tobias Neumann (ORCID |
| Source: | IEEE Transactions on Education. 2025 68(1):103-116. |
| Availability: | Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=13 |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Computer Science Education, Databases, Information Systems, Classroom Environment, Learning Management Systems, Self Management, Independent Study, Technology Uses in Education, Artificial Intelligence, Student Attitudes, Usability, User Satisfaction (Information), Influence of Technology, College Students, Higher Education, Natural Language Processing, Computer Software, Intelligent Tutoring Systems |
| DOI: | 10.1109/TE.2024.3467912 |
| ISSN: | 0018-9359 1557-9638 |
| Abstract: | Contribution: This research explores the benefits and challenges of developing, deploying, and evaluating a large language model (LLM) chatbot, MoodleBot, in computer science classroom settings. It highlights the potential of integrating LLMs into LMSs like Moodle to support self-regulated learning (SRL) and help-seeking behavior. Background: Computer science educators face immense challenges incorporating novel tools into LMSs to create a supportive and engaging learning environment. MoodleBot addresses this challenge by offering an interactive platform for both students and teachers. Research Questions: Despite issues like bias, hallucinations, and teachers' and educators' resistance to embracing new (AI) technologies, this research investigates two questions: (RQ1) To what extent do students accept MoodleBot as a valuable tool for learning support? (RQ2) How accurately does MoodleBot churn out responses, and how congruent are these with the established course content? Methodology: This study reviews pedagogical literature on AI-driven chatbots and adopts the retrieval-augmented generation (RAG) approach for MoodleBot's design and data processing. The technology acceptance model (TAM) evaluates user acceptance through constructs like perceived usefulness (PU) and Ease of Use. Forty-six students participated, with 30 completing the TAM questionnaire. Findings: LLM-based chatbots like MoodleBot can significantly improve the teaching and learning process. This study revealed a high accuracy rate (88%) in providing course-related assistance. Positive responses from students attest to the efficacy and applicability of AI-driven educational tools. These findings indicate that educational chatbots are suitable for integration into courses to improve personalized learning and reduce teacher administrative burden, although improvements in automated fact-checking are needed. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1460241 |
| Database: | ERIC |
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