Integrating Large Language Models in Foundational University Courses: A Teaching Reform Approach
The rapid advancement of Large Language Models (LLMs) presents both challenges and opportunities for traditional teaching methodologies. This study explores how higher education can integrate the "lifelong learning" philosophy emphasized in engineering education accreditation in the LLM era. We propose the introduction of a foundational course titled "LLM-assisted Essential Software Applications" to enhance students' competencies and improve teaching effectiveness. Findings indicate that implementing this interdisciplinary course uniquely bridges students' skill gaps by explicitly integrating essential software tools and Large Language Models (LLMs). This approach not only significantly enhances academic writing, data analysis, and visualization skills but also equips students with practical strategies for lifelong learning, aligning closely with modern engineering education accreditation requirements. This study offers new insights and practical guidance for curriculum reform in universities. Furthermore, we provoke discussion on whether software-related teaching should persist amid the evolving capabilities of LLMs.
Authors
- Chunpeng Zhang (ORCID: https://orcid.org/0000-0001-6497-5657)
- Touqeer Premy
Institutions
- Jilin University (CN)
- Jilin Agricultural University (CN)
- Changchun University (CN)
Publication Details
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1109/cste64638.2025.11092138
- Citations
- 1
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 1.69
Funders
- Jilin University