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Related Experiment Video

Updated: May 15, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Teaching artificial intelligence through drug-drug interaction clustering analysis: Integrating project-based

Ji Lv1, Guixia Liu2,3, Changjun Zhou1

  • 1School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China.

Plos Computational Biology
|May 13, 2026
PubMed
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This study introduces a project-based artificial intelligence (AI) course for non-computing undergraduates, using large language models (LLMs) to aid programming and analysis. The innovative approach enhances problem-solving skills and student engagement in AI research.

Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Computational Science

Background:

  • Artificial intelligence (AI) is increasingly integrated into daily life and scientific research.
  • Existing AI education often targets computer science majors, limiting access for undergraduates from non-computing backgrounds.
  • A need exists for accessible AI instruction for early-stage undergraduates from diverse academic fields.

Purpose of the Study:

  • To develop and evaluate an AI course for non-computing undergraduates.
  • To integrate project-based learning (PBL) with large language models (LLMs) for AI education.
  • To provide a framework for using LLMs as assistive tools in an undergraduate AI curriculum.

Main Methods:

  • Designed a project-based AI course centered on drug-drug interaction network clustering analysis.

Related Experiment Videos

Last Updated: May 15, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

  • Developed four progressive assignments utilizing LLMs to support programming, data analysis, and interpretation.
  • Students engaged in a full data science workflow without requiring prior pharmacology or programming knowledge.

    • Main Results:

    • Preliminary feedback indicates the course enhances problem-solving abilities.
    • The project-based approach with LLM integration significantly increases student engagement.
    • The curriculum successfully supports students through data curation, algorithm implementation, and result evaluation.

    Conclusions:

    • The study presents a viable framework for integrating LLMs into PBL for AI education.
    • This teaching model is valuable for educators aiming to create or enhance AI courses for diverse student populations.
    • The approach demonstrates the potential of LLMs to democratize AI education and foster interdisciplinary learning.