Teaching artificial intelligence through drug-drug interaction clustering analysis: Integrating project-based learning and large language models.
Clinical Snapshot
PICO Framework
| P — Population | Early-stage undergraduate students from non-computing backgrounds |
| I — Intervention | AI course integrating project-based learning with large language models (LLMs) using drug-drug interaction clustering analysis |
| C — Comparator | Traditional AI courses targeting computer science majors |
| O — Outcomes | Development of problem-solving skills, student engagement, and AI literacy |
Bottom Line
This educational intervention proposes an innovative approach to teaching AI concepts to non-computing undergraduates through drug-drug interaction analysis and large language model integration. While the framework addresses an important educational gap and offers a practical methodology, the study lacks rigorous evaluation with validated outcome measures, control groups, and quantitative results. The preliminary nature of the findings limits confidence in effectiveness. For Australian medical educators, this approach could inform curriculum development for AI literacy, particularly relevant given increasing AI applications in healthcare and regulatory environments. However, formal evaluation studies with robust methodology are needed before widespread implementation. The integration of real-world pharmaceutical data analysis provides authentic learning experiences that could enhance student engagement and practical skills development.
Key Findings
P Value: Not reported
Effect Size: Not reported
Primary Outcome: Preliminary feedback indicating support for problem-solving skills development and increased student engagement
Nnt Or Sensitivity: Not applicable - educational intervention without quantitative measures
Confidence Interval: Not reported
Clinical Application
Moderate - requires access to LLM tools and computational resources, plus faculty training in AI applications Relevant to Australian universities implementing AI literacy requirements. Could align with TGA's increasing focus on AI in drug regulation and RACGP's digital health competencies Undergraduate students in health sciences, pharmacy, and related fields seeking AI literacy
Abstract
In recent years, artificial intelligence (AI) has increasingly influenced daily life and scientific research. Traditionally, AI-related courses have targeted computer science majors, while systematic instructional opportunities for early-stage undergraduates from non-computing backgrounds remain limited. To bridge this gap, we developed an AI course that integrates project-based learning with large language models (LLMs). Specifically, we designed four progressive assignments based on our research project (i.e., drug-drug interaction network clustering analysis). The course does not require prior knowledge of pharmacology or programming. Instead, LLMs are used as assistive tools to support programming, data analysis, and result interpretation. Students engage in a complete workflow, including data curation, algorithm implementation, and critical evaluation of results. Preliminary feedback shows that this approach supports the development of problem-solving skills and increases student engagement. This study provides a framework for integrating LLMs into project-based learning. We believe that this teaching proposal will be valuable and inspiring for educators seeking to design or enrich similar courses.
References
- 1.Lv, J., Liu, G., & Zhou, C. (2026). Teaching artificial intelligence through drug-drug interaction clustering analysis: Integrating project-based learning and large language models. PLoS Computational Biology, 22(5), e1014236. https://doi.org/10.1371/journal.pcbi.1014236
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