2026 ASEE Annual Conference & Exposition

Background: With the rapid convergence of artificial intelligence (AI) and life sciences, pharmaceutical education is undergoing a paradigm shift from experience-driven learning to a data- and algorithm-driven mode of knowledge creation. Traditional pharmacy curricula primarily emphasize chemical synthesis, pharmacokinetics, and clinical applications but often lack systematic cultivation of algorithmic thinking and computational reasoning. How to cultivate high-level interdisciplinary professionals capable of applying AI technologies to pharmaceutical research remains a critical and unresolved challenge.
Purpose: This study takes the “AI Pharmacy” Master of Engineering program at Z University in China as a case to explore how algorithmic thinking can be effectively integrated into pharmaceutical education. Focusing on curriculum design, teaching environment, and project-based practice, the study aims to develop a practical and transferable framework for engineering education reform in the era of AI-driven life sciences.
Methods: This research adopts a single-case study approach. First, eight internal and publicly available documents were collected to analyze the program’s curriculum structure and implementation process. Second, five semi-structured interviews were conducted with curriculum designers, teaching administrators, instructors, and students. Third, 85 questionnaires were distributed to current master’s students to collect feedback on teaching effectiveness and learning outcomes, ensuring the comprehensiveness and validity of the findings.
Results: 1) In terms of curriculum design, the program is structured into two progressive modules: Algorithmic Foundations and Pharmaceutical Intelligence. The algorithmic module covers essential topics such as machine learning and data mining, while the pharmaceutical intelligence module includes AI-assisted drug discovery and intelligent clinical decision systems. Algorithmic thinking serves as a “cognitive framework” guiding students to deconstruct complex pharmaceutical problems into data- and model-oriented solutions. 2) Regarding the teaching environment, the program integrates offline facilities such as the Proof-of-Concept Center and Pilot Testing Platform, forming a complete chain from algorithmic modeling to industrial application. Online, it leverages digital learning infrastructures including the Zhihai Mo Platform, AI Online Service Platform, and Virtual Simulation Laboratory, enabling students to remotely access datasets, train AI models, and conduct virtual experiments. 3) For project-based practice, the program collaborates with leading technology enterprises such as Alibaba Cloud, Tencent, and ByteDance through joint laboratories to carry out research projects grounded in real-world pharmaceutical scenarios. Some graduate students directly participate in enterprise co-innovation projects, bridging academic theory and engineering practice. Through a closed-loop process of problem definition-model design-hypothesis validation-algorithm iteration, students internalize algorithmic thinking and develop strong problem-solving capabilities.
Conclusion: Integrating algorithmic thinking into pharmaceutical education requires a comprehensive reconstruction of the learning ecosystem. The success of Z University’s AI Pharmacy program lies in three dimensions: institutionalized interdisciplinary collaboration, infrastructure for immersive teaching, and clinically oriented innovation practice. This integrated approach significantly enhances students’ competencies in data reasoning, cross-domain collaboration, and model-based problem solving. The case demonstrates that engineering-oriented algorithmic literacy is a key driving force for transforming modern pharmaceutical education and provides valuable insights for global educators seeking to integrate AI and life sciences through interdisciplinary engineering pedagogy.