REVIEW article
Front. Educ.
Sec. Digital Learning Innovations
Revolutionizing biotech and pharmaceutical education with adaptive learning
Provisionally accepted
- 1Jining Medical University, Jining, China
- 2Chengdu University of Traditional Chinese Medicine School of Pharmacy, Chengdu, China
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Abstract Adaptive learning systems (ALS), powered by artificial intelligence (AI), represent a transformative approach to biotechnological and pharmaceutical education, which address critical limitations of traditional standardized pedagogy. This review highlights empirical evidence demonstrating how ALS dynamically personalize learning through Knowledge state modeling (KSM) and the synergistic integration of knowledge level (KL) and knowledge structure (KS) dimensions. This framework enables mastery-based progression in sequential domains (e.g., genetic engineering, pharmacodynamics), ensuring foundational competency before advancement. In addition, key applications of AL in the field of biological and pharmaceutical education are also detailed including scaffolding complex foundational sciences (e.g., real-time misconception detection in CRISPR-Cas9), enhancing technical skills via AI-driven virtual labs simulating industry workflows (e.g., HPLC, bioreactors), and navigating regulatory compliance through contextual simulations. Documented benefits include significant cost reduction, accelerated skill acquisition, and strengthened industry alignment. Nevertheless, challenges persist in technical fragmentation, algorithmic bias, and equitable resource access. Finally, it is suggested that the future research priorities should involve developing integrated architectures with blockchain-secured micro-credentials, human-AI synergy frameworks for ethical oversight, and equity-driven deployment via federated edge learning. Strategic implementation of ALS promises to cultivate a globally competitive, interdisciplinary workforce for next-generation biopharmaceutical innovation while establishing rigorous, regulatory-grade training.
Keywords: Adaptive learning systems, knowledge state modeling, competency-based learning, AI in pharmaceutical education, biopharmaceutical training, Interdisciplinary education
Received: 06 Aug 2025; Accepted: 31 Oct 2025.
Copyright: © 2025 Wang, Xu and LIU. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tao Wang, tao-wang@zju.edu.cn
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.