DOI: 10.11648/j.sr.20261404.17 ISSN: 2329-0927

Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform

Wei Ke, Dai Qian, Zheng Zhi
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Objective: This study aimed to investigate the application effectiveness of artificial intelligence (AI)-supported teaching reform in toxicology courses and to provide practical insights for cultivating public health professionals under the background of emerging medical education transformation. Methods: A “teacher–AI–student” collaborative teaching framework was developed and implemented in toxicology education. The AI-assisted teaching system integrated a toxicology knowledge graph, virtual simulation laboratory, and toxicity prediction learning modules to support safety evaluation education. A total of 101 undergraduate students were enrolled in this teaching practice and were divided into a traditional teaching group ( n =50) and an AI-assisted teaching group ( n =51). Both groups received a 16-hour toxicology course. Learning interest, knowledge comprehension, experimental engagement, and overall course satisfaction were evaluated through questionnaire surveys, and the differences between the two groups were statistically analyzed.. Results: Compared with the traditional teaching group, students receiving AI-assisted instruction showed significantly improved performance in toxicological knowledge understanding, experimental participation, and course satisfaction ( P <0.05). The integration of AI-based knowledge mapping and virtual reality (VR) simulation experiments enhanced students’ comprehension of complex toxicological mechanisms and safety evaluation procedures. Furthermore, the interactive learning environment promoted students’ autonomous learning behaviors and practical application abilities. Conclusion: AI-supported toxicology teaching provides an effective approach for addressing the limitations of conventional teaching methods, including the separation between theoretical knowledge and practical training and the restriction of experimental resources. The proposed teaching model may facilitate the digital transformation of medical education and contribute to the development of high-quality public health professionals with stronger analytical and practical competencies.

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