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

Wei Ke; Dai Qian; Zheng Zhi

doi:doi:10.11648/j.sr.20261404.17

Research Article |

| Peer-Reviewed

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

Wei Ke, Dai Qian, Zheng Zhi^*

Published in Science Research (Volume 14, Issue 4)

Received: 31 May 2026 Accepted: 14 July 2026 Published: 13 August 2026

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Abstract

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.

Published in	Science Research (Volume 14, Issue 4)
DOI	10.11648/j.sr.20261404.17
Page(s)	186-191
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Artificial Intelligence, Toxicology, Teaching Reform, Virtual Simulation, Medical Education

1．引言

毒理学作为公共卫生与预防医学领域的重要组成部分，是研究外源性因素对生物体健康影响及其作用规律的基础学科，涉及分子生物学、药理学、环境科学等多学科知识，涵盖毒物作用机制、安全性评价、风险评估及环境健康等多个领域，具有较强的综合性和抽象性，传统教学模式在知识体系整合、案例动态更新以及实验实践开展等方面仍存在一定不足，学生难以系统掌握毒理学知识并形成实践能力

[1]

。特别是在安全性评价教学中，动物实验周期长、成本高且具有一定风险，导致学生难以完整参与实验设计、毒性观察及结果分析全过程，从而影响实践能力的培养

[2]

。

现代毒理学研究已从传统单一实验评价模式向多层次、多维度综合分析方向发展，实现保障人群健康为核心目的，通过整合体外实验、动物模型、人群流行病学调查及多组学技术，系统解析化学物质、生物因素及环境暴露对机体产生的不良影响及机制，并结合风险评价方法为相关管理标准制定和安全应用提供科学依据，最终确保工业化学品、生物医药、化妆品、农药类等安全应用

[3]

。

后疫情时代，传统毒理学教学在案例资源更新、前沿知识融入以及理论与实践衔接方面逐渐暴露出不足，，由于毒理学涉及大量抽象概念和复杂评价流程，学生在学习过程中容易出现知识体系碎片化、理解困难以及实践应用能力不足等问题，传统教学模式已难以完全满足新时代医学人才培养需求。近年来，国家高度重视教育领域的数字化转型。《教育强国建设规划纲要（2024—2035年）》强调到2035年建成教育强国，并对推动教育体系高质量发展作出全面系统部署

[4]

。教育部2025年发布《关于加快推进教育数字化的意见》，强调要将人工智能（Artificial Intelligence，AI）等新兴技术融入教育教学的全要素全过程，积极推动科技教育深度融合，从而建设高校智慧课程，全面提高教育教学效率和质量

[5]

。AI已作为新时代信息科技和产业革命的重要驱动力量。AI结合知识图谱、自然语言处理（NLP）、虚拟现实（VR）及机器学习等技术，可实现智能答疑、个性化学习、虚拟实验及数据分析等功能，为毒理学教学改革提供新的思路。当前，AI辅助医学教学已在影像学、药学及生物学等领域开展探索，然而，针对毒理学课程特点，将AI技术系统应用于安全性评价教学、实验训练及能力培养方面的研究仍相对有限。

因此，本研究基于“教师-AI-学生”三元教学模式，探索AI技术在毒理学课程中的教学实践与应用效果，以期为医学教育数字化改革及高素质公共卫生人才培养提供参考。

2．传统毒理学教学存在的问题

毒理学课程具有明显的交叉学科特征，教学内容涵盖基础医学、药理学、环境卫生学及分子生物学等多个学科知识，涉及无可观察到不良反应剂量NOAEL、最低可观察到不良反应剂量LOAEL、剂量-反应关系及风险评估等复杂理论内容，相关知识涉及复杂的作用机制和评价体系，学生若缺乏前置学科基础，容易出现知识理解困难及“知识孤岛”现象。传统教学主要依赖课堂讲授和教师单向知识传递，教学形式较为单一，学生在学习过程中主动探究和问题分析的机会有限，导致其缺乏主动分析与综合应用能力。

此外，毒理学实验教学受实验周期、实验动物及实验条件限制较大。例如急性毒性试验、亚慢性毒性试验及慢性毒性试验往往需要较长观察时间，学生难以在有限课程时间内完整经历实验方案设计、动物处理、毒性指标检测以及结果评价等全过程。另外，实验动物管理要求、伦理规范以及教学资源配置等因素也会进一步限制了学生实践操作机会，使理论学习与实验技能训练之间存在一定脱节。现有毒理学教学模式还难以满足当前公共卫生人才培养需求。公共卫生风险因素呈现多样化发展趋势，突发事件和新型化学暴露问题对医学人才培养提出了新的挑战。未来公共卫生专业人才不仅需要具备系统的毒理学知识体系，还应能够利用数据分析方法开展风险识别，并具备科学评价和解决复杂健康问题的能力。但目前以知识传授为核心的传统教学方式，在培养学生自主探究能力、科研创新意识及职业实践能力方面仍存在局限。

[6]

。因此，探索智能化、实践化的新型毒理学教学模式具有现实意义。后新冠疫情时代，全球疾病风险因素更加复杂，人类疾病谱与死亡谱的演变加剧了毒理学领域发病因素的不确定性。这对新医学背景下高素质医药专业人才的知识、能力与素质提出新的要求，高校新型学科建设与教学方案改革迫在眉睫。

当前，新一轮科技革命和产业变革迅猛发展，人工智能进入新一轮技术爆发期，我国将踏进信息化迈向数字化、网络化、智能化全面跃升的新阶段，高校智能信息化结合的教学改革方案可能更符合后疫情时代高素质医学人才培养，解决传统毒理学教学局限性，深化新医时代教学模式改革。在此背景下，新型毒理教学方案对今后培养具备创新、批判、应用能力的高职业素质、决策型医药人才的具有重要的实践意义。

3．AI赋能毒理学教学的优势

AI是一类利用计算机算法实现知识学习、信息处理和智能决策的技术体系，核心技术包括自然语言处理（Natural Language Processing, NLP）、机器学习（Machine Learning, ML）以及深度学习等。近年来，AI不断向教育领域渗透，为教学资源整合、学习过程优化以及教学评价方式创新提供了新的技术支撑。在教育领域，基于AI构建的智能教学平台能够辅助教师开展课程设计、知识管理和学生学习反馈分析，同时通过学习行为识别和资源推荐等方式，为学生提供更加符合个体需求的学习支持，促进教学模式由传统知识传递向智能化、交互式学习转变

[7]

。高校人才培养模式中，AI已用于个性化学习路径规划和虚拟实验教学平台。例如，在医学影像学，AI结合教学能辅助诊断系统可以帮助学生快速识别病灶，提高影像判读的准确率和效率；在药学，AI模拟技术能够呈现药物作用机制和体内过程，增强学生对抽象理论知识的理解；在生物学，AI可以创建虚拟人体模型，让学生更深入地了解人体各系统的功能与调控

[8]

。这些AI结合的教学模式显著提高了教学效果，但其实践和应用、人才培养的效果处于探索阶段，推广应用仍需要更多的教学案例。

AI技术能够通过知识图谱、虚拟仿真及数据分析等方式提升毒理学教学效果。基于AI构建的毒理学知识图谱可动态整合教材、文献及数据库信息，将毒物作用机制、分子靶点及风险评估等内容进行结构化关联，帮助学生建立系统化知识框架

[9]

。

在实验教学中，AI结合VR/AR技术能够构建虚拟毒理学实验环境，模拟动物灌胃、给药、毒性观察及解剖分析等操作过程。学生可在安全环境中重复练习实验操作，提高实验参与度并降低实验风险。

在毒性评价教学中，计算模型与数据库资源可被设计为数据驱动型学习任务，用于训练学生的毒性预测和结果解释能力。以药物心脏毒性教学为例，学生可借助定量构效关系（quantitative structure-activity relationship，QSAR）模型分析化合物结构与毒性效应之间的关系，并结合分子对接方法判断其与相关靶点的潜在相互作用。同时，学生还可利用CompTox等毒理学数据库获取化合物理化性质、生物活性及毒性通路等信息，对预测结果进行综合分析。该过程有助于学生从被动接受毒理学结论转向基于数据证据的机制推断和风险判断，从而提升其数据处理能力、科研思维和综合应用能力。

此外，AI支持的教学系统能够记录学生的学习过程、知识掌握情况和任务完成表现，并据此生成个性化反馈。系统可针对学生在概念理解、实验流程或数据分析中的薄弱环节推送相应学习资源和训练任务，使教学过程更加符合学生个体差异。与传统统一化教学方式相比，该模式有助于提高学习效率，增强教学干预的精准性和针对性

[10]

。

4．AI结合毒理学教学实践与效果评价

4.1．AI教学模式构建

本研究基于“教师-AI-学生”三元教学结构开展毒理学教学实践（图1）。教师负责课程设计与教学引导，AI系统负责知识图谱构建、案例整合及虚拟实验支持，学生通过交互式学习完成知识理解与实验操作（图2）。教学内容主要包括：AI毒理学知识图谱构建、VR虚拟安全性评价实验、QSAR毒性预测与风险评估训练以及CompTox数据库检索与数据分析。

AI系统利用自然语言处理（NLP）技术整合教材、文献数据库、毒理案例及预测软件等资源，自动提取NOAEL、LOAEL、毒作用机制等关键知识点，并建立动态知识关联网络，帮助学生系统理解毒理学机制及风险评估流程。同时，AI可根据学生学习行为和实验操作情况，动态推荐个性化学习资源，提高学习效率与知识掌握程度

[11]

。

在实验教学中，结合VR/AR技术构建虚拟毒理学实验室，模拟动物给药、毒性观察、解剖分析及结果判定等操作流程。学生能够在安全环境中反复进行实验训练，降低传统实验教学中实验周期长、风险高及资源受限等问题，提高实验参与感与实践能力。

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Figure 1. 图1 AI+毒理学为基础的“教师-AI-学生”三元结构。

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Figure 2. 图2 AI结合毒理学教学方案四个实施步骤。

4.2．教学实施过程

选取某高校公共卫生专业101名学生作为研究对象，其中传统教学组50人，AI教学组51人。传统组采用PPT理论授课及常规实验教学；AI组采用AI知识图谱、虚拟实验及案例分析等教学方式。课程总学时为16学时。

AI教学组以2020年后典型毒理学案例为基础开展教学，包括公共卫生风险评估、药物毒性预测及环境暴露分析等内容。学生在虚拟实验环境中完成急性毒性试验、毒性观察及结果分析等操作，并利用QSAR模型及CompTox数据库开展毒性预测与风险评估训练（图3）

[12-14]

。AI系统可实时跟踪学生实验操作流程，并根据实验规范提供即时反馈，辅助学生完成数据分析与实验结果判定。

课程结束后，通过调查问卷比较两组学生在学习兴趣、知识理解度、实验参与度及课程满意度等方面的差异。结果显示，AI教学组在学习兴趣、知识理解度、实验参与度及课程满意度等方面均明显优于传统教学组（P＜0.05）。学生普遍认为，AI知识图谱能够帮助其理解复杂毒理学机制及知识点间的关联关系，VR虚拟实验则提高了实验参与感和实践操作能力

[15]

。

表1显示，两组学生在教学效果方面存在明显差异。AI教学组学生在实验流程掌握、毒理机制分析及案例综合应用方面表现更好，而传统教学组学生在安全性评价知识理解及实验操作过程中仍存在一定困难，部分学生对知识点关联性掌握不足，实验结果差异较大。

表1 两组学生教学效果比较。

指标	传统教学组（n=50）	AI教学组（n=51）	P值
学习兴趣满意度	较低	较高	＜0.05
知识理解程度	一般	明显提高	＜0.05
实验参与度	一般	明显提高	＜0.05
课程总体满意度	一般	明显提高	＜0.05

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Figure 3. 图3 绘制毒理学经典案例（3个）的教学教案图谱。

5．讨论与建议

AI在毒理学教学中具有较大拓展空间，但其在实际推广过程中仍面临数据质量、模型可靠性及教学规范等问题。AI生成内容并非完全可靠，可能存在知识更新滞后、结论表述不准确或专业术语理解不足等情况，教师需对AI输出内容进行筛选、判断，这对教师的数字素养、智能工具应用能力与职业素养提出了更高要求。高校应通过专题培训、教学案例建设以及评价机制完善等方式，加强教师AI技术培训及教学评价体系建设，推动教师角色由知识传授向教学设计与学习引导转变。

AI素养教育应融入毒理学课程体系，形成多学科交叉的教学模式，提高学生的数据分析能力、风险评估能力及复杂问题解决能力。同时，教师应鼓励学生在学习过程中主动辨别AI生成内容中的错误信息，通过开放讨论提升学生的批判性思维能力和科学判断能力。

此外，AI教学涉及学生学习数据、个人信息及教学资源共享等问题，可能带来隐私保护、教育公平及责任归属等伦理风险。因此，高校需建立完善的AI教学管理制度，加强AI伦理与数据安全教育，保障学生信息安全与合理使用

[16]

。

虽然AI技术为医学教育模式创新提供了新的可能，但其本质仍应服务于以学生为中心的教学目标和促进学生能力发展基础之上。未来毒理学教学改革需要合理平衡AI技术应用与传统教学优势，在发挥AI技术优势的同时，进一步强化师生之间的互动交流和实践指导。通过融合智能化学习工具、专业教师引导以及医学人文教育培养学生的团队合作能力、实践应用能力与职业责任素养，从而实现智能化教学与医学人文教育的协同发展。

6．总结

随着AI生成式大模型技术快速迭代，我国各地区教育单位会呈现高科技信息化发展的新阶段、新趋势、新动能，新引擎，这会加速建设共生、共学、共研、共创的未来智能教育新格局。我们认为AI结合毒理学教学改革方案，符合2025年教育部发布《关于加快推进教育数字化的意见》，发挥其独特优势，有助于加强AI医学课程融合与创新教学体系建立，显著提升公共卫生人才教育教学效率和质量，实现高层次公共卫生与预防医学人才就业技能链的耦合，促进高质量充分就业，满足社会需求和相关行业发展。本研究初期实践和探索结果，为后疫情时代，符合我国信息化发展高素质医学人才培养教学改革深化，提供参考依据。

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APA Style

Ke, W., Qian, D., Zhi, Z. (2026). Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform. Science Research, 14(4), 186-191. https://doi.org/10.11648/j.sr.20261404.17

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ACS Style

Ke, W.; Qian, D.; Zhi, Z. Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform. Sci. Res. 2026, 14(4), 186-191. doi: 10.11648/j.sr.20261404.17

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Ke W, Qian D, Zhi Z. Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform. Sci Res. 2026;14(4):186-191. doi: 10.11648/j.sr.20261404.17

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@article{10.11648/j.sr.20261404.17,
  author = {Wei Ke and Dai Qian and Zheng Zhi},
  title = {Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform},
  journal = {Science Research},
  volume = {14},
  number = {4},
  pages = {186-191},
  doi = {10.11648/j.sr.20261404.17},
  url = {https://doi.org/10.11648/j.sr.20261404.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sr.20261404.17},
  abstract = {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.},
 year = {2026}
}

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TY  - JOUR
T1  - Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform
AU  - Wei Ke
AU  - Dai Qian
AU  - Zheng Zhi
Y1  - 2026/08/13
PY  - 2026
N1  - https://doi.org/10.11648/j.sr.20261404.17
DO  - 10.11648/j.sr.20261404.17
T2  - Science Research
JF  - Science Research
JO  - Science Research
SP  - 186
EP  - 191
PB  - Science Publishing Group
SN  - 2329-0927
UR  - https://doi.org/10.11648/j.sr.20261404.17
AB  - 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.
VL  - 14
IS  - 4
ER  -

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Author Information

Wei Ke

School of Public Health, Henan Medical University, Xinxiang, China; Xinxiang Key Laboratory of Applied Microbial Technology, Xinxiang, China

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Dai Qian

School of Public Health, Henan Medical University, Xinxiang, China; Xinxiang Key Laboratory of Applied Microbial Technology, Xinxiang, China

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Zheng Zhi

School of Public Health, Henan Medical University, Xinxiang, China; Xinxiang Key Laboratory of Applied Microbial Technology, Xinxiang, China; The First Affiliated Hospital of Henan Medical University, Weihui, China

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Figure 1

Figure 1. 图 1 AI+毒理学为基础的“教师-AI-学生”三元结构。
Figure 2

Figure 2. 图 2 AI结合毒理学教学方案四个实施步骤。
Figure 3

Figure 3. 图 3 绘制毒理学经典案例（3个）的教学教案图谱。

Table 1

表1 两组学生教学效果比较。

Plain Text BibTeX RIS

APA Style

Ke, W., Qian, D., Zhi, Z. (2026). Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform. Science Research, 14(4), 186-191. https://doi.org/10.11648/j.sr.20261404.17

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ACS Style

Ke, W.; Qian, D.; Zhi, Z. Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform. Sci. Res. 2026, 14(4), 186-191. doi: 10.11648/j.sr.20261404.17

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AMA Style

Ke W, Qian D, Zhi Z. Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform. Sci Res. 2026;14(4):186-191. doi: 10.11648/j.sr.20261404.17

Copy | Download

@article{10.11648/j.sr.20261404.17,
  author = {Wei Ke and Dai Qian and Zheng Zhi},
  title = {Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform},
  journal = {Science Research},
  volume = {14},
  number = {4},
  pages = {186-191},
  doi = {10.11648/j.sr.20261404.17},
  url = {https://doi.org/10.11648/j.sr.20261404.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sr.20261404.17},
  abstract = {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.},
 year = {2026}
}

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TY  - JOUR
T1  - Artificial Intelligence Enabled Reform of Vocational Competency Teaching in Toxicology Under Healthcare Reform
AU  - Wei Ke
AU  - Dai Qian
AU  - Zheng Zhi
Y1  - 2026/08/13
PY  - 2026
N1  - https://doi.org/10.11648/j.sr.20261404.17
DO  - 10.11648/j.sr.20261404.17
T2  - Science Research
JF  - Science Research
JO  - Science Research
SP  - 186
EP  - 191
PB  - Science Publishing Group
SN  - 2329-0927
UR  - https://doi.org/10.11648/j.sr.20261404.17
AB  - 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.
VL  - 14
IS  - 4
ER  -

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