Scholarlink

<p>Food microbiology education faces significant challenges including prohibitive costs, safety concerns, and limited accessibility, as traditional laboratory-based instruction requires expensive infrastructure, specialized equipment, and stringent biosafety protocols, creating disparities in educational opportunities particularly for resource-constrained institutions. To address these challenges, this study evaluated the effectiveness of AI-enhanced virtual laboratories compared to traditional laboratory instruction in food microbiology education, examining learning outcomes, student engagement patterns, and cost-effectiveness. A convergent mixed-methods quasi-experimental design was employed involving 128 undergraduate students from food science and engineering and food quality and safety programs over 14 weeks. The AI-enhanced platform incorporated intelligent tutoring systems, automated assessment mechanisms, personalized learning pathways, and real-time feedback delivered through machine learning algorithms and natural language processing, with data collection including pre-post knowledge tests, skill evaluations, engagement surveys, and qualitative interviews. The findings revealed that experimental participants achieved significantly higher knowledge acquisition (86.3% vs. 78.6%, <i>P</i> &lt; 0.001, d = 0.80), superior skill development (84.6% vs. 72.8%, <i>P</i> &lt; 0.001, d = 1.17), and enhanced conceptual understanding with 71.6% misconception reduction compared to 36.4% in control groups. Furthermore, three distinct user interaction profiles emerged—systematic explorers (38.5%), targeted users (46.2%), and minimal adopters (15.4%)—with feature utilization patterns strongly predicting learning outcomes (β = 0.68, <i>P</i> &lt; 0.001). Economic analysis demonstrated 80.9% cost reduction while providing unlimited experimental access. These results demonstrate that AI-enhanced virtual laboratories represent transformative educational tools that personalize learning experiences, accelerate skill acquisition, and democratize access to quality food microbiology education, offering viable solutions for resource-constrained institutions while maintaining pedagogical effectiveness.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-enhanced virtual laboratories improve learning outcomes and student engagement in food microbiology education

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月19日

查看全文
  • Yingliang Ge,
  • Hefei Wang,
  • Dongxue Wang,
  • Bo Yang

摘要

Food microbiology education faces significant challenges including prohibitive costs, safety concerns, and limited accessibility, as traditional laboratory-based instruction requires expensive infrastructure, specialized equipment, and stringent biosafety protocols, creating disparities in educational opportunities particularly for resource-constrained institutions. To address these challenges, this study evaluated the effectiveness of AI-enhanced virtual laboratories compared to traditional laboratory instruction in food microbiology education, examining learning outcomes, student engagement patterns, and cost-effectiveness. A convergent mixed-methods quasi-experimental design was employed involving 128 undergraduate students from food science and engineering and food quality and safety programs over 14 weeks. The AI-enhanced platform incorporated intelligent tutoring systems, automated assessment mechanisms, personalized learning pathways, and real-time feedback delivered through machine learning algorithms and natural language processing, with data collection including pre-post knowledge tests, skill evaluations, engagement surveys, and qualitative interviews. The findings revealed that experimental participants achieved significantly higher knowledge acquisition (86.3% vs. 78.6%, P < 0.001, d = 0.80), superior skill development (84.6% vs. 72.8%, P < 0.001, d = 1.17), and enhanced conceptual understanding with 71.6% misconception reduction compared to 36.4% in control groups. Furthermore, three distinct user interaction profiles emerged—systematic explorers (38.5%), targeted users (46.2%), and minimal adopters (15.4%)—with feature utilization patterns strongly predicting learning outcomes (β = 0.68, P < 0.001). Economic analysis demonstrated 80.9% cost reduction while providing unlimited experimental access. These results demonstrate that AI-enhanced virtual laboratories represent transformative educational tools that personalize learning experiences, accelerate skill acquisition, and democratize access to quality food microbiology education, offering viable solutions for resource-constrained institutions while maintaining pedagogical effectiveness.