Scholarlink

Research examines how AI systems affect skill growth, gender influences, and institutional backing inside learning environments. An analysis of key features through clustering techniques divided participants into two distinct groups based on their responses to AI Platform Accessibility and Navigation (AIPAN), Support for Growth in AI Integration (SGIAI), Training Program Alignment (TPA), E-learning Platform Integration (EPI), Women's Engagement in Training (WET) and Institutional Support for Women (ISW). The participants showing stronger feature involvement belonged to Cluster 0, yet Cluster 1 members demonstrated less engagement. Female participants in Cluster 0 displayed the maximum average scores on all dimensions, earning better results than male participants in TPA (3.43) and EPI (3.40). The cluster groups achieved meaningful separation based on the Silhouette Score value 0.398. Results show that gender and institutional backing determine how people adopt AI training programs and boost their AI skills. The research introduces distinct recommendations to develop tailored AI training sessions and strengthen institutional support functions, focusing specifically on female empowerment to eliminate present skill discrepancies and build future professional talents.

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

Exploring Gender-Based Differences in Job Training and the Application of AI-Clustering Machine Learning in Identifying Women's Training Needs

  • 章节

  • 发表日期：

    2026年

查看全文
  • Boumedyen Shannaq,
  • Said Alrawahi,
  • Oualid Ali

摘要

Research examines how AI systems affect skill growth, gender influences, and institutional backing inside learning environments. An analysis of key features through clustering techniques divided participants into two distinct groups based on their responses to AI Platform Accessibility and Navigation (AIPAN), Support for Growth in AI Integration (SGIAI), Training Program Alignment (TPA), E-learning Platform Integration (EPI), Women's Engagement in Training (WET) and Institutional Support for Women (ISW). The participants showing stronger feature involvement belonged to Cluster 0, yet Cluster 1 members demonstrated less engagement. Female participants in Cluster 0 displayed the maximum average scores on all dimensions, earning better results than male participants in TPA (3.43) and EPI (3.40). The cluster groups achieved meaningful separation based on the Silhouette Score value 0.398. Results show that gender and institutional backing determine how people adopt AI training programs and boost their AI skills. The research introduces distinct recommendations to develop tailored AI training sessions and strengthen institutional support functions, focusing specifically on female empowerment to eliminate present skill discrepancies and build future professional talents.