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Empowering Women Across Generations: AI-Enhanced Learning for Inclusive Leadership Development

Author

Listed:
  • Lina Shouman

    (Management Information Systems Department, School of Business, The Lebanese International University, Beirut P.O. Box 146404, Lebanon)

  • Antoni Vidal-Suñé

    (Business Management Department, Faculty of Business and Economics, Universitat Rovira i Virgili, 43204 Reus, Spain)

  • Amado Alarcón Alarcón

    (Business Management Department, Faculty of Business and Economics, Universitat Rovira i Virgili, 43204 Reus, Spain)

Abstract

Women are essential to the growth of any progressive society. Equal access to employment is necessary for both women’s empowerment and global economic growth. However, discrimination against women persists at all career levels, making it challenging for them to overcome barriers to leadership such as access to personalized learning opportunities, mentorship, and career development programs. In addition, women leaders must navigate intergenerational change and transform existing systems to ensure that the experiences of all women within the organization are visible and valued, regardless of age. With different generations working together, the integration of Artificial Intelligence (AI) into leadership training presents revolutionary prospects for enhancing the skillsets of women leaders, especially in historically male-dominated industries. This article provides evidence of how AI-generated content can help women leaders bridge intergenerational gaps and create a more collaborative environment. Furthermore, we present a practical framework that women managers can implement to foster strong intergenerational connections by leveraging AI in pursuit of inclusive leadership and gender equity.

Suggested Citation

  • Lina Shouman & Antoni Vidal-Suñé & Amado Alarcón Alarcón, 2026. "Empowering Women Across Generations: AI-Enhanced Learning for Inclusive Leadership Development," Administrative Sciences, MDPI, vol. 16(2), pages 1-20, February.
  • Handle: RePEc:gam:jadmsc:v:16:y:2026:i:2:p:93-:d:1862001
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