FW-HTF-P: Collaborative Research: Artificial Intelligence-Supported Development of Future Organizational Leaders

这项提议的目的是帮助未来的领导力发展专业人士（LDP）在他们的工作中培养多样化的领导者。未来的工作涉及许多职业的虚拟环境。因此，自民党工作人员面临的两个新挑战是培训领导者在虚拟环境中有效地执行任务，以及在培训领导者时利用未来的技术。为了应对这些挑战，我们建议创建一个领导力培训平台，供自民党工作人员使用，以培训下一代多样化的领导者，以适应在线环境。该平台利用人工智能（AI）模型向领导者提供实时反馈。将开发包括可视化和讲故事在内的交互式仪表板作为前端，提供有关领导者绩效的信息，以改善他们的领导信号。拟议的工作将对许多利益相关者产生实际影响。首先，自民党将能够有效地培训现任和新兴领导人在虚拟环境中工作。其次，未来的领导者和他们的追随者将受益于改进的培训。第三，该方法旨在通过创建客观和多样化的培训数据，大幅减少领导力评估中的歧视。该奖项目前的工作范围侧重于建立伙伴关系并进一步发展拟议的工作。该合同将支持拟议人工智能平台（即ldp）最终用户的现场和虚拟会议和观察。将举办领导力和数据科学研讨会，分享和发展想法。ldp将参与培训和建立网络，并提供反馈。将聘请一位顾问来制定项目计划。研究生将协助建立额外的分析模型，并为项目和随后的数据收集创建学习材料（例如，制定实验方案；创建招聘和培训材料）。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The purpose of the proposed effort is to aid future leadership development professionals (LDP) in their work to train a diverse pipeline of leaders. The future of work involves a virtual context for many occupations. Hence, two new challenges for LDP workers are to train leaders to perform effectively in a virtual context and to leverage future technology in their training of leaders. We address these challenges by proposing to create a leadership training platform to be used by LDP workers to train the next generation of diverse leaders to adapt to an online setting. The proposed platform leverages Artificial Intelligence (AI) models to provide real-time feedback to leaders. An interactive dashboard including visualization and storytelling will be developed as the front-end to provide information on leaders’ performance to improve their leadership signals. The proposed work will have practical implications for numerous stakeholders. First, LDP will be able to effectively train current and emerging leaders to work in the virtual setting. Second, future leaders and their followers will benefit from the improved training. Third, the approach aims to substantially reduce discrimination in leadership evaluations by creating objective and diverse training data.The current scope of work within the award is focused on creating partnerships and further developing the proposed work. The award will support on-site and virtual meetings and observations of end users of the proposed AI platform (i.e., LDPs). A leadership and data science workshop will be held to share and develop ideas. LDPs will participate for training and networking purposes and to provide feedback. A consultant will be engaged to develop the project plan. Graduate students will assist in additional analytic model building and the creation of study materials for the project and subsequent data collections (e.g., developing the experimental protocol; creating recruitment and training materials).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.