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

University Of North Carolina At Charlotte, Charlotte NC

Investigators

Abstract

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.