White Paper: AI Perceptions at the University of Baltimore
dc.contributor.author | Jessica Stansbury | |
dc.contributor.author | Sarah Lausch | |
dc.contributor.author | Nima Zahadat | |
dc.contributor.author | David Kelly | |
dc.contributor.department | Center for Excellence in Learning Teaching and Technology | |
dc.date.accessioned | 2024-06-20T12:46:59Z | |
dc.date.available | 2024-06-20T12:46:59Z | |
dc.date.issued | 2024-02 | |
dc.description.abstract | This white paper presents a comprehensive analysis of the University of Baltimore's AI Perceptions project. Using a data-driven approach this white paper aims to highlight the methods, findings, and implications of this study, demonstrating how data-driven insights can guide the development of AI tools tailored for enhancing teaching and learning experiences. Additionally, the paper underscores UBalt's commitment to faculty and student success, highlighting how such forward-thinking initiatives can significantly contribute to the advancement of educational practices and student outcomes in the modern digital era. Faculty are an important part of student success, and it is critical to consider pedagogical approaches that enhance teaching and learning experiences so that students are equipped with the education, skills, and knowledge to compete in a 21st century workforce that includes generative artificial intelligence. | |
dc.format.extent | 25 | |
dc.genre | white papers | |
dc.identifier | doi:10.13016/m2j8kt-fsb5 | |
dc.identifier.uri | http://hdl.handle.net/11603/34643 | |
dc.language.iso | en_US | |
dc.relation.isAvailableAt | University of Baltimore | |
dc.subject | artificial intelligence | |
dc.subject | perceptions | |
dc.title | White Paper: AI Perceptions at the University of Baltimore | |
dc.type | Text |