Best Practice: Cross-Disciplinary Collaboration 

Objective: Enhance the benefits of AI across the university by fostering collaboration between service providers, researchers, and educators. 

 Practical Application: Establish partnerships with academic departments, research centers, and other stakeholders to explore and pilot innovative AI applications. These collaborations should aim to identify and pursue opportunities where AI can support educational and research objectives. 

Some examples of stakeholders across the university collaborating to enhance the benefits of AI on campus include: 

  • A developer in a unit develops a chatbot and shares the chatbot with other units.  These other units use the same chatbot, trained with their data, instead of developing their own chatbot, eliminating duplication and saving resources. 
  • Teams collaborating to develop a campus-wide chatbot instead of developing individual chatbots.  
  • When an AI service provider for a project was no longer able to meet milestones, another unit on campus was able to step in and leverage in-house AI expertise to replace the external service provider. 
  • Creating and sharing open data sets and AI models that can facilitate cross-disciplinary research and innovation, such as natural language processing, computer vision, or social network analysis. 

Outcome: The development of multidisciplinary projects and initiatives that leverage AI to enrich the academic and operational ecosystem, fostering innovation and shared knowledge.