Carahsoft, in conjunction with its vendor partners, sponsors hundreds of events each year, ranging from webcasts and tradeshows to executive roundtables and technology forums.

Government Events and Resources

Events

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Atlassian

Government Customer Experience and Engagement Summit


Event Date: June 22, 2022
Hosted By: FCW, Atlassian & Carahsoft

For nearly thirty years, state and local governments have been working to improve the delivery of their services as well as internal and external communication. The Biden Administration has placed a focus on customer experience. These strategic objectives are designed for agencies to zero in on modernizing infrastructure and digital delivery. 

During this hybrid event, FCW fostered conversations as government and industry experts shared various success stories and explored how technology can aid in the development of digital delivery. 

Attendees learned how to:

  • Increase the use of self-service options
  • Manage digital services across multiple delivery channels
  • Gather the data from customer interactions to improve future performance

Resources


Harnessing Artificial Intelligence and Data for Education
Resources

Harnessing Artificial Intelligence and Data for Education

The higher education sector harnesses Artificial Intelligence (AI) to enhance accessibility and personalization. Predictive insights driven by data facilitate improvements in areas such as student retention, learning progress tracking, and student group segmentation. Quantiphi provides tailored solutions across segments like higher education, e-learning, and education administration. Key offerings include Student 360, gathering student data for analysis and machine learning models, QDox for Intelligent Document Processing automating data extraction from student documents, and an AI-powered Contact Center addressing student queries 24/7. An illustrative customer story involves Quantiphi's use of deep learning to build knowledge-tracing models, elevating student learning outcomes through personalized content recommendations based on factors like learning ability and exercise difficulty.


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