The vendor is required to provide a artificial intelligence academic sales training software that include: - Integration capabilities with third-party identity providers. - Compliance with recognized standards (e.g., NIST, FIDO). - Explain all authentication methods for administrative access. Include password policies, SSO support, and any additional security layers or protocols. - Describe the process for creating and presenting a user portal that allows users to request and/or assign addresses. Include: • Steps involved in portal setup and configuration. • Customization options for branding and user interface. • Workflow capabilities for request and approval processes. • Security measures and authentication methods for portal access. • Integration options with existing systems or directories. - Detail all options for importing configuration data from other systems or vendors. Include supported formats, migration tools, and any limitations or prerequisites. - Explain how RBAC is implemented. Include: • Levels of granularity (e.g., user, group, role). • Customization options for roles and permissions. • Any predefined roles or templates available. - Describe how AI is incorporated into your solution. Include: • Types of AI used (e.g., NLP, Machine Learning, Generative AI). • Specific functions supported by each AI type. • How models are trained, updated, and monitored for bias or ethical compliance. - Provide detailed specifications for physical requirements (rack space, power, cooling). - List all connectivity needs (e.g., Ethernet, fiber, speed requirements). Include redundancy options and compatibility with common standards. - Explain how APIs are used in your solution. Include: • Available endpoints and supported functions (e.g., monitoring, reporting) • Authentication and security measures for API calls. • Availability of documentation and developer support" • Describe how your solution integrates with ICON (Canvas), including authentication method (HawkID SSO vs separate login), gradebook synchronization, and any technical requirements for linking AI engagement scores to course assessments - Specify the typical lead time required to assemble a professional services team for deployment. Include factors that may affect scheduling and any expedited options. - Describe whether your solution offers a pilot or test period (e.g., one semester) Include: Duration and scope of the pilot period. - Explain data retention policies and configurability. Include default retention periods, options for extension, and compatibility with external storage solutions. - Specify the maximum number of interactions (e.g., simulations, quizzes) supported per student per course or semester. Include any limits or scalability options. - Describe whether faculty can create content using AI (e.g., conversational input). If not, explain the process and timeline for company-assisted content creation. Include any tools or templates provided. - Explain how grading of AI-driven interactions can be linked to institutional gradebooks. Include integration methods, automation options, and supported LMS platforms. - Describe avatar capabilities (animated vs. audio-only). Include customization options for appearance, gestures, and voice. - Specify how long scoring data is retained and whether retention is configurable. Include any export options for archival purposes. - Describe how licenses are structured (e.g., per student, per semester). Include whether licenses can be transferred between terms and any volume discount options. - Are there license add-ons that require more tokens, for example, or are the licenses all-inclusive of all features? - Supplier to address if a student "license" must be a named user or if it can be a concurrent user license, like if a student drops out of a course, would another student be able to take up that license? - Explain billing process Include flexibility, invoicing details, and any integration with university billing systems - Identify how long a student may access the software over their entire time at UI regardless of how many courses they may access this software for while a student.
Timeline
RFP Posted Date:Saturday, 31 Jan, 2026
Proposal Meeting/
Conference Date:
NA
NA
Deadline for
Questions/inquiries:
Sunday, 08 Feb, 2026