Course Evaluation Solution

The Vendor is required to provide for course evaluation software.
- Responsible for importing historical course evaluation results with support from the university’s information technology team.
- Responsible for importing historical course evaluation results with support from the University’s Information Technology team. This application will be required to provide reports, integrate with Canvas LMS.
- Application must be compatible with the University’s single sign on authentication system, Microsoft Entra.
- Application and tools associated with this proposal must meet or exceed WCAG 2.1 Level AA accessibility standards.
- Integration
•    File/Data transfer from Ellucian Banner or Canvas LMS in order to populate course and evaluation respondents (student) information.
•    Full Canvas integration, preferably using an LTI.
•    Ability to prompt evaluation respondent in Canvas through a pop-up or similar
•    Ability for respondents to scan a QR code for easy access to the survey/dashboard of surveys
•    Ability to send push notifications via the Canvas mobile app
•    Ability to hijack respondent to surveys if surveys haven’t been completed. Allow respondent to skip until specified amount of time before course end date
- Flexibility
•    Ability to use custom questions and vary those questions by college, major, year, and course modality.
•    When multiple instructors teach a course, offer the option to respond to instructor related questions for a specific instructor
•    Ability to allow instructor to conduct mid-term survey
•    Ability to filter courses that will be offered a survey (e.g. courses with less than x number of students aren’t evaluated, ability to skip an entire department
- AI tools
•    AI tools for interpreting both qualitative and quantitative data.
•    Full campus ownership of created data and assurance data will not be used to train AI models.
•    Provide information on if and how the AI model creates its outputs.
•    Provide information on AI engines/models used (e.g., GPT-4, Claude) as well as model dependence and contingency plan if access changes.
•    Explain how mitigation of AI bias has been identified and addressed, ensuring equitable access and outcomes for all teachers and students.
•    Vendor must disclose by checking the box below whether their AI-powered features interact with or make use of instructional content housed in a school or districts.