How our Voice-AI reflections works
Guided conversations help students process their co-op, internship, or work-integrated learning at key moments throughout their experience.
Staged touch points: Pre, mid, and final reflections create a continuous development arc - not a one-time checkbox
Competency tracking: Measurable growth across NACE-aligned or custom competencies
Early signal detection: Challenges surface in conversation, allowing staff to intervene before small issues derail placements
Conversation Structure
A. Setting a Baseline: This first call helps you think about what you expect before starting your co-op. You will be asked about what you hope to learn, what skills you want to build, and what goals you are setting for yourself. This call captures your starting point.
B. Progress Check-in(s): This call is a quick check-in during your co-op. You will talk about what is going well, what has been challenging, and what you are learning so far. This is a chance to pause and reflect while you are still in the experience.
C. Final Check-in & Wrap-up: This final call helps you look back on your co-op experience as a whole. You will reflect on what you learned, how your skills changed, and whether you met the goals you set at the beginning. This call helps you put your experience into words and prepare for what comes next.
Feedback and Analytics
Student Satisfaction Snapshot: Summarizes reported satisfaction levels with the experience and reflection process, grouped into positive, neutral, and negative sentiment bands based on student self-reports.
Competency Progress Overview: Displays how students self-assessed progress across assigned competencies, showing distribution of scores and areas of strongest and weakest perceived growth.
Goal Progress Tracking: Shows progress against goals set in previous sessions
Purpose Alignment: Indicates whether students believe their experience is moving them toward their stated purpose, including signals where purpose may need refinement or support.
Flagged Reflections & Risk Signals: Surfaces high- and low-priority flagged comments extracted from student reflections, enabling early identification of concern, disengagement, or support needs.