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What if AI could help higher education work smarter, not just faster—building systems that scale care rather than erode it?
AI for Student Success Operations: Systems that Scale Care is a self-paced, asynchronous course for the people who keep students moving through complex institutional pathways. Grounded in systems thinking, equity-centered design, and organizational change, it invites participants to map the student success ecosystem, from outreach to graduation, and identify where AI can meaningfully augment (not replace) human judgment and connection.
Through practice-based vignettes, scenario work, and applied design sprints, participants experiment with AI for communication, workflow design, and data storytelling—always with attention to transparency, ethics, and cultural responsiveness. Each module builds toward a responsible AI operations roadmap that includes ecosystem maps, revised workflows, governance considerations, and practical next steps for cross-functional teams who want to align operational excellence with student-centered values.
LEARNING OBJECTIVES
Upon successful completion of this course, participants will be able to:
- Analyze the student journey and identify touchpoints where AI can enhance support while maintaining human connection.
- Create AI-assisted communications and workflows that balance efficiency with empathy, accessibility, and cultural sensitivity.
- Translate institutional data into actionable insights through AI-supported analysis and narrative storytelling.
- Design cross-functional collaboration systems and knowledge repositories that leverage AI for organizational learning.
- Lead strategic conversations about AI governance, ethics, and sustainable innovation in higher education operations.
SERVICE DELIVERABLES
Participants and institutions receive:
- Access to a six-module, self-paced eLearning course (plus orientation) focused on AI for student success operations and systems thinking.
- A Student Success Ecosystem Mapping Toolkit to visualize roles, systems, and student touchpoints across the institution.
- Templates and examples for AI-assisted communication and outreach workflows (e.g., campaigns, nudges, and follow-up protocols).
- A Workflow and Process Design Canvas for identifying where automation can free time for higher-impact, relational work.
- A Data Storytelling Brief and prompts for turning AI-supported analyses into narratives that drive student-centered decisions.
- A Collaboration and Knowledge-Sharing Playbook to support cross-unit alignment around AI-enabled processes.
- A customizable Responsible AI Operations Roadmap that synthesizes ecosystem maps, workflows, governance considerations, and next steps.
INTENDED AUDIENCE
Academic and administrative leaders; Student success professionals and advisors; Student affairs and co-curricular leaders; Institutional research, analytics, and data strategy teams; Enrollment management, financial aid, and registrar staff; Educational technologists and operations staff who support digital learning and student-facing systems
SERVICE TYPE
Professional learning
SERVICE MODEL
Faculty/Staff experience
CONTACT HOURS
0 required contact hours
TIME ON TASK
- Approximately 20–22 hours total.
- Typical pacing: 3–5 hours per week over six weeks, or equivalent time in a shorter intensive format.
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