Artificial Intelligence is rapidly reshaping higher education, from admissions analytics to student advising chatbots. This two-week intensive course equips academic advisors with practical, ethical, and strategic knowledge of AI tools used in universities. Participants will explore generative AI, predictive analytics, student engagement platforms, and workflow automation tools tailored to advising contexts. Hands-on labs and real-world examples will help advisors improve student success, retention, and personalization while maintaining academic integrity and privacy standards.
By the end of the program, advisors will be able to integrate AI responsibly into advising workflows, identify risks and biases, and collaborate with faculty and IT teams to implement effective solutions. The course emphasizes ethical considerations, accessibility, and human-centred advising, ensuring advisors remain trusted mentors while leveraging AI for better student outcomes.
Course Objectives
- Understand core AI concepts relevant to higher education advising.
- Evaluate AI tools for student engagement, analytics, and automation.
- Apply generative AI to advising communications and documentation.
- Use predictive analytics for retention and risk identification.
- Address ethics, bias, privacy, and academic integrity issues.
- Design AI-supported advising workflows.
- Develop an implementation plan for AI adoption in a university setting.
Target Audience
- Academic advisors and student success coaches.
- Directors of advising or retention programs.
- University administrators exploring AI adoption.
- Career services and student support staff.
- Faculty advisors and mentoring coordinators.
DAY 1 – Introduction to AI in Higher Education
- History of AI in universities.
- Key terminology for advisors.
- Overview of advising workflows.
- AI myths vs realities.
- Setting expectations for the course.
DAY 2 – Generative AI for Advisor Productivity
- Writing student emails with AI.
- Creating study plans.
- Drafting reports and notes.
- Prompt engineering basics.
- Avoiding hallucinations and errors.
DAY 3 – Student Data and Predictive Analytics
- Understanding retention metrics.
- Early alert systems.
- Risk prediction models.
- Data visualization dashboards.
- Interpreting AI recommendations.
DAY 4 – AI Chatbots for Student Support
- Chatbot design principles.
- FAQ automation.
- Escalation to human advisors.
- Measuring chatbot success.
- Accessibility considerations.
DAY 5 – Ethics, Bias, and Academic Integrity
- Bias in student risk predictions.
- Privacy law basics.
- Transparency in advising.
- Ethical decision frameworks.
- Responsible AI policies.
DAY 6 – AI Tools Integration with University Systems
- LMS integrations.
- CRM systems for advising.
- API basics for non-technical staff.
- Workflow automation tools.
- Vendor evaluation checklist.
DAY 7 – Personalisation and Student Experience
- Adaptive learning pathways.
- AI-driven nudges and reminders.
- Career recommendation tools.
- Equity considerations.
- Measuring student satisfaction.
DAY 8 – Building AI Workflows for Advisors
- Mapping advising processes.
- Identifying automation opportunities.
- Human-in-the-loop systems.
- Collaboration with IT teams.
- Pilot program planning.
DAY 9 – Change Management and Training Others
- Building advisor buy-in.
- Training faculty on AI tools.
- Communicating benefits to students.
- Managing resistance to AI.
- Evaluating impact and ROI.
DAY 10 – Capstone and Implementation Planning
- Designing an AI advising plan.
- Risk assessment checklist.
- Budget and vendor planning.
- Metrics for success.
- Presentations and feedback.
CASE-STUDY: AI Personalization at Netflix
Netflix implemented AI-driven recommendation systems to personalize user experiences, improve engagement, and reduce churn. The company uses machine learning to analyse viewing patterns, predict preferences, and recommend content in real time. Advisors can draw parallels between student advising and content recommendation by using behavioural data to provide tailored academic pathways, career suggestions, and support interventions. However, Netflix also faced challenges around algorithm bias, transparency, and ensuring users were not trapped in content "bubbles." Universities face similar concerns when using AI to guide student choices.
Group Discussion Questions
- What lessons from Netflix personalisation apply to academic advising?
- What risks might universities face using similar AI systems?
- How can advisors implement AI responsibly?
