Learning
Student AI, generative AI & analytics
AASTMT Smart Village · University teaching
3 Sessions
- Client
- AASTMT Smart Village
- Industry
- University teaching
Photographs from the sessions
Solution
On-campus learning at AASTMT Smart Village covering AI, generative AI and data analysis, with two applied RFM (recency–frequency–monetary) sessions taking students from customer data to practical analytical decisions.
Our approach
We ran hands-on sessions built around a live RFM segmentation model — students worked through recency, frequency and monetary scoring and what each implies for a business — alongside the AI, generative-AI and data-analysis material, so the analysis was something they could repeat rather than a purely conceptual overview.
Proposed Performance Targets
Proposed target
70%
Target relative increase in applied AI and analytics assessment scores
- What is measured
- Evaluate student progress in AI, generative AI, data analysis and RFM through practical assessments connecting technical understanding with analytical application.
- How it would be evaluated
- Before and after the relevant learning sequence, tracking the same students and using equivalent assessment coverage.
- What this figure is not
- Students, not executives. An assessment score does not imply an employment outcome.