Anonymized cohort intelligence · Sri Lanka HRM students
AI-aware, but not yet prototype-ready.
This interactive dashboard translates the pre-primer questionnaire into workshop design decisions. It visualizes readiness, misconceptions, skill gaps, segment differences, and peer-reviewed literature alignment without exposing any student identity.
Cohort pulse
Four numbers that shape the workshop
Interactive filter
Examine readiness by prior experience
Use the filters to compare students with and without prior creative AI, HR digital-content, or app/prototype experience. The filter uses anonymous metrics only.
Readiness anatomy
Confidence is strongest in general AI and weakest in design/prototyping
The Likert scores use a 1–5 scale. Low confidence means students rated themselves 1 or 2.
Current filtered cohort
n = 95Capability heatmap
Mean · low-confidence share · high-confidence shareKnowledge check
Strong quiz performance, but high-stakes misconceptions remain
The average score is high, yet errors cluster around surveillance, dismissal, recruitment bias, and safe classroom data.
Correct by question
12 questionsMisconception clinic
teach explicitlyOpen-ended themes
Students frame AI-HRM through recruitment first
Open-ended answers were grouped into non-identifying themes. The app does not store or display raw written responses.
Theme chart
multi-label codingSegment intelligence
Experience helps, but it does not replace guided practice
Segment gaps are descriptive signals, not causal estimates. Cohen’s d is shown as a rough effect-size cue.
Segment gap explorer
Anonymous respondent scatter
knowledge × prototype readinessPeer-reviewed literature comparison
Where the cohort aligns with — and diverges from — the research
The review is a rapid peer-reviewed narrative review, not a full systematic review. It is designed to guide this workshop.
Anonymized respondent explorer
Examine patterns without exposing identities
Synthetic IDs only. The table excludes names, registration numbers, contacts, batch labels, timestamps, and raw open-text responses.
n = 95
| ID | AI media | HR content | App sketch | Readiness | Knowledge | Misconceptions | Weakest domain |
|---|
Author and credibility
Prepared by Kushan Liyana Arachchige
This dashboard is designed as a transparent, research-informed teaching diagnostic. The author section documents public identity links, scope, methodological limits, and data-responsibility practices so readers can evaluate the work behind the visual analysis.
Workshop design response
Recommended delivery moves based on the data
Start with recruitment, then broaden.
Students naturally see AI through recruitment. Use that entry point, then widen to onboarding, L&D, employee experience, HR communication and analytics.
Make vibe coding canvas-first.
Because prototype confidence is the lowest area, ask students to define user, HR problem, data boundaries and risks before touching an app builder.
Teach risks as decisions, not definitions.
Use surveillance, dismissal, medical-data, and recruitment-bias scenarios. Students should classify: allow, modify or reject.
Use creative AI as HR communication quality practice.
Canva, avatars, voice and image/video tools should be evaluated on accuracy, inclusion, consent, disclosure and employee dignity.