Pre-Workshop Questionnaire Diagnostic
Audience: HRM students attending the AI-Powered Digital HRM Future Lab Responses analyzed: 95 Purpose: Tune the 4-hour primer workshop to the actual readiness, misconceptions, and learning priorities of the cohort. Privacy note: This page uses aggregate results only. Do not publish names, registration numbers, contact details, or individual-level responses.
Executive summary
The cohort is AI-aware but not yet prototype-ready. Every respondent has used an AI assistant, and most have used creative AI tools, but only a minority have created HR-related digital content or sketched a website, app, or chatbot idea. This means the workshop should be hands-on, scaffolded, and confidence-building, rather than assuming advanced digital production skills.
The strongest student understanding is around basic generative AI, synthetic media, social media employer branding, and human-in-the-loop principles. The weakest areas are vibe coding, gamification, IoT/workplace sensors, and responsible AI risk diagnosis. Open-ended responses strongly over-index on recruitment, especially CV screening, job descriptions, and recruitment communication. The workshop should deliberately broaden students beyond recruitment into onboarding, learning, employee experience, people analytics, digital communication, and governance.
Participation and prior exposure
| Indicator | Count | Share |
|---|---|---|
| Used ChatGPT, Gemini, Copilot, Claude, or similar AI assistant | 95 / 95 | 100.0% |
| Used Canva AI, image generation, avatar, or voice tools | 69 / 95 | 72.6% |
| Created digital content for recruitment, branding, onboarding, or employee communication | 21 / 95 | 22.1% |
| Built or sketched a website, app, or chatbot idea | 18 / 95 | 18.9% |
Interpretation
Students are already familiar with AI assistants, so the workshop does not need a long generic introduction to ChatGPT-style tools. However, they need support moving from casual AI use to structured HRM use, digital content production, and prototype design.
Self-assessed confidence profile
Scale: 1 = low confidence, 5 = high confidence.
| Capability area | Mean score | Agree / strong agree | Low confidence |
|---|---|---|---|
| Explain generative AI in Digital HRM | 3.55 | 62.1% | 10.5% |
| Identify practical HR use cases for AI tools | 3.54 | 60.0% | 9.5% |
| Identify social media use cases in employer branding | 3.52 | 58.9% | 10.5% |
| Use AI to draft simple HR content | 3.43 | 56.8% | 20.0% |
| Contribute to team-based AI-Digital HRM prototype activity | 3.37 | 55.8% | 17.9% |
| Storyboard AI-generated image, avatar, voice, or video content | 3.35 | 52.6% | 21.1% |
| Understand when human judgment should remain central | 3.26 | 44.2% | 16.8% |
| Describe IoT or workplace sensors in HRM | 3.17 | 44.2% | 28.4% |
| Suggest an AI-assisted employer branding or EX campaign | 3.15 | 40.0% | 25.3% |
| Identify privacy, bias, consent, misinformation, surveillance, and dignity risks | 3.07 | 38.9% | 29.5% |
| Explain gamification in recruitment, learning, or engagement | 3.05 | 31.6% | 25.3% |
| Outline a basic HR website/app idea using vibe-coding logic | 2.86 | 24.2% | 33.7% |
Knowledge check results
Students performed well overall on the 12-item baseline knowledge check.
| Metric | Result |
|---|---|
| Mean score | 10.03 / 12 |
| Median score | 11 / 12 |
| Full score | 34 students, 35.8% |
| 10 or more correct | 64 students, 67.4% |
| 7 or fewer correct | 13 students, 13.7% |
Strongest knowledge areas
| Item | Correct response rate |
|---|---|
| Synthetic media may include AI-generated images, avatars, voice, or video | 94.7% |
| Generative AI can create text, images, audio, or video | 91.6% |
| Employer branding through social media shapes how current and potential employees view the organization | 90.5% |
| Public AI tools require concern for privacy, confidentiality, consent, and data protection | 88.4% |
| Human-in-the-loop means humans review, approve, interpret, or override AI-supported outputs | 86.3% |
Weakest knowledge areas to address directly
| Item | Correct response rate | Teaching implication |
|---|---|---|
| Major risk of AI in recruitment is reproducing bias from poor-quality or historically biased data | 70.5% | Add a recruitment bias mini-case in Session 1 or 2. |
| Appropriate AI-assisted HR task is drafting an onboarding welcome message for human review | 74.7% | Reinforce “AI drafts; humans decide.” |
| Gamification means points, badges, challenges, or leaderboards to increase engagement | 75.8% | Teach gamification as design psychology, not entertainment. |
| IoT involves devices and sensors that collect/share data from the physical work environment | 78.9% | Use concrete examples: wearables, access cards, safety sensors. |
| Safest classroom data is synthetic or fictional HR data | 83.2% | Make synthetic-data practice a non-negotiable safety rule. |
Misconception alerts
These should be handled gently but explicitly during the workshop.
| Misconception signal | Why it matters | Workshop response |
|---|---|---|
| 13 students selected “secretly monitor all employee conversations” as an appropriate AI-assisted HR task | Indicates some confusion between digital HRM and surveillance | Add a 5-minute “allowed, restricted, prohibited” activity. |
| 7 students selected “allowing AI to make the final dismissal decision without review” as appropriate | High-risk misunderstanding for HR accountability | Use a human-in-the-loop decision wall. |
| 8 students selected real medical data and 7 selected real disciplinary records as safe classroom data | Serious privacy and dignity concern | Repeat “synthetic data only” at the beginning of every practical activity. |
| Recruitment risk awareness is not universal | AI hiring bias is a core HRM issue | Add a bias audit demonstration even in primer form. |
Open-ended response themes
Where students think generative AI is useful in HR
| Theme | Count | Share |
|---|---|---|
| Recruitment, selection, CV/resume screening | 78 | 82.1% |
| Recruitment communication, job descriptions, job posts, emails | 52 | 54.7% |
| Analytics or decision support | 13 | 13.7% |
| Onboarding | 5 | 5.3% |
| Training and development | 3 | 3.2% |
| HR operations/admin | 3 | 3.2% |
Interpretation: Students strongly associate AI in HRM with recruitment. The workshop should honor this interest but broaden the frame to onboarding, employee experience, learning, communications, people analytics, and governance.
Risks students mentioned
| Theme | Count | Share |
|---|---|---|
| Bias, unfairness, discrimination | 71 | 74.7% |
| Privacy, confidentiality, data protection | 53 | 55.8% |
| Loss of human judgment / over-reliance | 10 | 10.5% |
| Inaccuracy, misinformation, hallucination | 2 | 2.1% |
| Surveillance, monitoring, dignity | 1 | 1.1% |
Interpretation: Students recognize bias and privacy, but they under-recognize hallucination, misinformation, surveillance, consent, dignity, and over-reliance. These risks should be made visible through short cases.
What students hope to learn
| Theme | Count | Share |
|---|---|---|
| Practical AI use in HRM | 62 | 65.3% |
| AI skills and tool confidence | 59 | 62.1% |
| Digital HRM / future technology trends | 31 | 32.6% |
| Responsible or ethical AI | 29 | 30.5% |
| Employee engagement, employee experience, communication | 24 | 25.3% |
| Recruitment | 19 | 20.0% |
| Vibe coding, websites, apps, prototyping | 15 | 15.8% |
| HR analytics / Power BI | 1 | 1.1% |
Recommended workshop tuning
Overall facilitation stance
Use a beginner-to-builder model:
- Start with what they know: AI assistants and recruitment examples.
- Quickly broaden to Digital HRM beyond recruitment.
- Move into guided creation using templates.
- Make ethics and privacy visible through repeated checkpoints.
- End with a confident prototype pitch.
Session-by-session adjustments
| Session | Original focus | Survey-informed adjustment |
|---|---|---|
| Session 1: Generative AI Trends in Digital HRM | AI trends and opportunity mapping | Reduce generic AI explanation. Add “Beyond recruitment” mapping and a misconception clinic on monitoring, dismissal, and synthetic data. |
| Session 2: Social Media, Gamification, IoT | Digital experience blueprint | Teach gamification and IoT from fundamentals because confidence is low. Use simple Sri Lankan cases: hotel onboarding badges, apparel safety sensors, university employer branding. |
| Session 3: Vibe Coding | Prompt-to-prototype HR websites/apps | Make this highly scaffolded. Start with paper/app canvas before tools. Use one instructor-built example, then allow teams to customize rather than build from zero. |
| Session 4: AI Creative Studio | Canva, HeyGen, ElevenLabs, image/video AI | Students have creative-tool exposure but little HR content experience. Focus on HR message quality, consent, disclosure, and inclusive communication. |
Revised 4-hour run of show
| Time | Segment | Survey-informed emphasis |
|---|---|---|
| 0:00–0:10 | Welcome + audience pulse | Show aggregate findings: high AI exposure, low prototype experience, strong interest in practical AI. |
| 0:10–0:25 | AI in HRM beyond recruitment | Use recruitment as entry point, then add onboarding, L&D, engagement, HR service, analytics. |
| 0:25–0:45 | AI-HRM opportunity map sprint | Teams map AI use cases with one human-review point each. |
| 0:45–1:00 | Misconception clinic | Secret monitoring, final dismissal decisions, synthetic-data rule, AI bias. |
| 1:00–1:15 | Social recruiting demo | Employer branding campaign for a Sri Lankan organization. |
| 1:15–1:35 | Gamified onboarding design | Explain points, badges, missions, feedback, and what not to gamify. |
| 1:35–1:50 | IoT ethics tribunal | Safety sensors vs surveillance. Teams vote allow/modify/reject. |
| 1:50–2:00 | Blueprint share-out | One benefit and one safeguard per team. |
| 2:00–2:15 | Vibe coding primer | Explain prototype vs production; show one safe HR micro-app prompt. |
| 2:15–2:35 | App canvas before tool | Teams define user, HR problem, features, data not to collect, risks. |
| 2:35–2:55 | Prototype sprint | Teams build, mock up, or storyboard. Prioritize confidence and safety. |
| 2:55–3:00 | Security teardown | What should not be published without review? |
| 3:00–3:15 | AI creative tool tour | Canva AI, HeyGen, ElevenLabs, image/video platforms. |
| 3:15–3:35 | HR media sprint | Teams create/storyboard poster, voiceover, avatar/video, and disclosure. |
| 3:35–3:45 | Consent and media ethics check | Real faces/voices, AI disclosure, stereotypes, misleading claims. |
| 3:45–4:00 | Final pitch | 60-second prototype pitch using the pitch timer. |
Team formation recommendation
Create mixed teams of 4–5. Where possible, each team should include:
- At least one student who has used Canva/image/avatar/voice tools.
- At least one student who has created digital content or sketched a website/app/chatbot.
- One student assigned as Ethics Guardian.
- One student assigned as Prompt Lead.
Do not publicly display students’ questionnaire answers. Use the survey only to balance teams if the instructor is comfortable handling the data privately.
Instructor emphasis points
Say this early
“Most of you have used AI tools before. Today we move from casual AI use to responsible HRM use.”
Repeat this before every practical activity
“Use fictional or synthetic data only. Do not upload real student, employee, applicant, salary, medical, disciplinary, or institutional information.”
Use this as the workshop anchor
“AI can draft, suggest, summarize, and prototype. Human HR professionals remain accountable for decisions affecting people.”
Recommended modifications to the web app
Add or highlight the following:
- Pre-workshop pulse card on the landing page.
- Misconception clinic inside Session 1.
- Beginner-friendly vibe coding canvas inside Session 3.
- Synthetic-data warning banner before all prompt boxes.
- Consent and AI media disclosure checklist in Session 4.
- Instructor-only diagnostic page summarizing this analysis.
Starter questions for the live workshop
Use these as fast polls or discussion prompts:
- “What is one HR task AI should support but not decide?”
- “Is smart workplace data for safety different from surveillance? Where is the line?”
- “Would you trust an AI-generated job ad? What would you check first?”
- “What data should never be entered into a public AI tool?”
- “What makes an HR app prototype safe for classroom use but unsafe for production?”
Bottom line
This cohort is ready for an engaging AI-HRM primer, but the workshop should not be too abstract or too advanced. The best delivery style is:
practical demonstration → guided team canvas → short build sprint → ethics checkpoint → pitch.
The strongest opportunity is to convert students from AI users into responsible AI-HRM prototype thinkers.