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Executive Snapshot
The AI Christmas Calendar was a time-boxed adoption intervention designed to re-engage employees who were not responding to standard AI Hub learning formats. Using a 24-day gamified micro-learning challenge delivered via a smartphone app, I focused on lowering activation energy, driving hands-on AI usage, and reconnecting learners to the broader AI Hub ecosystem. I led the initiative end-to-end under extreme time constraints.
Business Context & Performance Problem
Despite multiple AI Hub learning activities already in place, engagement remained concentrated among early adopters. A large portion of employees:
Did not explore the AI tool voluntarily
Perceived AI learning as optional or time-consuming
Felt unsure where to start or whether it was “for them"
The risk was not lack of content, but lack of activation. Without intervention, AI Hub adoption would plateau, limiting ROI on both tooling and L&D effort.
The calendar was explicitly positioned as a behavioral on-ramp, not a comprehensive skills program.
Objectives:
Build foundational knowledge of large language models (LLMs) and prompting skills among a broader audience.
Motivate employees to discover the GEN AI tool by demonstrating useful, time-saving use cases.
Develop basic knowledge of LLMs and prompting skills in wider audiences.
Promote AI hub platform and its educational activities that would lead to more skill development.
Action Mapping (Diagnostic Backbone)
Although time constraints limited a full formal analysis, I applied Action Mapping principles to define a minimum viable intervention.
Business Goal
Increase weekly active AI tool usage and expose a broader audience to practical, time-saving AI use cases.
Critical Behaviors
Employees must:
Open the AI tool repeatedly
Complete small, practical tasks independently
Share outputs and observe peer usage
Discover follow-up learning opportunities in the AI Hub
Key Barriers Identified
Activation Energy Barrier
AI learning felt “heavy” or time-intensive.
Low Curiosity Trigger
Existing formats did not generate urgency or excitement.
Social Proof Absence
Learners did not see peers actively experimenting.
What Was Intentionally Excluded:
Deep AI theory
Long-form courses
Perfection in instructional polish
Speed and reach were prioritized over depth
Explore
Explore a demo of the first two capsules of the AI Christmas Calendar:
Daily 10-minute prompt challenges
Quizzes and examples preceding each task
Peer-visible prompt sharing
Points, leaderboards, and rewards
Constraints & Friction
This project operated under unusually tight constraints:
Two weeks from concept to launch
First-ever score-tracking challenge in the Beedeez app
I simultaneously acted as ID, SME, promoter, and evaluator
Limited opportunity for testing and iteration pre-launch
These constraints required aggressive scope control and rapid decision-making.
Options Considered & Rejected
Several alternatives were consciously rejected:
Another traditional AI course
Unlikely to reach disengaged users.
Email-only promotion
Previously ineffective without experiential pull.
Single “big challenge” event
High drop-off risk; low habit formation.
A daily micro-commitment format with visible progress and rewards was selected instead
Design Strategy & Key Decisions
Key design decisions included:
24 short daily tasks instead of fewer deep ones
→ Lowered psychological barrier to entry.
Mandatory prompt submission
→ Ensured real AI usage, not passive participation.
Peer-visible outputs
→ Created social proof and inspiration.
Cross-promotion of existing AI Hub content
→ Reconnected learners to deeper learning paths.
Implementation & Delivery
The initiative was delivered through:
Beedeez mobile app (daily unlocks, scoring, leaderboard, tracking, comments, gamification)
App notifications and promotional messaging
Participants received daily prompts, reinforcing consistency and habit formation.
Learning Approach
Participants received daily notifications via the app, prompting them to unlock a new task. Tasks were designed to be practical and interactive, followed by quizzes to reinforce learning.
Gamification elements included:
Points for task completion and quiz accuracy.
Leaderboards to foster competition.
Prizes (e.g., Kindle readers) for top participants.
The initiative leveraged:
Gamification: Points, leaderboards, and prizes to sustain motivation.
Micro-learning: Short, daily tasks to build habits and encourage consistent use of the AI tool.
Example tasks included:
Inviting a colleague to the challenge.
Using the AI tool to summarize a meeting transcription and generating action points for participants.
Generating multiple sales pitches and having the AI tool scoring them and explaining pros and cons of each.
Comparing two documents, formatting the output, and sharing the prompt that was used with the community.
Completing a learning capsule from another course of micro-videos on prompt engineering.
Giving an example of 5 tasks that you could use the AI tool for at work.
Learning Science
Gamification Theory
Incorporated game elements such as points, leaderboards, and prizes to enhance motivation and engagement. Fostered competition and sustained interest through daily challenges and reward.
Micro-Learning
Delivered short, daily tasks (10 minutes each) to build consistent habits and reduce cognitive load.
Focused on bite-sized, actionable learning to ensure accessibility for busy employees.
Behaviorism
Reinforced desired behaviors (e.g., practicing prompt engineering) through rewards, feedback, and repetition.
Used notifications and reminders to encourage daily participation.
Constructivist Learning Theory
Designed practical, hands-on tasks to encourage active learning and real-world application of skills.
Encouraged participants to share their prompts in the app’s comment section, fostering peer-to-peer learning.
ARCS Model of Motivation
Captured attention with gamified elements and daily notifications.
Highlighted relevance by linking tasks to real-world applications of AI tools.
Built confidence through scaffolded tasks and immediate feedback.
Maintained satisfaction with tangible rewards and leaderboard recognition.
Learning across context
Delivered various tasks over 24 days to ensure prompting skill retention in various circumstances.
Reinforced key concepts through daily practice and follow-up quizzes.
Development Process
Time was scarce and I could not rely on my usual process of eLearning development that would include proper needs analysis, visual storyboard, careful design of learning objectives and more testing of the Beedeez app functionalities. Therefore, I relied heavily on AI assistants to help me through all the design and development phases as well as on rapid prototyping and continuous feedback from the client, and future learners. It was exciting and stressful at the same time!
Throughout the design & development process, I used the AI as assistant for the following tasks:
Brainstorming prompting techniques to include in the challenge.
Designing tasks taking into account the functionalities of the Beedeez app.
Generating images to be used in the eLearning and blogs.
Correcting grammar and style.
Designing promotional strategy.
Writing promotional blogs, notifications and emails.
Analysing the score reports from Beedeez apps.
Evaluating feedback from the learners.
Outcomes, Signals & Learnings
The initiative delivered measurable adoption impact:
24% employee participation
~2.4 hours of learning per participant
+11% increase in users logging into the AI tool
+41% increase in weekly active AI tool users
These results confirmed that activation mechanics, not content volume, were the missing lever.
What This Project Demonstrates About Me
I design activation-first interventions when engagement is the bottleneck
I make pragmatic tradeoffs under extreme constraints
I use gamification as a behavioral lever, not decoration
I leverage AI to compress delivery timelines without sacrificing intent
I reflect critically on outcomes and improvement opportunities
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