How Santa Could Use Micro-RCTs to Deliver the Perfect Christmas

In the North Pole, even Mrs Claus and her knitting group have embraced modern technology to keep up with the holiday hustle. This year, they’re using an AI-powered computer to autotype responses to children’s letters. Santa’s logic model suggests that this intervention will reduce the time they spend writing letters, which will decrease hand cramp and, in turn, leave hands nimble enough to bake more cookies.

But something’s not adding up - despite the AI’s help, there aren’t more cookies appearing in the kitchen. Somewhere in this chain of assumptions, the logic model may be breaking down. To identify where the logic model might be failing, Santa can use a micro-randomised controlled trial (micro-RCT) to test the first assumption: Does the AI-powered computer actually reduce letter-writing time?

How It Works:
- Randomly divide children’s letters and knitters into two groups.
- Group A: responds using the AI computer.
- Group B: writes responses manually.
- Measure the time taken for each group and assess whether the AI significantly reduces writing time.

These results will help Santa to develop and refine his logic model. If the AI doesn’t significantly reduce time, then a key assumption underpinning the logic model needs to be rethought. If the AI does save time, but Mrs Claus’s hands are still sore, then the issue lies in another assumption made by the logic model - perhaps the AI requires too much typing or stress in another way. If the AI saves time and reduces hand cramp but cookies still aren’t increasing, then Mrs Claus may be spending the saved time on other tasks and this needs to be included in the logic model. In a similar way, this approach could be used to test claims that AI reduces teacher workload.

Santa faces a perennial challenge: helping children move from the "naughty" list to the "nice" list before Christmas Eve. This year, he’s decided to implement behaviour improvement interventions, predicting they’ll encourage better behaviour and ensure more stockings are filled with gifts instead of coal.

But how can he be certain his strategies work? By using micro-randomised controlled trials (micro-RCTs), Santa can rigorously compare the effectiveness of interventions against a control group.

How It Works:
Santa randomly assigns children on the naughty list to one of two groups.
- Intervention Group: Receives a personalised letter encouraging good behaviour.
- Control Group: Receives no intervention.

Santa then measures behaviour changes using metrics like parental reports, chore completion rates, and feedback from teachers. If the intervention group shows significant improvement compared to the control group, Santa knows his strategy works. If there’s little to no difference, it might be time to rethink his approach. This simple intervention-versus-control framework allows Santa to validate whether his efforts are making an impact or if he needs to explore alternative methods.

Once Santa identifies that interventions can positively influence behaviour, he can take it a step further by refining his strategies through A/B testing. Instead of comparing an intervention to a control group, Santa tests a beta version of the intervention to determine if this outperforms existing approaches.

How It Works:
Santa assigns naughty-list children to two groups.
- Group A: Receives a personalised letter encouraging good behaviour.
- Group B: Gets a virtual visit from an elf reminding them to be nice.

By comparing the effectiveness of Group A and Group B, Santa can identify which intervention leads to greater behavioural improvements. For example, if elf visits outperform personalised letters, Santa could focus resources on deploying more elves in future years. Conversely, if the letters prove more effective, he might invest in scaling up his stationery supply.This iterative approach allows Santa to fine-tune his methods and ensure every child has the best chance of landing on the "nice" list. Similarly, organisations can adopt A/B testing to refine their own interventions, using evidence to guide decision-making and optimise outcomes.

Even Santa’s reindeer face the pressures of peak season, from managing long-haul flights to ensuring a flawless Christmas Eve performance. To support their wellbeing, Santa purchases a mindfulness meditation app designed specifically for reindeer. The app features calming jingles, guided deep breathing exercises, and visualisations of serene snowfields.

To test its effectiveness, Santa implements a micro-randomised controlled trial (micro-RCT). Half the reindeer classes across the flying academy are assigned to use the app daily during December, while the other half stick to their regular routine. Metrics like festive cheer levels are tracked to assess impact. If the app proves effective, Santa can expand its use across his team, ensuring Dasher, Dancer, and all their friends are at their best for the big night. Similarly, schools can use micro-RCTs to evaluate EdTech, ensuring their purchases are truly beneficial for students.

The elves’ workshop school is gearing up for a curriculum update, aiming to equip Santa’s helpers with the skills they need to meet increasing toy production demands. Instead of rolling out the new curriculum workshop-wide without evidence, Santa could use micro-RCTs to evaluate its effectiveness compared to the existing one.

How It Works:
Santa divides the workshop school into three groups:
- Group A: Follows the current curriculum.
- Group B: Tests the new curriculum in a beta phase.
- Group C: Combines elements of both curricula to explore hybrid benefits.

By tracking learning outcomes, toy production accuracy, and confidence levels, Santa can identify which approach works best. For instance, if Group B outperforms the others, Santa could expand the new curriculum workshop-wide. If the hybrid model proves most effective, he might integrate elements of the old and new for a comprehensive solution. This iterative approach ensures that Santa’s elves are trained with the best possible methods, leading to happier, more skilled helpers and smoother Christmas preparations. Similarly, educational institutions can use micro-RCTs to refine teaching strategies, improving outcomes for all learners.

Santa knows that children around the world are wonderfully diverse, with varying needs, traditions, and preferences. To ensure his interventions work consistently across different contexts, Santa could use replication studies and cumulative meta-analysis. These approaches allow him to test whether a strategy is universally effective or needs tailoring to specific regions. By measuring recipient satisfaction and feedback in each region, Santa can determine whether the method’s success is universal or context-dependent. Similarly, organisations can use replication studies and meta-analysis to refine their strategies, ensuring they are effective across diverse populations and settings.

As Santa gears up for his busiest season, it’s clear that even the magic of Christmas can benefit from the rigor of evidence-based decision-making. Micro-randomised controlled trials (micro-RCTs) offer a powerful way to test, refine, and optimise every aspect of Santa’s operations—from helping naughty-list children improve their behaviour to testing reindeer wellbeing apps and perfecting gift delivery strategies.

These trials allow Santa to identify what truly works and adapt his methods to meet diverse needs across the globe. By embracing this innovative approach, Santa ensures his resources are used efficiently, his elves and reindeer are supported, and every child experiences the joy of a magical Christmas morning.

In much the same way, education systems can adopt micro-RCTs to refine teaching methods, evaluate new technologies, and improve outcomes for learners worldwide. Whether it’s delivering presents or delivering knowledge, the power of evidence ensures that every effort brings maximum impact. This Christmas, let’s take a page from Santa’s playbook and in partnership with WhatWorked Education, embrace the gift of smarter, data-driven decisions to create a brighter future for all.