Mar 10, 2025

BUGgy: Supporting AI Safety Education through Gamified Learning

Sophie Sananikone, Xenia Demetriou, Mariam Ibrahim, Nienke Posthumus

Details

Summary

As Artificial Intelligence (AI) development continues to proliferate, educating the wider public on AI Safety and the risks and limitations of AI increasingly gains importance. AI Safety Initiatives are being established across the world with the aim of facilitating discussion-based courses on AI Safety. However, these initiatives are located rather sparsely around the world, and not everyone has access to a group to join for the course. Online versions of such courses are selective and have limited spots, which may be an obstacle for some to join. Moreover, efforts to improve engagement and memory consolidation would be a notable addition to the course through Game-Based Learning (GBL), which has research supporting its potential in improving learning outcomes for users. Therefore, we propose a supplementary tool for BlueDot's AI Safety courses, that implements GBL to practice course content, as well as open-ended reflection questions. It was designed with principles from cognitive psychology and interface design, as well as theories for question formulation, addressing different levels of comprehension. To evaluate our prototype, we conducted user testing with cognitive walk-throughs and a questionnaire addressing different aspects of our design choices. Overall, results show that the tool is a promising way to supplement discussion-based courses in a creative and accessible way, and can be extended to other courses of similar structure. It shows potential for AI Safety courses to reach a wider audience with the effect of more informed and safe usage of AI, as well as inspiring further research into educational tools for AI Safety education.

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Cite this work:

@misc {

title={

BUGgy: Supporting AI Safety Education through Gamified Learning

author={

Sophie Sananikone, Xenia Demetriou, Mariam Ibrahim, Nienke Posthumus

date={

3/10/25

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Reviewer Name

Constructive critique

Criteria 1:

Advancing interpretability (Possible questions to answer in this criteria: Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?)

2.5

Criteria 2:

Relevance for AI safety (Possible questions to answer in this criteria: How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?))

2.5

Criteria 3:

Methodology & presentation (Possible questions to answer in this criteria: How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation))

2.5

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