BUGgy: Supporting AI Safety Education through Gamified Learning

Mar 10, 2025

BUGgy: Supporting AI Safety Education through Gamified Learning

Sophie Sananikone, Xenia Demetriou, Mariam Ibrahim, Nienke Posthumus

Read Project

Review Project

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.

Download PDF

View Presentation

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}

}

Reviewer's Comments

Hannah Betts

A neat demonstration of what could be a much larger game. Some references seem to have been misquoted (e.g. Shaffer, 2006 doesn't include commentary on "Knowing" and "doing", and some of the concepts quoted don't appear to have influenced the game design.) I suggest further care is taken as this project develops to ensure deep engagement with the existing literature to influence design choices.

Great to see the user testing, and examples of questions (and approximate difficulty) of the questions for the final app. Technical and reflective questions enable the user to engage with the content on a variety of levels. Aiming to have a discussion board and readings available in the app are good ideas to extend and contextualize the learning from the BlueDot Impact course. However, beware of additional capacity required to moderate or manage active discussions between app users. Further explanation of the BlueDot Impact course and how the relevant knowledge was selected would be great to see.

Li-Lian Ang

Amazing project! I really enjoyed going through your prototype.

Here are some things I particularly enjoyed:

- It is so cute! It gave me a very friendly vibe and I truly had a lovely time going through the journey.

- I loved that you used the loading screens as an opportunity to educate users about AI safety.

- Good job tying the readings to particular quizzes, this would be a good learning supplement given we don't have quizzes on the intro to TAI course.

- Good call on using Figma to make a prototype and focusing more time on the content and execution! This means that you've managed to create something way more substantial by the end.

Here are some things I thought could have been improved:

- I think there were lots of moments where you could have leveraged your user's attention to teach them AI safety concepts! Perhaps when a person got a question wrong, you could explain why their answer is wrong. Or given they were already invested in BUG and the story, you might have woven in AI safety concepts into the narrative and used the quiz as a way to evaluate learning objectives.

- Participants typically have lots of questions about the content that aren't answered within the readings and are usually part of the facilitator's responsibility to answer. I wonder if there is a way that your app could also address this challenge.

- The key challenge here is developing challenging questions from the resources which properly assess learner's understanding. I would have loved to see more details on how you address this!

I think this sort of app would be excellent for targeting a much younger audience who would find this learning journey more accessible!

Recent Projects

View All

Mar 10, 2025

Attention Pattern Based Information Flow Visualization Tool

Understanding information flow in transformer-based language models is crucial for mechanistic interpretability. We introduce a visualization tool that extracts and represents attention patterns across model components, revealing how tokens influence each other during processing. Our tool automatically identifies and color-codes functional attention head types based on established taxonomies from recent research on indirect object identification (Wang et al., 2022), factual recall (Chughtai et al., 2024), and factual association retrieval (Geva et al., 2023). This interactive approach enables researchers to trace information propagation through transformer architectures, providing deeper insights into how these models implement reasoning and knowledge retrieval capabilities.

Jan 24, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

Jan 24, 2025

CoTEP: A Multi-Modal Chain of Thought Evaluation Platform for the Next Generation of SOTA AI Models

As advanced state-of-the-art models like OpenAI's o-1 series, the upcoming o-3 family, Gemini 2.0 Flash Thinking and DeepSeek display increasingly sophisticated chain-of-thought (CoT) capabilities, our safety evaluations have not yet caught up. We propose building a platform that allows us to gather systematic evaluations of AI reasoning processes to create comprehensive safety benchmarks. Our Chain of Thought Evaluation Platform (CoTEP) will help establish standards for assessing AI reasoning and ensure development of more robust, trustworthy AI systems through industry and government collaboration.

Mar 10, 2025