Interactive Assessments for AI Safety: A Gamified Approach to Evaluation and Personal Journey Mapping

Anusha Asim, Ammar Ahmed Farooqi, Aqsa Khan

An interactive assessment platform and mentor chatbot hosted on Canvas LMS, for testing and guiding learners from BlueDot's Intro to Transformative AI Course.

Reviewer's Comments

Reviewer's Comments

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Hannah Betts

A great idea to cement learning through interactive, and nuanced decision-making. References early on in the paper create a clear scaffold for the project. While it was difficult to understand the analysis of current courses, the problem was clearly defined ("independent learning can be improved by moving beyond self-report").

I would love to see more detail written about the analysis of AI safety courses in sections 1 and 2, and how you chose the important concepts for inclusion in your interactive materials. This would be useful for others to build on your work, and to aid understanding of the reader. For example, what was the method of your analysis, and what did your analysis show, for the sentence: "we analyzed learning patterns from AI safety education initiatives..." ? Some citations did not show strong relevance to the context in which they were cited (e.g. Li et al 2019 re: iterative learning).

The AI mentor was a good idea, and I wonder how it could be extended to provide support and feedback that truly leverages the opportunity of an LLM chatbot; the demonstration seemed to follow a scripted conversation, with limited insight beyond the materials already available to the learner.

It was good to see a preliminary evaluation of the project, though analysis on the breadth and depth of content covered, and access to the course materials for deeper evaluation, would be useful to aid understanding of the technical quality.

Li-Lian Ang

Nice work on the effort put into this project! FYI I don't have access to the course, so could only rely on the demo video.

Here are some things I thought were particularly good:

- I admire the breadth approach in incorporating lots of different mediums for users to learn through, it shows a lot of creativity.

- Nice touch with making sure it is accessible!

Here are some places where it could have been improved:

- Questions from your user feedback might be slightly leading, given they are framed as positives. In the future, I would recommend doing user interviews given your small sample size. I'd recommend the Mom Test for a guide on how to do these user interviews well. Doing user interviews would more likely yield higher quality responses and provide more info on areas to improve.

- I would have loved to see more details on how you designed the chatbot mentor and how it would adapt to learners with specific needs. One of our major challenges is curating content for each user's unique background and interests.

- I would have loved to learn more about how the debate and assignments are graded. Is a human doing the grading or would an LLM do so? How would you ensure that user's get informative learnings from the feedback?

Cite this work

@misc {

title={

Interactive Assessments for AI Safety: A Gamified Approach to Evaluation and Personal Journey Mapping

},

author={

Anusha Asim, Ammar Ahmed Farooqi, Aqsa Khan

},

date={

3/10/25

},

organization={Apart Research},

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

howpublished={https://apartresearch.com}

}

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We argue that epidemic-style monitoring and tools grounded in interpretable, physics-aligned dynamics can serve as early warning systems for cascading AI agentic failures.

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In this work, we adapt classical epidemiological modeling (specifically SEIR compartment models) to model adversarial behavior propagation in AI agents.

By solving systems of ODEs describing the systems with physics-informed neural networks (PINNs), we analyze stable and unstable equilibria, bifurcation points, and the effectiveness of interventions.

We estimate parameters from real-world data (e.g., adversarial success rates, detection latency, patching delays) and simulate attack propagation scenarios across 8 sectors (enterprise, retail, trading, development, customer service, academia, medical, and critical infrastructure AI tools).

Our results demonstrate how agent population dynamics interact with architectural and policy design interventions to stabilize the system.

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We argue that epidemic-style monitoring and tools grounded in interpretable, physics-aligned dynamics can serve as early warning systems for cascading AI agentic failures.

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.