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 In this talk, I present a preliminary framework to integrate these two conceptualizations of a mental model. I then present insights from several examples of past work to illustrate and explore the potential of this framework to build theory, inform practices, and generate testable hypotheses in robot-assisted learning. 

SCI's graduate computer science programs have been ranked #51, five spots higher than in 2024, on the Best Grad Schools rankings. 

Multimodal AI systems are transforming how users interact with digital content. However, deploying these models in high-stakes settings requires ensuring both the trustworthiness of the information they process and generate, as well as the reliability of their learning and inference mechanisms. In this talk, I present approaches to addressing these challenges. 

This talk will discuss recent advances in support of collaboration using GenAI and LLMs, with a particular focus on two recent classroom studies investigating LLM-based support for reflection and learning during collaborative software development.

In this talk, I will advocate for a transformative shift from scaling monolithic LMs through training-time computation to Augmented LMs, which are designed, trained, and deployed alongside complementary modules.

In this talk, I will explore the application of quantum computing in key areas such as machine learning, covering both quantum algorithms that can run on near-term devices and those designed for future quantum systems incorporating quantum error correction codes.

Modern AI systems such as LLMs are pervasive and helpful, but do they really have the social intelligence to seamlessly and safely engage in interactions with humans? In this talk, I will delve into the limits of social intelligence of LLMs and how we can measure and anticipate their risks.