9 citations · 10 across the 10 of their papers we have counts for
10 papers
Evolution of A4L: A Data Architecture for AI-Augmented Learning
Ploy Thajchayapong, Suzanne Carbonaro, Tim Couper +3
As artificial intelligence (AI) becomes more deeply integrated into educational ecosystems, the demand for scalable solutions that enable personalized learning continues to grow. T…
Learning Ecology with VERA Using Conceptual Models and Simulations
Spencer Rugaber, Scott Bunin, Andrew Hornback +2
Conceptual modeling has been an important part of constructionist educational practices for many years, particularly in STEM (Science, Technology, Engineering and Mathematics) disc…
A4L: An Architecture for AI-Augmented Learning
Ashok Goel, Ploy Thajchayapong, Vrinda Nandan +2
AI promises personalized learning and scalable education. As AI agents increasingly permeate education in support of teaching and learning, there is a critical and urgent need for…
Designing a Communication Bridge between Communities: Participatory Design for a Question-Answering AI Agent
Jeonghyun Lee, Vrinda Nandan, Harshvardhan Sikka +2
How do we design an AI system that is intended to act as a communication bridge between two user communities with different mental models and vocabularies? Skillsync is an interact…
Contextualizing Large-Scale Domain Knowledge for Conceptual Modeling and Simulation
Sungeun An, Spencer Rugaber, Jennifer Hammock +1
We present an interactive modeling tool, VERA, that scaffolds the acquisition of domain knowledge involved in conceptual modeling and agent-based simulations. We describe the knowl…
Cognitive Assistance for Inquiry-Based Modeling
Sungeun An, Robert Bates, Spencer Rugaber +3
Inquiry-based modeling is essential to scientific practice. However, modeling is difficult for novice scientists in part due to limited domain-specific knowledge and quantitative s…
Explanation as Question Answering based on a Task Model of the Agent's Design
Ashok Goel, Harshvardhan Sikka, Vrinda Nandan +3
We describe a stance towards the generation of explanations in AI agents that is both human-centered and design-based. We collect questions about the working of an AI agent through…
Understanding Self-Directed Learning in an Online Laboratory
Sungeun An, Spencer Rugaber, Jennifer Hammock +1
We described a study on the use of an online laboratory for self-directed learning by constructing and simulating conceptual models of ecological systems. In this study, we could o…
Explanation as Question Answering based on Design Knowledge
Ashok Goel, Vrinda Nandan, Eric Gregori +2
Explanation of an AI agent requires knowledge of its design and operation. An open question is how to identify, access and use this design knowledge for generating explanations. Ma…
Using VERA to explain the impact of social distancing on the spread of COVID-19
William Broniec, Sungeun An, Spencer Rugaber +1
COVID-19 continues to spread across the country and around the world. Current strategies for managing the spread of COVID-19 include social distancing. We present VERA, an interact…