Sina Rismanchian

About Me

I’m Sina Rismanchian, a PhD candidate in Education at UC Irvine, working with Shayan Doroudi. I study how artificial intelligence shapes the epistemic and cognitive capabilities of students, teachers, and learning scientists. I am motivated by the idea that AI is more than a tool to automate teaching and learning as it can open up new ways of understanding for researchers and learners, an idea with roots in the early days of AIED. My research sits at the intersection of educational technology, learning sciences, and AI.

Prior to UC Irvine, I completed my Bachelor’s degree in Computer Engineering at Sharif University of Technology in Iran. I was born and raised in Isfahan, one of the most ancient cities in Iran.

Research Interests

• AI as an Epistemic Tool for Learning: I design and study learning experiences that use AI to expand learners’ ways of knowing about the world and about themselves. This includes fostering domain knowledge (such as math and AI literacy) and learners’ dispositions such as intellectual virtues and metacognitive knowledge. Further, I use current AI models’ failures as a new lens to understand human learning capabilities.

• AI’s Cognitive Impact on Learning in Natural Settings: I study the promises and threats that arise when teachers and students encounter AI in practice. On the promising side: cases where AI genuinely extends what learners can do or think. On the threatening side: cognitive surrender, cheating, and their downstream costs to learning, or what students misunderstand about these tools in the first place. Understanding the threats is the first step toward designing against them.

• AI as Computational Models of Human Cognition: I develop AI and computational models as formal analogies for human learning to transform abstract theories into interrogable objects that yield new insights. This includes ABICAP (an agent-based model of ICAP theory) and Procedural ABICAP (reconciling Cognitive Load Theory, KLI, and ICAP). Further, I explore training language models as computational models of students’ misconceptions to study the fairness and pedagogical implications of these models in personalized learning settings.

Contact

Please feel free to reach out via email. I’m open to discussing research, collaborations, or answering questions about my work.