AI-Enhanced Analytics for Equitable Classrooms: From Teacher Dashboards to Conversational Agents

Dra. Namrata Srivastava

Vanderbilt University, EE.UU.

Resumen/Abstract

Artificial intelligence is reshaping how we understand and support teaching and learning in real-world classrooms. This talk presents a human-centered AI approach to designing tools that enhance inquiry-based and collaborative STEM+C education in K-12 settings. I will share recent work developing AI-supported systems that make students’ thinking visible and help teachers orchestrate complex, open-ended learning environments. These include teacher-facing dashboards that integrate multimodal analytics to surface patterns of engagement and reasoning; student-facing conversational agents that scaffold inquiry and reflection during science and computational modeling activities; and researcher dashboards that transform multimodal data into interpretable insights for iterative design. Together, these tools illustrate how AI can serve as a collaborator, augmenting teachers’ expertise, amplifying student voices, and promoting equitable learning opportunities across diverse classrooms.

Profesora Proponente EPS: Ruth Cobos.

Curriculum ponente

Dr. Namrata Srivastava is a Research and Development Scientist at the Institute for Software Integrated Systems (ISIS), Vanderbilt University, where she works on human-centered AI and multimodal learning analytics to enhance equitable and responsive teaching and learning in STEM+C classrooms. Her research focuses on understanding students’ inquiry processes, collaboration, and learning performance through multimodal data such as eye-tracking, dialogues, and interaction logs. Prior to this, she was a Postdoctoral Researcher at the University of Pennsylvania’s Penn Center for Learning Analytics, developing AI-based detectors of student engagement (e.g., boredom, confusion). She also serves as Adjunct Research Fellow at Monash University, Australia  investigating self-regulated learning and engagement in digital learning environments.Dr. Srivastava earned her Ph.D. in Computer Science from the University of Melbourne, where she pioneered research on sensor-based learning analytics to detect cognitive load using non-invasive physiological sensors. Her work has contributed to major international projects such as NSF EngageAI, SPICE, and FLoRA, and collaborations with institutions like Stanford University, Adobe Research, and TU Delft. A recipient of the 2024 Emerging Scholar Award from the Society for Learning Analytics Research (SoLAR), her interdisciplinary research bridges AI, HCI, and Data Science to design intelligent systems that foster more effective, inclusive, and data-informed education.