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Ana Trindade Ribeiro
Access is Not Enough: Human Support Improves Engagement with AI Tutoring
Topics: School ChoiceTags: Educator perceptionsAI tutoring platforms offer a promising path to scaling personalized instruction, but only if students use them. We report findings from two randomized controlled trials in which elementary students were assigned to use an AI literacy platform independently or with an in-person tutor focused on… more →
Institutions: Stanford UniversityTutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise
Topics: Student LearningTags: Generative artificial intelligence (AI) in education, Instructional design, Instructional practices, Student engagement, Student supportsGenerative AI, particularly Large Language Models (LLMs), can expand access to expert guidance in domains like education, where such support is often limited. We introduce Tutor CoPilot, a Human-AI system that models expert thinking to assist tutors in real time. In a randomized controlled trial… more →
Institutions: Stanford UniversityUnderstanding Disruptions: Causes of and Variation in Lost Instructional Time
Topics: Student LearningVirtual instruction has boomed after the COVID-19 pandemic, including the use of virtual environments within in-person schools. But, research has provided little evidence about student experiences on these virtual platforms, nor how to improve the use of these platforms. Through natural language… more →
Institutions: Stanford University, Public Policy Institute of CaliforniaEducator Attention: How computational tools can systematically identify the distribution of a key resource for students
Topics: MethodsEducator attention is critical for student success, yet how educators distribute their attention across students remains poorly understood due to data and methodological constraints. This study presents the first large-scale computational analysis of educator attention patterns, leveraging over… more →
Institutions: Stanford University