AI Roles For Collective Learning
AI Roles For Collective Learning
Future Directions
时间
Spring 2025
标签
# AI Pedagogy
# Learning Design
# Concept Development
我的角色
Researcher and concept designer - Individual project
受众
Educators, learning designers
时间
Spring 2025
标签
# AI Pedagogy
# Learning Design
# Concept Development
我的角色
Researcher and concept designer - Individual project
受众
Educators, learning designers
Reinterpreting AI roles: From individuals to communities.
Reinterpreting AI roles: From individuals to communities.
Designed a conceptual framework for class-level AI use in higher education. Instead of focusing on AI as an individual study tool, this project reinterpreted AI as a support for collective learning and proposed four class-facing roles: Community Scribe, Inquiry Case Generator, Debate Challenger, and Peer Collaborator. The framework draws on Community of Inquiry and recent research on AI, engagement, and collaborative learning.
项目背景
The project began from a gap in current AI use. Students use AI heavily for individual academic work, faculty use it more selectively for preparation, and classroom integration remains limited. The presentation frames this as an opportunity to design AI for real-time, class-level learning rather than only individual support.
核心问题
Most AI use in education still centers on personalization for individual learners. This leaves less attention to collaboration, community-building, peer learning, and class-level progress, even though those are central to many learning experiences.
设计过程
Synthesized research on student AI use, faculty adoption, engagement, and collaborative learning to identify a class-level design gap.
Used Community of Inquiry as a framing model to reinterpret AI roles beyond individual support.
Built a conceptual map linking AI roles, learning mechanisms, and possible outcomes.
Developed four class-facing roles: Community Scribe, Inquiry Case Generator, Debate Challenger, and Peer Collaborator.
Created scenario-based applications to show how each role could operate in real classroom activities.
交付内容
Conceptual framework for collective learning with AI
Role map connecting individual, hybrid, and community-level AI roles
Four class-facing AI roles
Scenario-based classroom applications
项目成果
Produced a future-facing framework that reframed AI from an individual support tool to a class-level learning design question. The project translated theory into a set of usable roles and classroom scenarios, with Inquiry Case Generator standing out as a model for using AI to introduce productive struggle and shared inquiry.
项目反思
This project pushed me to think about classroom AI as a space for both course learning and AI literacy. It also reinforced that future-facing design is not only about adding new tools, but about deciding how those tools should support learning goals, participation, and shared classroom experience. A remaining question for me is evaluation: how we would know whether class-facing AI roles truly improve learning.
项目背景
The project began from a gap in current AI use. Students use AI heavily for individual academic work, faculty use it more selectively for preparation, and classroom integration remains limited. The presentation frames this as an opportunity to design AI for real-time, class-level learning rather than only individual support.
核心问题
Most AI use in education still centers on personalization for individual learners. This leaves less attention to collaboration, community-building, peer learning, and class-level progress, even though those are central to many learning experiences.
设计过程
Synthesized research on student AI use, faculty adoption, engagement, and collaborative learning to identify a class-level design gap.
Used Community of Inquiry as a framing model to reinterpret AI roles beyond individual support.
Built a conceptual map linking AI roles, learning mechanisms, and possible outcomes.
Developed four class-facing roles: Community Scribe, Inquiry Case Generator, Debate Challenger, and Peer Collaborator.
Created scenario-based applications to show how each role could operate in real classroom activities.
交付内容
Conceptual framework for collective learning with AI
Role map connecting individual, hybrid, and community-level AI roles
Four class-facing AI roles
Scenario-based classroom applications
项目成果
Produced a future-facing framework that reframed AI from an individual support tool to a class-level learning design question. The project translated theory into a set of usable roles and classroom scenarios, with Inquiry Case Generator standing out as a model for using AI to introduce productive struggle and shared inquiry.
项目反思
This project pushed me to think about classroom AI as a space for both course learning and AI literacy. It also reinforced that future-facing design is not only about adding new tools, but about deciding how those tools should support learning goals, participation, and shared classroom experience. A remaining question for me is evaluation: how we would know whether class-facing AI roles truly improve learning.
AI Roles For Collective Learning