Pre-Conference Workshop FD1
FD1 - EdHack: A Participatory Design Sprint for Advancing Educator AI Literacy
Date Wednesday, 3 Dec Time – Price: free of charge Status: places available
Dan Be Kim
AI Fellow, Harvard Graduate School of Education
Dan Be Kim is a startup founder turned educational technologist focused on reimagining teaching and learning in the age of AI. A startup enthusiast with an extensive background in product management, she has conceptualized and launched several marketplaces, including a venture-backed PropTech startup in the Bay Area. After arriving at Harvard determined to build evidence-based, AI-powered tools for education, she quickly discovered how overwhelmed and under-supported many educators and learners felt when engaging with this rapidly advancing technology.
Today, Dan Be’s work explores how AI can truly enhance and augment — not replace — human intelligence. Through PromptED, she helps educators and learners develop the intuition, fluency, confidence, and critical judgment needed to collaborate with AI in thoughtful, creative, and human-centered ways.
She currently contributes to research on Inspiring Moral AI Guidance in Education (IMAGINE) at the Harvard Center for Digital Thriving. In her role as an AI Fellow at the Harvard Graduate School of Education, she is helping to onboard ~700 incoming students by equipping them with the skills needed to leverage AI responsibly in their academic work and professional pursuits. As a Learning Design Fellow, she co-designed an AI literacy tutorial for all incoming students, a mandatory requirement before starting any coursework. The module introduces what responsible and effective AI use looks like in higher education, explores the nuance between school-wide and course-specific AI policies, and features diverse faculty perspectives to highlight how expectations vary across disciplines.
As an educational technologist, she is an active member of both the UNESCO Global Science of Learning Alliance and the Global Science of Learning Education Network (GSoLEN) hosted at UC San Diego, where she advocates for the conscious design of educational technologies grounded in evidence-based Science of Learning principles.
EdHack is a hands-on design sprint where educators, researchers, technologists, and learning scientists come together to prototype AI-powered learning tools that address real-world classroom challenges. This OEB edition builds on the inaugural Ed<>Hack hosted at the MIT Media Lab, co-organised by the SundAI Club (an AI hackers community incubated at MIT and Harvard) and the AI Literacy Club at the Harvard Graduate School of Education. That event brought learning scientists and classroom educators together with AI technologists and tinkerers to conceptualise and prototype EdTech solutions rooted in the learning sciences and shaped by real needs.
In this full-day session, participants will explore functional AI literacy through interdisciplinary collaboration and hands-on immersion in the application layer of generative AI. They will learn how to use a range of AI tools to enhance existing workflows, build custom tools, and do so in ways that are highly domain-specific and relevant to their professional roles—whether as faculty rethinking assessment, administrators automating certain workflows, or EdTech entrepreneurs prototyping new ideas.
Agenda:
• Welcome, agenda overview, and group formation (15 minutes)
• Opening Talk: A Multidimensional Look at AI Literacy (45 minutes)
• Coffee Break (15 minutes)
• Live Tool Demos (30 minutes)
• Introducing the Grand Challenge (15 minutes)
• Lunch Break (60 minutes)
• Team-Based Prototyping (120 minutes)
• Showcase Preparation (30 minutes)
• Grand Challenge Showcase (60 minutes)
• Closing Circle, Reflections and Key Takeaways (30 minutes)
Target Audience:
Educators, learning designers, technologists, EdTech professionals, curriculum developers, instructional coaches, education leaders, learning scientists
Target Audience Sector:
Higher education, workplace learning, upper-secondary education, EdTech sector
Prerequisite Knowledge:
Familiarity with educational contexts; no technical expertise required. Curiosity about AI and openness to creative experimentation encouraged.
Outcomes:
• Gain practical experience with functional AI literacy
• Prototype a tangible learning artefact using AI
• Experience the human-AI collaboration process firsthand
• Move from blind adoption to intentional curation and creation