In conversation with Professor Bodong Chen, Stian Håklev will explore how AI tools such as Claude Code are transforming knowledge work, learning design, and creative production. Through examples from language learning, research, and tool-building, the session will highlight how Al can support complex intellectual work for educators, researchers, and students.
Large language models are remarkably capable—but most of us interact with them through a chat window, copying and pasting text back and forth. Claude Code, Anthropic's command-line tool, offers a fundamentally different way of working with AI: it can read your files, write code, search the web, and orchestrate complex multi-step tasks—all from your terminal. This talk introduces Claude Code as a tool for knowledge work, not just software engineering. Through live demos and concrete examples, Dr. Håklev will show how he's used it to build a personalized Arabic learning app, construct knowledge bases from academic literature, automate data gathering and cleaning for social science research, and rapidly prototype interactive web applications. No programming experience is required to follow along—the point is that Claude Code lowers the barrier so dramatically that the bottleneck shifts from "Can I code this?" to "Can I describe what I want?" We'll discuss what this means for researchers, students, and anyone doing complex intellectual work.
Speakers
Stian Håklev is a learning scientist and developer based in Norway. He holds a PhD in Learning Sciences from the University of Toronto (OISE), where he researched collaborative learning at scale, and did postdoctoral work at EPFL's CHILI Lab, developing tools for orchestrating collaborative learning scenarios. He co-founded Peer 2 Peer University (P2PU), worked as Principal Learning Architect at the Minerva Project, and is currently a builder at Tana, a networked-thought tool. Stian now runs AI workshops for organizations, drawing on his learning science background to design hands-on formats that lead to lasting adoption.
Bodong Chen is an associate professor at Penn GSE and director of Penn Wonder Lab and Knowledge Building Innovation Network. A learning scientist and educational technologist, he strives to make learning a meaningful part of social participation for people of all backgrounds and circumstances. His scholarly inquiry integrates knowledge media design, software engineering, and data science methods to continually improve infrastructures for learning. Guided by design-based research and participatory design approaches, he aims to generate justice-oriented pedagogical designs, technological innovations, and empirical understandings of learning in authentic settings.
Sponsored by Penn Wonder Lab
Co-organized by AIED@Penn