arXiv AI

An emancipatory vision for designing (generative) AI for learner flourishing

arXiv AI
Aug 24

Environmental Slow AI: Design Principles for Generative Systems

The paper "Environmental Slow AI: Design Principles for Generative Systems" argues that generative AI reflects embedded cultural values that can be reshaped. It proposes five design principles—restraint, sufficiency, selectivity over retention, material visibility, and friction as affordance—grounded in environmental sustainability and the Slow AI tradition. Each principle is illustrated with current system designs and operates at both implementation and interpretive levels to restore human agency and encourage reflective engagement.

By Vanessa Utz
arXiv AI
Sep 11

With a Thermomix You Lose the Ability to Cook: A Kitchen Machine Analogy for Applications of Generative AI in Education

The paper titled "With a Thermomix You Lose the Ability to Cook: A Kitchen Machine Analogy for Applications of Generative AI in Education" uses the Thermomix kitchen appliance as an analogy to explore how generative AI tools like ChatGPT are adopted in education. By mapping Thermomix use cases onto learning scenarios and situating them within the ICAP and SAMR frameworks, the authors illustrate how different modes of tool use can either support or undermine meaningful engagement and learning. The study emphasizes that the key issue is not merely whether learners use AI, but how such use shapes their learning processes, offering a conceptual lens for researchers and practitioners to critically examine and guide AI integration in education.

By Nikol Rummel, Valentina Nachtigall, Ernesto Panadero
arXiv AI
5d ago

Agentic University: Collaborative Agents for Personalized Learning

The paper introduces Agentic University (AU), a Human‑Participatory Multi‑Agent Organization that lets learners act as organization members and collaborate with specialized agents to build a revisable Capability Program. This program links personal goals, multimodal learning activities, and layered assessment, using learner feedback and performance to guide future support. Evaluation shows that AU improves program organization, assessment design, and revision quality, yielding higher mean assessment gains and stronger cognitive support compared to baseline.

By Jingru Fan, Tianle Zhou, Yunjie Zhou, Zihao He, Yixuan Zhou, Kezhao Wu, Xinyu Zheng, Xiyuan Wang, Yuheng Wang, Zhi Li, Songhua Liu, Weinan E, Chen Qian