From Individual Prompts to Collective Intelligence: Mainstreaming Generative AI in the Classroom

2026-01-07

arXiv.org

AI Summary

This study addresses the limitations of current generative AI applications in engineering education, which predominantly rely on individual prompting and risk exacerbating educational inequities while offering limited support for deep collaboration. To counter this, the project proposes leveraging generative AI as a catalyst for collective intelligence (CI), designing and implementing Generative Collective Intelligence (GCI) instructional activities in two undergraduate engineering courses. These activities integrate Harvard Project Zero’s thinking routines—such as Question Sorts and Peel the Fruit—with strategic AI use to scaffold students’ externalization of reasoning and collaborative knowledge construction. Grounded in learning sciences, embodied cognition, and philosophy of technology, the research develops a scalable CI pedagogical framework. Findings indicate that human-AI collaboration significantly enhances students’ depth of understanding and creative problem-solving capabilities, with group-based engagement outperforming individual AI use, though risks of overreliance warrant careful mitigation.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Philosophy and Ethics of AI: Artificial General Intelligence
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Engineering classrooms are increasingly experimenting with generative AI (GenAI), but most uses remain confined to individual prompting and isolated assistance. This narrow framing risks reinforcing equity gaps and only rewarding the already privileged or motivated students. We argue instead for a shift toward collective intelligence (CI)-focused pedagogy, where GenAI acts as a catalyst for peer-to-peer learning. We implemented Generative CI (GCI) activities in two undergraduate engineering courses, engaging 140 students through thinking routines -- short, repeatable scaffolds developed by Harvard Project Zero to make thinking visible and support collaborative sense-making. Using routines such as Question Sorts and Peel the Fruit, combined with strategic AI consultation, we enabled students to externalize their reasoning, compare interpretations, and iteratively refine ideas. Our dual-pronged approach synthesizes literature from learning sciences, CI, embodied cognition, and philosophy of technology, while also empirically learning through student surveys and engagement observations. Results demonstrate that students value the combination of human collaboration with strategic AI support, recognizing risks of over-reliance while appreciating AI's role in expanding perspectives. Students identified that group work fosters deeper understanding and creative problem-solving than AI alone, with the timing of AI consultation significantly affecting learning outcomes. We offer practical implementation pathways for mainstreaming CI-focused pedagogy that cultivates deeper engagement, resilient problem-solving, and shared ownership of knowledge.

Problem

• generative AI
• collective intelligence
• equity gaps
• classroom pedagogy
• peer-to-peer learning

Innovation

• Generative AI
• Collective Intelligence
• Thinking Routines
• Pedagogical Innovation
• Human-AI Collaboration