Scaling Networked Learning Through AI-Enabled Program Level Redesign - Moy, Magdalene; Feldstein, Andrew (Proceedings of the International Conference on Networked Learning, 2026)
As higher education increasingly shifts online, institutions face the challenge of creating engaging, collaborative learning experiences that extend beyond individual courses. This paper presents a novel approach to program-level curriculum redesign that integrates networked learning principles with ethical AI support to transform an entire undergraduate professional program. While existing literature demonstrates networked learning's effectiveness in individual courses, research has not addressed how to scale these principles across complete academic programs, a critical gap because students' educational experiences are cumulative and interconnected. We ground our redesign in networked learning theory, which positions learning as a web of purposeful connections among learners, educators, and resources rather than content transmission to individuals. Our conceptual framework operationalizes this through five iterative steps that maintain human-in-the-loop pedagogical authority while leveraging AI capabilities. Throughout, faculty retain final pedagogical authority, with AI serving as a thought partner that proposes while humans decide on pedagogical intent and ethical boundaries. Our five-step process begins with AI-enabled gap analysis against accreditation standards and quality frameworks, proceeds through backward design aligned with networked learning values, develops AI chatbots with explicit ethical guardrails, implements data-informed evaluation tracking both learning outcomes and network participation patterns, and concludes with continuous improvement cycles that preserve traceability for accreditation. This approach makes four distinct contributions to value-based AI-supported learning. First, it demonstrates how AI can scale personalized support while preserving essential social dimensions of learning. Second, it provides a replicable methodology for ethical AI integration that maintains disciplinary values. Third, it addresses digital equity challenges through deliberate design that ensures AI enhances rather than gatekeeps learning opportunities. Fourth, it demonstrates how networked learning principles can create program-level coherence, extending prior course-level research to show how collaboration and community-building become cumulative dimensions of professional identity development. By the conference, we will share concrete artifacts including AI prompt templates, chatbot configurations with ethical guardrails, and assessment rubrics that demonstrate values-embedded AI integration. Our work argues that the future of teaching lies not in choosing between human and artificial intelligence, but in orchestrating their collaboration to create learning networks that prepare students for contemporary professional practice where human judgment and AI assistance are increasingly intertwined.