Conference Agenda

This paper presents a digital learning framework, coLearn-AI (hereafter referred to as the system), that combines guided inquiry pedagogy with intelligent, adaptive feedback mechanisms to support collaborative learning in introductory programming courses. The system enables instructors to author structured inquiry activities, manage student groups, and provide embedded AI guidance that scaffolds learning in real time. Students work in groups and engage with progressively complex problems through exploration, collaboration, and reflection. The system integrates synchronous collaborative workspaces, turn-taking mechanisms, and analytics dashboards that help instructors monitor student progress at scale. A key innovation is the integration of AI-driven formative feedback, where instructors embed AI guidance directives within activity files. These directives, visible only to instructors, enable the AI module to provide context-aware feedback that aligns with pedagogical goals. By bridging structured human facilitation with adaptive, intelligent tutoring capabilities, the system promotes engagement, accountability, and deeper conceptual understanding. This paper describes the pedagogical foundations, system architecture, activity model, AI feedback pipeline, and group-learning mechanisms and outlines directions for formal efficiency evaluation and refinement.

As artificial intelligence(AI) becomes increasingly visible in everyday life, many students are experiencing growing anxiety about what AI is and how it affects them. This signals the urgent need for meaningful AI literacy education that helps learners understand how AI systems work and empowers them to use these tools confidently and effectively. Recognizing the importance of ensuring that all learners can develop this understanding, this paper introduces a learner-centered AI literacy lesson designed for 6-8 grade students. The lesson envisions AI literacy through a learner-centered lens, prioritizing accessibility, agency, and inclusivity. Grounded in the AI4K12’s “Big Five Ideas in AI” and informed by learning science frameworks such as Universal Design for Learning (UDL), Funds of Knowledge, and Depth of Knowledge, the proposed lesson supports students to learn AI in ways that connect to their experiences, questions, and capabilities.