Reimagining Inquiry Through AI: Emerging Technologies for Scalable Complex Problem-Solving

Abstract

Abstract This emerging technology report examines the design, development, and implementation of an artificial intelligence (AI)d chatbot to support inquiry-based learning within a K–12 capstone context. Grounded in the increasing emphasis on ill-structured problem-solving aligned with contemporary educational standards, the project addresses persistent challenges related to cognitive load, scalability of instructional support, and variability in student experiences. The intervention was situated in a university-affiliated public high school where senior students engaged in individualized, community-based internships requiring sustained inquiry and reflection. To mitigate limitations in providing timely, personalized feedback, the design team developed a customized AI chatbot using vibe coding integrated with a large language model. Unlike generic systems, the chatbot was trained and designed on course-specific rubrics, internship requirements, and theoretical frameworks, including self-regulated learning, productive failure, and self-efficacy. The system functioned as a reflective scaffold, guiding students in goal setting, monitoring progress, and articulating challenges through iterative, theory-aligned prompts. The design process highlights the importance of aligning AI functionality with instructional goals, reducing extraneous cognitive load through streamlined prompting, and positioning AI as a complement rather than a replacement for teacher expertise. While the chatbot demonstrated potential to enhance personalization and scale inquiry-based learning, challenges related to ethical use, data privacy, and safeguards for minors that remain a significant challenge in K-12 settings. This report contributes to ongoing discourse by illustrating how intentional instructional design can mediate the risks of AI while leveraging its affordances to support complex learning environments.