AI-Supported Mind Mapping for Collaborative Discussion: An Exploratory Qualitative Classroom Study Using Personary

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Conference Proceedings
Authors: Hiroko Kanoh
Abstract

Generative artificial intelligence (AI) is increasingly shaping how university students search for information, write, create, communicate, and solve problems. In higher education, this situation requires not only operational skills for using AI tools, but also broader competencies such as critical evaluation, information literacy, ethical judgment, self-regulation, and collaborative reflection. This study examines a classroom practice using Personary, a digital mind-mapping platform with an optional AI-assisted mode, to explore how university students conceptualize competencies needed in the AI era. The activity was conducted at two Japanese universities. Students received a common instructional presentation on digital safety, misinformation, AI risks and benefits, cognitive bias, and digital well-being. They then discussed the question, “What competencies should university students develop in the AI era?” and created collaborative mind maps using Personary. Student-generated mind maps and written reflections were analyzed through interpretive map analysis and text-mining-assisted qualitative analysis. The results show that students understood AI-era competencies as multidimensional capacities rather than as technical skills alone. Their maps and reflections emphasized critical evaluation of AI-generated information, media and data literacy, autonomous thinking, communication, ethical responsibility, appropriate AI use, and adaptability. Personary supported the externalization and organization of these ideas, while the AI-assisted mode provided additional prompts for expanding selected branches. The study demonstrates how AI-supported mind mapping can function as a reflective learning activity for visualizing, sharing, and reorganizing students’ understanding of AI literacy in higher education.

Keywords: AI literacy education, AI-supported mind mapping, collaborative learning, human–AI interaction, qualitative classroom study

DOI: 10.54941/ahfe1008102

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