An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending
Project Overview
The document highlights Somekone, an explainable AI (XAI) educational tool aimed at K-12 students in grades 4-9, designed to foster an understanding of social media mechanisms through interactive learning. By focusing on four key AI concepts—data collection, profiling, engagement, and recommendation algorithms—the tool utilizes an Instagram-like interface that allows students to visualize and engage with these concepts, thereby enhancing their AI literacy and awareness of the ethical implications surrounding social media usage. In an educational intervention involving 12 test sessions with 209 children, the initiative demonstrated the significance of equipping young learners with essential skills to navigate digital environments responsibly, ultimately fostering a generation of informed and ethical digital citizens. The findings underscore the potential of generative AI tools in education to promote critical thinking and responsible engagement with technology among youth.
Key Applications
Somekone - an XAI education tool
Context: K-12 classrooms, specifically for students aged 11-16 (grades 4-9)
Implementation: Integrating Somekone into classroom activities to teach AI concepts related to social media
Outcomes: Enhanced data agency, AI literacy, and understanding of AI ethics among students
Challenges: Simplification of complex social media mechanisms might lead to misconceptions; potential lack of depth for advanced learners
Implementation Barriers
Educational Barriers
The risk of students generalizing the simplified model of Somekone as a true representation of real social media, which may lead to misconceptions.
Proposed Solutions: Future research and development to integrate more advanced AI concepts and ethics into the learning experience.
Project Team
Nicolas Pope
Researcher
Juho Kahila
Researcher
Henriikka Vartiainen
Researcher
Mohammed Saqr
Researcher
Sonsoles Lopez-Pernas
Researcher
Teemu Roos
Researcher
Jari Laru
Researcher
Matti Tedre
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Nicolas Pope, Juho Kahila, Henriikka Vartiainen, Mohammed Saqr, Sonsoles Lopez-Pernas, Teemu Roos, Jari Laru, Matti Tedre
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai