On-Device AI in Education: A Privacy-First, Open-Source Alternative to Cloud-Based Models
Year: 2024
Type: other
Abstract: This poster, presented at the MassCUE Fall Conference 2024 at Gillette Stadium, Foxborough, Massachusetts, explores on-device artificial intelligence (AI) as a privacy-first, open-source alternative to widely-used corporate-run models such as ChatGPT and Claude. The work highlights the benefits of deploying AI directly on devices, ensuring enhanced data security, greater control over content, and reduced reliance on cloud-based infrastructures. Focused on educational applications, this approach empowers educators and learners with AI-driven tools that prioritize user privacy while maintaining robust functionality and learning outcomes. By breaking the dependency on corporate models, this research advocates for the democratization of AI technologies in K-12 and higher education. It also addresses ethical considerations in AI use, particularly regarding privacy, data security, and equitable access. This research is vital for educators, technologists, and policy makers seeking ethical, sustainable alternatives to traditional cloud-based AI models.
DOI: 10.7275/g6qd-km54
Author: Gattupalli, Sai
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6940053269
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