Virtual Education in Artificial Intelligence: A Conceptual Framework
Year: 2026
Type: preprint
Abstract: Background: Delivering rigorous, resource-heavy computer science and artificial intelligence curricula to remote, low-bandwidth environments presents severe technical and pedagogical infrastructure bottlenecks that traditional educational models cannot resolve. Methods: This conceptual policy analysis evaluates the efficacy of asynchronous instruction architectures, cloud-based virtualization environments, and administrative automation by synthesizing verified public deployment data and established pedagogical frameworks. Results: The structural mapping demonstrates that deploying lightweight, browser-based sandbox coding environments (e.g., Google Colab, Replit) eliminates severe local hardware friction, while Intelligent Tutoring Systems (ITS) and natural language processing (NLP) chatbots drastically optimize conceptual mastery and administrative overhead. Conclusion: To bypass the prohibitive capital costs that widen the digital divide, educational institutions and non-profit learning networks must pivot toward lightweight, cloud-virtualized sandbox architectures and form institutional consortiums to scale equitable technical training globally.
Source: Figshare
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7166327758