GenAI as Digital Plastic: Understanding Synthetic Media Through Critical AI Literacy
Project Overview
The document explores the role of Generative Artificial Intelligence (GenAI) in education, referring to it as 'digital plastic' to illustrate its dual nature of potential benefits and inherent challenges. It highlights the necessity of Critical Artificial Intelligence Literacy (CAIL) as a vital component of multiliteracies, equipping learners to effectively navigate the complexities of synthetic media. The discussion acknowledges that while GenAI can significantly enhance both creative and academic outputs, it also introduces risks such as misinformation, bias, and the risk of homogenizing content, which could further intensify existing educational inequalities. The authors advocate for the promotion of CAIL to ensure equitable educational practices and to mitigate the disruptive impacts of GenAI on knowledge creation and dissemination, ultimately underscoring the need for a balanced approach to leveraging AI in educational contexts.
Key Applications
Generative AI for Educational Literacy and Language Learning
Context: Educational contexts focusing on multiliteracies, digital literacies, and language learning; target audiences include students, educators, and researchers.
Implementation: Integrating generative AI tools and critical AI literacy into curricula and educational frameworks to support language learning, critical engagement with AI-generated content, and understanding of AI's societal impacts.
Outcomes: Promotes the development of critical competencies, enhances multiliteracies, fosters responsible navigation of synthetic media, and informs discussions on educational justice and inequalities.
Challenges: Resistance to change in traditional educational practices, unequal access to technology among students, potential overreliance on AI tools leading to diminished creative expression, and navigating biases in AI outputs.
Implementation Barriers
Access Barrier
Disparities in access to AI technologies, particularly in the Global South, which may exacerbate existing inequalities in education. This includes reliance on large, well-funded corporations to develop AI models, limiting the ability of local entities to create their own solutions.
Proposed Solutions: Develop open-source and ethically trained AI models that can be accessed universally, and encourage local development of AI technologies while providing resources for training and implementation.
Cultural Barrier
The potential for AI-generated content to reinforce existing biases and knowledge hierarchies, undermining educational justice.
Proposed Solutions: Integrate critical discussions of bias and inclusivity into the educational frameworks surrounding AI.
Project Team
Jasper Roe
Researcher
Leon Furze
Researcher
Mike Perkins
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Jasper Roe, Leon Furze, Mike Perkins
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