When fairness is an abstraction: Equity and AI in Swedish compulsory education
Project Overview
The document explores the role of generative AI in Swedish compulsory education, emphasizing the need for a careful examination of fairness and equity in its application. It critiques the tendency to implement AI technologies without considering their sociopolitical implications, which could potentially deepen existing inequalities. The analysis highlights three relevant social groups engaged with AI in education: those pursuing economic gains, those prioritizing pedagogical outcomes, and those advocating for citizen rights and active participation. It underscores the importance of recognizing that fairness is not an automatic characteristic of AI systems but a sociopolitical value that necessitates active management and consideration. Overall, the findings call for a more nuanced approach to integrating AI in educational contexts, ensuring that its deployment contributes positively to equity and enhances educational experiences for all students.
Key Applications
Adaptive learning technologies using AI
Context: Swedish compulsory education, targeting students and teachers
Implementation: AI applications are integrated into educational processes to offer personalized learning experiences.
Outcomes: Potential to improve educational access and quality, allowing for individualized instruction based on student needs.
Challenges: Risks include biased data leading to discrimination, lack of transparency in AI decision-making, and potential exacerbation of existing inequalities.
Implementation Barriers
Technical
Data bias and lack of transparency in AI algorithms can lead to unfair educational outcomes.
Proposed Solutions: Ensure ethical AI design practices and involve educators in the development process.
Sociopolitical
Decentralization in the education system may lead to unequal access to resources and qualified teachers.
Proposed Solutions: Develop policies that address socioeconomic disparities and ensure equitable resource allocation across schools.
Project Team
Marie Utterberg Modén
Researcher
Marisa Ponti
Researcher
Johan Lundin
Researcher
Martin Tallvid
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Marie Utterberg Modén, Marisa Ponti, Johan Lundin, Martin Tallvid
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