The Ethics of AI in Education
Project Overview
The document explores the transformative role of generative AI in education, highlighting its diverse applications, from personalized learning experiences to automated grading and feedback. It underscores how AI tools can enhance student engagement and facilitate differentiated instruction, catering to individual learning needs. However, the discussion also delves into the ethical implications surrounding AI in education, particularly the risks of biases embedded in AI systems that can perpetuate inequalities. It stresses the importance of developing AI tools that are fair and equitable, ensuring they are designed with a deep understanding of the socio-technical contexts in which they are implemented. The findings suggest that while generative AI holds significant potential to improve educational outcomes, it also necessitates a commitment to ethical standards and practices to mitigate potential harms such as algorithmic bias. Overall, the document calls for a balanced approach that harnesses the benefits of AI while prioritizing inclusivity and fairness in educational technology development.
Key Applications
Data-Driven Learning Enhancement Systems
Context: AI applications used in educational settings, including schools, to analyze learning data, provide personalized experiences, and create transparent environments that empower students by allowing them to view and modify their learning data.
Implementation: These systems leverage machine learning algorithms and open learner models to analyze educational data, enabling personalized learning experiences while fostering student agency and ownership of their learning data.
Outcomes: Enhanced student engagement and empowerment, improved learning experiences, and potentially reduced achievement gaps through personalized learning paths.
Challenges: Bias in data leading to unequal outcomes for different demographics, reliance on traditional pedagogies that may not suit all learners, and technical challenges in developing flexible designs that accommodate diverse learner needs.
Implementation Barriers
Technical
Limited access to adequate technology and internet connectivity in schools, particularly in rural areas.
Proposed Solutions: Invest in infrastructure improvements and ensure equitable access to technology for all schools.
Cultural
Dominance of mainstream language and cultural references in AIED systems, which may hinder accessibility for non-native speakers.
Proposed Solutions: Incorporate diverse linguistic and cultural perspectives in AIED design to make systems more inclusive.
Project Team
Kaska Porayska-Pomsta
Researcher
Wayne Holmes
Researcher
Selena Nemorin
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Kaska Porayska-Pomsta, Wayne Holmes, Selena Nemorin
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