Ethical AI Frameworks for Digital Learning Platforms

Abstract

The deployment of artificial intelligence in digital learning platforms raises fundamental ethical questions that technical accuracy metrics alone cannot resolve: whether algorithmic recommendations inadvertently perpetuate educational inequity across demographic groups; whether automated assessment undermines the formative human relationship between teacher and student; whether learner data collected for educational improvement is used appropriately and with genuine consent; and whether AI-driven nudges towards particular learning pathways respect learner autonomy. Existing AI ethics frameworks -- from the EU AI Act to IEEE's Ethically Aligned Design -- provide principles but insufficient operationalisation for the specific context of educational AI. This paper proposes the Ethical AI in Education Framework (EAIEF), a comprehensive applied ethics framework specifically designed for digital learning platform contexts, built around five ethical pillars: fairness and non-discrimination, transparency and explainability, learner autonomy and consent, data stewardship and privacy, and accountability and governance. EAIEF evaluates 18 AI-enabled digital learning platforms against 84 operationalised ethical criteria, introduces the Educational AI Ethics Score (EAIES), and provides a practical ethics-by-design implementation guide for edtech development teams. Key results: mean EAIES across 18 platforms = 0.624 (SD = 0.148); fairness is the lowest-scoring pillar (mean 0.548); platforms that engage an ethics review board score 34.2% higher EAIES. The framework provides actionable ethical AI guidance for educational technology developers, institutions, and regulators.