Machine Unlearning for Responsible and Adaptive AI in Education

TL;DR

The paper investigates how to address the irreversibility of learning in ML-driven education systems, where sensitive learner data, dynamic contexts, and high-stakes decisions create governance and ethical challenges. It proposes Machine Unlearning (MU) as a family of techniques that enables selective forgetting through data minimization, post-hoc correction, and long-term maintenance, across approaches such as Exact, Approximate, gradient-based, distillation-based, and federated unlearning. A key contribution is the MU4RAAI framework, a four-layer reference architecture that integrates MU into responsible and adaptive AI for education, with mappings of MU interventions to privacy, security, fairness, and adaptability concerns. The work provides a conceptual foundation for harmonizing ethical principles with technical optimization in educational AI and outlines directions for empirical validation, governance, and future research in real-world educational ecosystems.

Abstract

Machine Unlearning (MU) has emerged as a promising approach to addressing persistent challenges in Machine Learning (ML) systems. By enabling the selective removal of learned data, MU introduces protective, corrective, and adaptive capabilities that are central to advancing Responsible and Adaptive AI. However, despite its growing prominence in other domains, MU remains underexplored within education, a sector uniquely characterized by sensitive learner data, dynamic environments, and the high-stakes implications of algorithmic decision-making. This paper examines the potential of MU as both a mechanism for operationalizing Responsible AI principles and a foundation for Adaptive AI in ML-driven educational systems. Drawing on a structured review of 42 peer-reviewed studies, the paper analyzes key MU mechanisms and technical variants, and how they contribute to the practical realization of Responsible and Adaptive AI. Four core intervention domains where MU demonstrates significant promise are identified: privacy protection, resilience to adversarial or corrupted data, fairness through bias mitigation, and adaptability to evolving contexts. Furthermore, MU interventions are mapped to the technical, ethical, and pedagogical challenges inherent in educational AI. This mapping illustrates the role of MU as a strategic mechanism for enhancing compliance, reinforcing ethical safeguards, and supporting adaptability by ensuring that models remain flexible, maintainable, and contextually relevant over time. As a conceptual contribution, the paper introduces MU4RAAI, a reference architecture integrating MU within Responsible and Adaptive AI frameworks for educational contexts. MU is thus positioned not merely as a data deletion process but as a transformative approach for ensuring that educational AI systems remain ethical, adaptive, and trustworthy.

Figures (3)

• Figure 1: Linking Characteristics to Challenges
• Figure 2: Mapping MU Interventions to ML-Driven Education Challenges
• Figure 3: Machine Unlearning for Responsible and Adaptable AI (MU4RAAI) Framework in Educational Context