Abstract

The integration of artificial intelligence into social media-based learning environments is reshaping contemporary educational landscapes. This chapter presents a comprehensive framework that merges algorithmic intelligence with the principles of social pedagogy to ensure ethical, equitable, and learner-centric personalization. While AI offers unprecedented capabilities to tailor educational content and pathways, its application raises profound concerns surrounding data privacy, algorithmic bias, learner autonomy, and the commodification of behavioral data. Addressing these challenges, the proposed framework emphasizes ethical personalization strategies grounded in transparency, fairness, and participatory design. By examining key dimensions such as differential privacy, dynamic consent mechanisms, bias mitigation, and informed consent for minors, this work outlines pathways to embed responsible AI practices in educational ecosystems. The it explores the socio-technical implications of treating learners as data subjects within surveillance capitalism, advocating for governance models that prioritize dignity, agency, and inclusive pedagogical values. The chapter offers actionable insights for educators, technologists, and policymakers seeking to balance data-driven efficiency with humancentered learning philosophies in the digital age. 

Introduction

The digitization of education has accelerated with the proliferation of artificial intelligence (AI), transforming how knowledge is accessed, curated, and consumed. Social media-based learning environments have emerged as prominent platforms for informal and formal learning, offering rich, interactive spaces where learners engage through personalized content, peer interaction, and algorithmic recommendations [1]. These platforms harness user data to optimize content delivery, identify learning preferences, and predict academic trajectories. The this shift also introduces a complex web of ethical and pedagogical challenges. Learners are increasingly subjected to opaque decision-making systems that profile, segment, and influence their educational pathways based on behavioral analytics [2]. While the use of AI promises enhanced efficiency, engagement, and tailored learning experiences, the underlying data practices often lack transparency and accountability [3]. This evolving ecosystem necessitates a critical exploration of how algorithmic intelligence can be aligned with pedagogical principles to safeguard human dignity, equity, and intellectual autonomy [4]. The current educational discourse must thus address not only technological innovation but also the fundamental question of how education can remain ethically grounded and socially responsive in the face of algorithmic mediation [5]. Social pedagogy, traditionally rooted in relational education and community-oriented learning, provides a valuable counterbalance to the data-centric logic of AI. Unlike algorithmic systems that operate through quantitative optimization, social pedagogy emphasizes empathy, trust, dialogue, and shared meaning-making [6]. In the context of digital learning, these human-centered values become essential in ensuring that technology supports not just cognitive development but also emotional, social, and moral growth. Social pedagogy advocates for educational practices that see learners as whole individuals, embedded in cultural and interpersonal contexts [7]. When AI tools are designed without such considerations, the result can be a dehumanizing form of personalization that narrows educational objectives to metrics of performance and engagement, disregarding the broader developmental needs of the learner [8]. Integrating social pedagogy into AI-driven educational systems requires reimagining how algorithms function—not merely as tools of prediction, but as collaborators in fostering meaningful, context-aware, and learner-led experiences [9]. By embedding pedagogical ethics into the design and implementation of AI, the educational community can move toward a more inclusive, reflective, and democratic model of digital learning [10].