The integration of artificial intelligence (AI) into social media-based learning environments has revolutionized the way learners engage with content and interact with peers. This book chapter explores the transformative potential of AI-driven personalization, emphasizing the symbiotic relationship between algorithmic intelligence and social pedagogy. It provides a comprehensive examination of how adaptive learning algorithms dynamically curate content to enhance learner engagement, foster autonomy, and optimize knowledge retention. The chapter delves into the intricate balance between learner autonomy and algorithmic nudging, exploring how AI can support rather than constrain the learner's agency. The importance of ethical, privacy, and governance considerations in the deployment of AI technologies is also addressed, with a focus on ensuring transparency, fairness, and inclusivity within informal learning contexts. Through a critical review of existing frameworks and evaluation metrics, the chapter proposes a holistic model for AI-powered social learning that prioritizes learner experience, equitable access, and long-term sustainability. This research highlights the need for adaptive governance structures and emphasizes the importance of a learner-centered approach to AI integration in educational technology.
The rapid development of artificial intelligence (AI) has led to a transformative shift in education, particularly within informal learning spaces such as social media platforms [1]. These platforms, characterized by their interactive nature and vast user engagement, offer a rich environment for AI-driven personalized learning [2]. Social media-based learning environments present unique opportunities to engage learners in dynamic and collaborative ways [3]. AI systems can harness data generated by users’ interactions to curate content, recommend learning paths, and promote social learning, creating individualized educational experiences that adapt to the learner's needs [4]. The focus of this book chapter is on understanding the mechanisms through which AI can personalize learning in social media environments, and the implications of these technologies for both individual learners and larger educational ecosystems [5].
AI-driven personalization is increasingly recognized as a powerful tool for enhancing learning outcomes [6]. Adaptive learning algorithms, powered by AI, can dynamically adjust content and resources according to the learner's progress, interests, and learning style [7]. This creates an environment where learners receive the most relevant and timely content, fostering deeper engagement and more effective learning [8]. Personalized learning through AI provides the opportunity to cater to diverse educational needs, ensuring that each learner can progress at their own pace [9]. In the context of social media platforms, the potential for integrating AI into learning environments is vast, as these platforms already have established networks and rich social interactions that can be leveraged to enhance the learning experience [10].
The integration of AI into learning spaces is not without challenges. One of the most critical issues is finding a balance between learner autonomy and algorithmic nudging [11]. While AI can enhance personalized learning, it also has the potential to influence and limit the learner's decision-making process [12]. Algorithmic nudging refers to the subtle guidance AI systems provide, directing learners toward certain actions, content, or interactions based on their past behaviors [13]. This can help learners stay on track and discover relevant content, but it may also inadvertently restrict their freedom to explore outside the suggested pathways [14]. As AI systems become more integrated into social learning environments, understanding how to balance these competing forcesâ€â€ÂÂsupporting learner autonomy while leveraging the advantages of AI guidanceâ€â€ÂÂbecomes crucial [15].
