AI-Augmented Education Platforms: Understanding Learner Identity Formation through Natural Language Interaction Analysis
Keywords:
AI-augmented education, learner identity, natural language processing, learning analytics, sociotechnical systems, algorithmic fairness, platform governanceAbstract
The rapid proliferation of AI-augmented education platforms has shifted the research focus from content delivery efficacy toward the more subtle process of learner identity formation. Natural language interaction data, generated through dialogue-based tutors, discussion forums, and conversational agents, now offers an unprecedented signal for understanding how learners construct, negotiate, and transform their academic self-concepts. This paper presents a system-level analysis of such platforms, emphasizing architectural design, natural language processing pipelines, and the governance frameworks required to interpret identity-related signals responsibly. We argue that identity formation is not an incidental byproduct of platform use but a structural feature deeply embedded in interaction design, feedback loops, and personalization algorithms. Through a synthesis of learning sciences, sociotechnical systems theory, and natural language processing research, we examine how platform architectures both enable and constrain identity exploration. We further analyze critical trade-offs between real-time identity inference and learner privacy, between algorithmic adaptation and the preservation of authentic self-narrative, and between global scalability and local cultural sensitivity. Robustness challenges stemming from noisy, context-dependent natural language data are discussed in relation to fairness, accountability, and long-term sustainability. Policy implications extend to data governance, algorithmic auditing, and the ethical obligations of platform providers operating across diverse educational jurisdictions. The paper contributes a conceptual framework that reframes AI-augmented platforms as socio-cognitive ecosystems where identity, language, and infrastructure are co-constituted, offering a path toward more humane and identity-aware educational systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
