The Future of Learning: Building Trust and Transparency in AI Education

Abstract

Artificial intelligence has become the mark of the day; no one of the aspects of life is beyond it. Education is not only a field but a way to train and make the generation civilized, AI made this scenario easy and advanced; building confidence in AI-enabled education systems becomes a fundamental requirement for its successful application. This research article aims to investigate the connections among trust, privacy, and surveillance related to AI-facilitated educational contexts, drawing on insights from Christina Castelfranchi and Rino Falcone's work "Trust Theory: A Socio-cognitive and Computational Model." This study examines the multiple aspects of trust building, including computational dimensions and socio cognitive dimensions, which can be helpful but difficult to investigate the attitudes and actions in an AI-mediated educational environment. This article delivers an elaborate exploration of the drivers affecting trust in AI systems deployed for educational purposes by carefully examining trustworthiness, perceived dangers, and institutional trust. Additionally, thorough examination and inherent privacy would be the foundational aspects of privacy and surveillance issues associated with AI detection, protection, and surveillance risk mitigation measures. This scholarly discourse, supported by empirical evidence, illustrative case studies, and prescriptive recommendations, articulates actionable strategies for building trust and addressing privacy and surveillance concerns in AI-enabled educational landscapes, heralding the arrival of ethically sound and trustworthy educational technologies.