Proceeding International Conference on Educational Technology and Social Humanities
Proceeding International Conference on Educational Technology and Social Humanities (ICoTESH) is an international peer-reviewed conference proceeding published annually by Gerakan Edukasi. The proceeding disseminate high-quality research presented at the International Conference on Educational Technology and Social Humanities (ICoTESH), providing a global platform for academics, researchers, educators, policymakers, industry practitioners, and postgraduate students. The conference focuses on advancing interdisciplinary scholarship in educational technology, artificial intelligence, social humanities, and sustainable development through innovative research and international collaboration. ICoTESH 2026 adopts the theme: "Human-Centered Artificial Intelligence, Educational Innovation, and Social Transformation for the Sustainable Development Goals." Accepted papers undergo a peer-review process before publication in the conference proceedings. Selected papers may be recommended for publication in partner national and international journals based on editorial evaluation. The conference welcomes original research papers covering, but not limited to, the following areas: Artificial Intelligence and Future Learning Educational Technology and Digital Transformation Sustainable Education and the Sustainable Development Goals (SDGs) Social Humanities in the Digital Age Inclusive Education and Social Equity Future Skills, Entrepreneurship, and Workforce Development Well-Being, Mental Health, and Human Development Governance, Ethics, and Policy in Education and Technology The proceedings aim to foster international collaboration and disseminate innovative research that contributes to sustainable educational transformation and social development.
Trust in AI, Cognitive Offloading and Learning Outcomes in Higher Education: A Moderated Mediation Model of Critical AI Literacy
Purpose – This study develops and pilots a moderated mediation model of how university students’ trust in AI-generated knowledge relates to learning outcomes through cognitive offloading, and how critical AI literacy conditions this indirect process. Design/methodology/approach – Survey data from students who use generative AI for academic work (N = 27) were analysed using partial least squares structural equation modelling (Smart-PLS 4), with the interaction estimated via the two-stage approach and significance assessed by bootstrapping. Findings – Trust positively predicted cognitive offloading. Contrary to expectation, offloading positively predicted self-reported learning; combined with a negative direct trust–learning path, this produced competitive mediation. Critical AI literacy was the strongest predictor of learning and showed a directionally consistent but non-significant buffering of the offloading–learning path, so moderated mediation was supported in direction only. Research limitations/implications – The small pilot sample underpowers the interaction; estimates are preliminary and require confirmation with roughly 160 cases and objective learning measures. Practical implications – Critical AI literacy merits treatment as a core curricular competency rather than an optional add-on. Originality/value – The paper reframes the AI-in-learning debate as a conditional process with a specified mechanism and boundary condition and validates a new instrument for confirmatory testing.
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