Content area

Abstract

This paper explores the transformative potential of Generative Artificial Intelligence (GenAI) and audio-visual cloning technologies in reshaping digital education, grounded in the ongoing project ACCLAIMED (Artificial Intelligence Content Cloning of Language-Agnostic Media for Education Democratisation). As education systems increasingly rely on digital platforms, the imperative to ensure accessibility, inclusion, and ethical integrity becomes more pronounced. ACCLAIMED introduces a novel triadic framework, namely, Course Generator, Guardrail, and AI-Renderer, to address elearning challenges. The Course Generator collaborates with educators to produce comprehensive, pedagogically sound multilingual content. The Guardrail ensures human oversight, reinforcing societal norms, factual accuracy, and ethical alignment. The AI-Renderer transforms materials into realistic, human-like audio-visual formats, delivering engaging, culturally sensitive learning experiences. We discuss how ACCLAIMED advances the state-of-the-art by surpassing conventional AI tutors and adaptive learning systems through deeper pedagogical integration and ethical AI moderation. A key feature is its ability to deliver high-quality content across multiple languages, removing linguistic barriers and fostering educational equity especially for underserved or non-English-speaking populations. The paper also critically addresses broader issues: ethical concerns around AI-generated content, privacy and data protection (GDPR, EU AI Act), and digital sovereignty. Consideration is given to how such innovations can bridge or deepen the digital divide depending on their responsible and inclusive deployment. Ultimately, this paper calls for a reconceptualisation of digital learning, not merely as content delivery but as an inclusive, ethical, and adaptive ecosystem. It positions ACCLAIMED as a forward-looking blueprint for educational technologies prioritising innovation, equity, and societal impact.

Details

Title
Leveraging Generative AI and Audio-Visual Cloning to Democratise Digital Learning
Publication title
Pages
485-493
Number of pages
10
Publication year
2025
Publication date
Oct 2025
Publisher
Academic Conferences International Limited
Place of publication
Kidmore End
Country of publication
United Kingdom
ISSN
2048-8637
e-ISSN
2048-8645
Source type
Conference Paper
Language of publication
English
Document type
Conference Proceedings
ProQuest document ID
3279067038
Document URL
https://www.proquest.com/conference-papers-proceedings/leveraging-generative-ai-audio-visual-cloning/docview/3279067038/se-2?accountid=208611
Copyright
Copyright Academic Conferences International Limited 2025
Last updated
2025-12-05
Database
ProQuest One Academic