This critical literature review examines the use of digital learning environments in special and inclusive education, focusing on assistive technologies, adaptive platforms, artificial intelligence, online learning, smart learning environments, and teacher competence. The review addresses three main questions: what benefits these environments offer students with special educational needs and disabilities, what knowledge and competencies teachers need to use them effectively, and what barriers limit their inclusive value. The findings show that digital tools can improve access, communication, engagement, self-paced learning, and social participation when they offer flexible, multimodal ways to learn. However, technology alone does not create inclusion; its success depends on teacher guidance, institutional support, reliable infrastructure, accessibility by design, and careful ethical governance. The review also highlights risks such as cognitive overload, weak accessibility, unequal access, privacy concerns, and biased or opaque AI systems. Overall, digital learning environments can support more equitable education, but only when guided by learner needs, teacher judgement, and clear lines of responsibility.
Panagiotis Toumpos, Alexandros Gazis, T. Vavouras et al.· WSEAS Transactions on Advanc...· 0 citations
Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In high-stakes environments, ranging from journalism and finance to healthcare and legal contexts, the consequences extend to severe misinformation, market manipulation, identity fraud, and the erosion of institutional trust. This entry explores how modern visual intelligence and computer-vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaining how adversarial methods enhance realism and challenge existing detectors. The overview highlights visual artifacts, digital patterns, and physiological cues commonly leveraged in detection and reviews major CNN, transformer, and frequency-based approaches. It also summarizes evaluation practices and the difficulty of achieving strong generalization. Finally, it identifies emerging directions, including modern intelligence techniques for civilian and military content verification. This survey covers generation architectures (GANs, latent diffusion, neural rendering, video synthesis), the spatial, temporal, frequency-domain, and physiological artifacts they produce, and the detector families that exploit them. We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field’s central open challenge. Beyond detection, we discuss cryptographic provenance standards, watermarking, and regulatory frameworks (EU AI Act, DSA, GDPR). We conclude that effective deepfake governance requires defense in depth integrating forensic detection, verifiable provenance, and institutional accountability.
