Adaptive Learning Environments: Integrating Artificial Intelligence for Special Education Advances

Students with special educational needs are confronted with various barriers that restrict their learning and performance in the educational environment. The lack of accessible and adapted educational resources tailored to their needs based on their disability negatively impacts their ability to interact effectively with both teachers and peers. In this context, Artificial Intelligence emerges as a promising solution to overcome some of these educational barriers by offering personalized tools, speech recognition technologies, and text-to-voice conversion technologies that allow students with disabilities to communicate seamlessly. Artificial Intelligence not only addresses the challenges mentioned previously but also opens up new possibilities for more effective learning and an enriching educational experience. The integration of these technologies could represent a significant advancement towards the creation of more accessible and egalitarian educational environments. This article focuses on identifying challenges from the perspective of teachers and parents, proposing the implementation of adapted educational resources using artificial intelligence. To establish a reference base for the needs and problems of students, a survey was conducted with the participation of 66 teachers and 112 parents in two developing countries, Ecuador and Guatemala. The results obtained offer an opportunity to improve student education by identifying possible solutions in adapting resources and promoting inclusion through the implementation of Artificial Intelligence. A recurring challenge in both countries is the lack of adequate infrastructure, specialized tools, inclusive methodologies, and software to facilitate the learning process in educational institutions

• Hlavní autor: Jadán-Guerrero, Janio (author)
• Další autoři: Tamayo-Narvaez, Karla (author), Méndez, Elena (author), Valenzuela, María (author)
• Médium: article
• Jazyk: eng
• Vydáno: 2024
• On-line přístup: https://link.springer.com/chapter/10.1007/978-3-031-61953-3_10; https://hdl.handle.net/20.500.14809/6949