Sistemas de recomendação na formação intercultural: desafios e caminhos para a etnoeducação na América Latina

Recommender systems based on Artificial Intelligence (AI) can potentially transform educational experiences by suggesting content tailored to users’ realities. However, cultural biases in these systems can compromise their effectiveness in promoting inclusive and intercultural education. This article critically analyzes the impact of recommender systems on university ethnoeducation in Latin America, considering both their potential to enhance cultural diversity and the ethical and technical challenges associated with the reproduction of inequalities. We argued that adopting these technologies in teacher training depends on implementing ethical principles and strategies that ensure the representation of ethnic and linguistic communities. To this end, we proposed guidelines for developing culturally responsive recommender systems, including the incorporation of multicultural databases, the involvement of communities in the design of tools, and implementing mechanisms to mitigate algorithmic biases. We concluded that the intersection between AI and ethnoeducation can contribute to formulating more equitable public policies. This study contributes to the debate on implementing AI-based educational technologies that respect and promote social justice and cultural plurality.