Adaptive Learning Through Artificial Intelligence in Natural Sciences Teaching

Abstract

The objective of this research was to interpret the perceptions that underlie the teacher about adaptive learning through artificial intelligence in the teaching of natural sciences, for this purpose the interpretive paradigm was assumed under a qualitative approach. As a technique for collecting information, the semi-structured interview was applied; as an instrument, an interview guide was used, using four (4) questions. The unit of analysis was made up of five (5) teachers who teach the natural sciences subject for basic general education students. In order to interpret the information provided by the teachers, categorization was used as an analysis technique, which gave rise to the emergence of the following categories with their respective subcategories: Personalized learning (individual needs, adaptation of content), Inclusion (equal opportunities, physical barriers), Experiential learning (the student as the protagonist of their learning, learning through experience) and feedback (personalized feedback, personalized recommendations). It is concluded that adaptive learning through artificial intelligence in the teaching of natural sciences is essential to create a more inclusive, personalized and effective educational environment in the teaching of natural sciences. It is essential that teachers take advantage of these tools to design meaningful learning experiences, where students can put their knowledge into practice, solve real problems and develop 21st century skills such as creativity, collaboration and problem solving.