INTERGENERATIONAL PERCEPTIONS OF ARTIFICIAL INTELLIGENCE IN EXPERIMENTAL SCIENCES: EMPIRICAL FOUNDATIONS FOR A CRITICAL LITERACY MODEL

Artificial Intelligence (AI) is making progress in the realm of science education, bringing with it assurances of simulation, personalization, and instant feedback to the learning process. However, little research exists on the actual perception of these tools by high school teachers, who are key players in any form of meaningful innovation, particularly in Latin America, where there are also significant structural gaps. For the purpose of understanding these, we invited 72 science teachers from Ecuador to participate in a strict formulation of questions through a questioning process.We approached these for understanding through taking the voices from 72 science teachers in Ecuador in a strict questioning process. Eight out of ten teachers had never used tools such as LeonardoIA or Midjourney, but they received the highest ratings for their ability to run experiments, for which AI has many applications. They were very concerned about losing pedagogical control (39%), algorithmic biases (44%), and losing human interaction (48%). Believe it or not, age is irrelevant; digital natives and digital immigrants are similar in surprising ways. Eight out of 10 of them said that the biggest challenge was a lack of training based on their reality and discipline. Focusing on these elements, we suggest a four-pillar model of critical literacy: technical learning, pedagogical integration in a situated process, ethical-algorithmic awareness, and collective institutional action. The problem, succinctly, is not simply to persuade teachers of the value of AI—they know that already—but to make the case for the value of reflective and fair use of AI in a discursive manner.