Letramento em inteligência artificial generativa para professores de ensino superior: um mapeamento sistemático da literatura

Abstract

The field of education has undergone transformations over the years, partly driven by information technologies utilized at various levels of the teaching-learning process. With the emergence of Generative Artificial Intelligence (GenAI), educators have sought to develop new pedagogical competencies to incorporate this technology into their professional practice. In this context, this research presents a Systematic Literature Mapping (SLM) that examines how the literature addresses GenAI literacy for teachers, particularly those in higher education. Searches were conducted across established digital databases, resulting in the selection of 37 articles for final synthesis out of a total of 3,371 candidate studies. The evaluation was peer-conducted, replicable, and based on specific inclusion and exclusion criteria. The results indicate that GenAI literacy comprises multidimensional skills. Key skills identified include (i) the development of critical thinking, (ii) the assessment of the reliability of GenAI-generated responses, and (iii) ethical and legal considerations. Training strategies were also identified, categorized into (i) practical activity-based approaches, (ii) active learning methodologies, and (iii) the need for continuing education. Challenges faced by higher education faculty were highligh-ted, specifically: (i) ethical and legal challenges, (ii) pedagogical challenges, (iii) technical and functional challenges, and (iv) institutional and/or socioeconomic challenges. The results suggest that integrating Generative AI into educational practices depends not only on the development of individual competencies and skills but also on the implementation of collective, multidisciplinary training policies, alongside institutional programs for the continuous professional development of teachers.