HYBRID ANALYSIS IN EDUCATION: COMPARING CHATGPT AND HUMAN THEMATIC ANALYSIS OF TEACHERS' RESPONSES ON TECHNOLOGY USE IN SCHOOLS

This article aims to explore the potential of using Artificial Intelligence (AI) to support analysis methodologies. The conversational process with data can reveal conceptual themes that might elude human interpretation. In the complex contexts of the post-digital era it is timely to reconceptualize educational research as a hybrid system of interactions involving both human and non-human agents.
We aim to gather teachers' perceptions regarding the use of technologies in classrooms. The questions focus on both the potential for integrating digital resources into classroom teaching and the strategies for addressing the challenges it presents at the secondary education level. The research is guided by the idea of determining whether the integration of digital and virtual elements into daily life, which is increasingly normalized, is also reflected in school contexts, and whether this hybridity can be a valuable resource for educational purposes. In line with the topic, we intend to apply the principle of hybridity to our research methodologies. Specifically, we will compare two modes of analysis applied to a corpus of N=1023 teachers' responses on digital education: Reflexive Thematic Analysis and an automated analysis conducted by a pre-trained generative system (ChatGPT - OpenAI). Our objectives are twofold: a) to employ the chatbot as a
research assistant to reduce time and complexity, and to provide a confirmatory function for our analysis; b) to explore the possibility of partially revealing the subjective influence of the researcher’s biases in the grounded analysis process.