Artificial intelligence in contemporary Moocs for teacher training: didactic, ethical, and technical analysis of courses through an emergent category system
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Abstract
The expansion of Artificial Intelligence (AI) usage is reflected in the increasing availability of teacher training courses. Focusing on Massive Open Online Courses (MOOCs), this research aims to understand how the educational value of AI is addressed in contemporary MOOCs designed for teacher education. This is achieved through the design and application of an emergent category system encompassing didactic-formative, ethical, and technical dimensions. A descriptive-interpretative qualitative methodology was applied, based on inductive content analysis. Throughout the process, an emergent category system with three progressive levels of complexity was constructed, refined, applied, and validated. This allowed the formulation of a three-level progression hypothesis: Level 1 – initial approach, Level 2 – functional application, and Level 3 – transformative integration. The sample consisted of 25 MOOCs selected from the Coursera platform. In terms of results, basic or intermediate levels predominated in relation to teaching competences, didactic integration, and technical use of AI. With few exceptions, there was limited critical and reflective depth. Particularly scarce were advanced ethical considerations such as data privacy and protection, as well as advanced didactic-formative aspects such as adaptive assessment. Courses exhibiting functional competence were the most developed and balanced. It is concluded that, although some notable training initiatives address didactic-formative, ethical, and technical dimensions in isolation, most of the courses analysed do not address the transformative potential of AI in education. On the other hand, the emergent category system and the proposed progression hypothesis constitute a valid tool for future analyses of AI's educational integration in teacher training.
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