Reframing teacher digital competence for generative AI: a task–capability analysis of multi-platform educational discourse
TL;DR
Positive associations of all five task areas were found in pedagogical assistance; the greatest estimates were made with classroom enactment and professional learning; the competence model is confirmed, which is task sensitive and is based on assessment of capability, orchestration of instruction, the verification of evaluation, mediation of the learner and inquiry of the profession.
Abstract
Generative artificial intelligence (GenAI) has brought the issue of teacher digital competence into focus again, although it is possible to see that most of the frameworks list the skills and do not make any distinction between the tasks of teaching where AI applications are used. The research was based on six platforms provided to determine the expressions of five capabilities in which teachers were able to perform tasks. There were 12,861 records included in the analytic sample and they came from 300 source URLs. The highest capability expressions were efficiency (41.87%) and content development (34.76%). Positive associations of all five task areas were found in pedagogical assistance; the greatest estimates were made with classroom enactment and professional learning. Task-replacement language was less represented in classroom enactment and professional learning after multiplicity adjustment. The results confirm the competence model, which is task sensitive and is based on assessment of capability, orchestration of instruction, the verification of evaluation, mediation of the learner and inquiry of the profession. The corpus explains the expectations of discourse instead of the competence of teachers or effects of their instruction.