Teacher agency in the age of generative AI: towards a framework of hybrid intelligence for learning design
<|reference_start|>Teacher agency in the age of generative AI: towards a framework of hybrid intelligence for learning design: Generative AI (genAI) is being used in education for different purposes. From the teachers' perspective, genAI can support activities such as learning design. However, there is a need to study the impact of genAI on the teachers' agency. While GenAI can support certain processes of idea generation and co-creation, GenAI has the potential to negatively affect professional agency due to teachers' limited power to (i) act, (ii) affect matters, and (iii) make decisions or choices, as well as the possibility to (iv) take a stance. Agency is identified in the learning sciences studies as being one of the factors in teachers' ability to trust AI. This paper aims to introduce a dual perspective. First, educational technology, as opposed to other computer-mediated communication (CMC) tools, has two distinctly different user groups and different user needs, in the form of learners and teachers, to cater for. Second, the design of educational technology often prioritises learner agency and engagement, thereby limiting the opportunities for teachers to influence the technology and take action. This study aims to analyse the way GenAI is influencing teachers' agency. After identifying the current limits of GenAI, a solution based on the combination of human intelligence and artificial intelligence through a hybrid intelligence approach is proposed. This combination opens up the discussion of a collaboration between teacher and genAI being able to open up new practices in learning design in which they HI support the extension of the teachers' activity.<|reference_end|>
@article{frøsig2024teacher,
    title={Teacher agency in the age of generative AI: towards a framework of
  hybrid intelligence for learning design},
    author={Thomas B Fr{o}sig (UniCA, LINE), Margarida Romero (UniCA, LINE)},
    journal={IRMBAM 2024, IPAG, Jul 2024, Nice, France},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06655},
    primaryClass={cs.AI cs.CY}
}