Empowering educators to be AI-ready
By R. Luckin, Mutlu Cukurova, Carmel Kent, John Benedict du Boulay. Computers and Education: Artificial Intelligence, 2022
354 citations recorded by the source corpus.
In this paper, we present the concept of AI Readiness, along with a framework for developing AI Readiness training. ‘AI Readiness ’ can be framed as a contextualised way of helping people to understand AI , in particular, data-driven AI. The nature of AI Readiness training is not the same as merely learning about AI. Rather, AI Readiness recognises the diversity of the professions, workplaces and sectors for whom AI has a potential impact. For example, AI Readiness for lawyers may be based on the same principles as AI Readiness for Educators. However, the details will be contextualised differently. AI Readiness recognises that such contextualisation is not an option: it is essential due to the multiple intricacies, sensitivities and variations between different sectors and their settings, which all impact the application of AI. To embrace such contextualisation, AI Readiness needs to be an active, participatory training process and aims to empower people to be more able to leverage AI to meet their needs. The text that follows focuses on AI Readiness within the Education and Training sector and starts with a discussion of the current state of AI within education and training, and the need for AI Readiness. We then problematize the concept of AI Readiness, why AI Readiness is needed, and what it means. We expand upon the nature of AI Readiness through a discussion of the difference between human and Artificial Intelligence, before presenting a 7-step framework for helping people to become AI Ready. Finally, we use an example of AI Readiness in action within Higher Education to exemplify AI Readiness.
Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology
View Paper: https://www.semanticscholar.org/paper/f07d5ae0bd63f1600392b6f3b718b78b4de655b8
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Citations in the corpus
- Leading teachers' perspective on teacher-AI collaboration in education (2023, neutral): “Second, institutional guidance that clearly articulates the visions and goals of TAC, role and task distributions in TAC, and teachers’ AI readiness training toward TAC needs to be framed to effectively contextualize them into the actual classroom practice (Luckin et al., 2022).”
- Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT (2023, background): “…that these three roles underscore the importance of teacher-student interaction in understanding the teacher-AI relationship (Choi et al., 2023a; Luckin et al., 2022; Kim et al., 2022; Xu & Ouyang, 2022), as Teacher 9 stated that teachers’ pedagogical decisions regarding ChatGPT should be made…”
- Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy (2023, background): “In addition, recent educational researchers have argued that AI literacy is a minimum learning outcome for all post-secondary and K12 students (Kandlhofer et al., 2016; Luckin et al., 2022; Ng et al., 2021; Su et al., 2022).”