From ai knowledge to inclusive practice: how smarted supports teacher learning and inclusive education

This paper will analyze how the three interrelated intellectual outputs from the Erasmus+ SmartEd project can facilitate AI-related knowledge among teachers. To conduct a qualitative document analysis, the three intellectual outputs, namely the Teacher Training Program: AI Integration for Inclusive Education (IO1); the Adaptive Learning Modules: Tailored AI Solutions for Diverse Learners (IO2); and the SmartEd AI Learning Platform and its virtual-piloting report (IO3) were analyzed against the following five dimensions: AI basics and critical literacy; pedagogical application and instructional design; inclusion, differentiation and accessibility; ethics, human agency and responsible use; and reflection, collaboration and continuing professional learning. The findings reveal a coherent developmental pathway. Firstly, IO1 prepares the teacher participants for understanding AI concepts, ethical issues and pedagogy in terms of seven modules with a variety of structured activities including tool exploring, case analysis, collaborative work, self-assessment and reflection. Secondly, IO2 turns this conceptual knowledge into practice by using 70 adaptive activities related to different subjects. Thirdly, IO3 provides flexible and self-paced access to the learning material that will be useful beyond one training session. The evidence base to support the perceived relevance of the material is provided through descriptive evidence obtained through a seven-country virtual piloting with 206 teachers. During the virtual piloting, 91.7% of respondents evaluated the platform as high regarding the promotion of understanding of AI in inclusive education, 92.2% regarding the practical applicability, 93.2% regarding confidence and readiness and 94.2% regarding facilitating inclusive and differentiated methods. Thus, this study revealed the transferable professional-learning model of developing AI literacy, designing instruction, applying AI in an inclusive manner and developing reflectively. However, the evidence is perception-based only and cannot prove any causal effect on practice or learning outcomes.