Empowering Teachers through Human-Centered AI for Content-Pedagogical Improvement across Diverse Sociocultural Settings

• In Event: Roundtable Session 6
  In Refereed Roundtable Session: AI Revolution in Education: Redefining Teaching and Teacher Identity

Proposal

The rapid development and adoption of AI-powered technologies present both opportunities and challenges for educational equity. While these technologies hold promise for personalizing learning and providing tailored support, research has shown they can also perpetuate and even worsen existing disparities if not carefully designed and implemented with equity and sociocultual awareness at the forefront (Akgun & Greenhow, 2022; Mohammed & Watson, 2019; Nazaretsky et al., 2022a).

Recent scholarship highlights a shift from technology-centric AI tools to interdisciplinary pedagogies that emphasize human-machine collaboration, authentic problem-solving, and creative tasks (Hwang & Chen, 2023; Kim, Lee, & Cho, 2022; Nazaretsky et al., 2022b), providing fresh evidence for the theoretical understanding of situated learning (Lave & Wenger, 1991) and the principles of improvement science (Langley et al., 2009; Lewis, 2015).

Our study investigates the integration of artificial intelligence (AI) in teacher development through collaborative lesson planning and inquiry in Hong Kong, Shanghai, Taiwan, and the United States. We explore how AI-powered tools can empower teachers to take an active role in enhancing their own practice and improving educational outcomes for all students, specifically by assisting in the analysis of student learning data, providing teachers with tools and materials to design instructional strategies tailored to the needs of diverse student groups, and informing schools and districts in providing personalized and adaptive in-service development experiences for teachers.

We utilize purposive sampling to identify 3-5 public schools in each setting that have been recognized as leaders in AI integration and innovation. In each school, the research team conducts content analysis of policies and semi-structured interviews (Patton, 2014) with school administrators, teacher leaders, and classroom teachers. Data analysis follows an inductive approach, using open and axial coding to identify emerging themes and patterns across the case study sites (Corbin & Strauss, 2014).

The findings reveal both convergent and context-specific factors that influence the effectiveness of AI integration efforts. We find that teachers across the four settings engage in similar processes of collaborative lesson planning and inquiry to integrate AI, such as elaborating problems, identifying content-pedagogical gaps, analyzing classroom practices, envisioning and testing alternatives, and reflecting on the effects. Crosscutting enabling conditions include the availability of AI-powered tools and resources, the level of teacher buy-in and digital literacy, and the presence of supportive leadership. Furthermore, the study highlights that the centralized versus decentralized nature of educational governance in these settings may shape the policies, resources, and supports available for teachers to effectively leverage AI in their professional development and instructional practices. We also find that organizational power distance influences the degree of AI integration and the effectiveness of collaborative inquiry and lesson planning efforts, as described in extant literature (Liu & Yin, 2023; Talbert, 2009).

Our exploratory and theory-informing study aligns well with the conference theme of "Envisioning Education in a Digital Society.” While limited in generalizability, findings from this comparative cross-cultural investigation provide valuable insights into how teachers can effectively leverage AI-powered tools and strategies to meet student learning needs and address inequitable distribution of quality instruction.

Author

• Xiu Cravens, Vanderbilt University