Teacher Identity and Teachers’ Adoption of Generative AI

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

Proposal

Purposes
This study aims to explore the relationship between K-12 teachers’ attitudes toward adopting generative artificial intelligence (AI) as pedagogical tool and their commitment, instructional beliefs, self-efficacy, and agency — constituting what we collectively define as teacher professional identity—through mixed methods design.
Conceptual Framework
Building upon existing literature, we underscore four pivotal dimensions within teachers’ professional identity: commitment, instructional beliefs, self-efficacy, and agency (Beijaard et al., 2004; Canrinus et al., 2011; Day et al., 2006). Teachers’ commitment to their profession entails their level of identification and dedication (Day et al., 2006; Hagenauer et al., 2015), reflecting their personal investment, pride, and loyalty to their role (Day et al., 2006). Their beliefs about teaching encompass attitudes, values, and assumptions regarding education (Beijaard et al., 2004; Day et al., 2006; Hagenauer et al., 2015), including perceptions of educational purpose, knowledge, and the teacher-student dynamic. Self-efficacy, rooted in Bandura’s theory (2006), relates to teachers’ confidence in executing teaching tasks effectively, encompassing instructional skills, classroom management, and student engagement (Bandura et al., 1999; Tschannen-Moran & Hoy, 2001). Lastly, teacher agency, as delineated by Alsup (2018) and Drake et al. (2001), denotes teachers’ autonomy and empowerment in their professional roles, enabling them to make decisions, influence policy, and advocate for students and the teaching profession (Beijaard et al., 2004; Day et al., 2006; Hagenauer et al., 2015).
Research Design
The mixed methods approach allows for a comprehensive understanding of these relationships by integrating quantitative and qualitative data.
Quantitative Phase: The quantitative phase involves the administration of an online survey to K-12 teachers, focusing on their attitudes toward integrating generative AI in teaching and their perceptions of commitment, instructional beliefs, self-efficacy, and agency. The survey utilizes validated scales to measure these constructs, providing quantitative data for analysis.
Qualitative Phase: Following the quantitative phase, a subset of participants will be selected for in-depth interviews or focus group discussions. These qualitative methods aim to delve deeper into teachers’ experiences, perceptions, and attitudes toward generative AI and its impact on their professional identity. Through open-ended questions, themes related to commitment, instructional beliefs, self-efficacy, and agency will be explored in detail.
Integration: Data from both phases will be analyzed separately initially, using quantitative techniques such as correlation analysis and regression analysis for the survey data, and thematic analysis for the qualitative data. Subsequently, findings from both phases will be triangulated.
Implications
The findings will inform educational policymakers, administrators, and practitioners about the potential implications of integrating generative AI in teaching practices and its impact on teacher identity. Moreover, the study contributes to the broader discourse on educational equity by reconceptualizing generative AI as a transformative tool that can challenge existing norms and empower teachers in their professional roles.

Author

• Chenchen Lu, Purdue University