Browsing by Author "Rabel, Brianne"

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    TEACHER PERCEPTIONS OF AIED: Exploring Future Scenarios in Secondary Education
    (2024-10-14) Rabel, Brianne; University of Gothenburg/Department of education, communication and learning; Göteborgs universitet/Institutionen för pedagogik, kommunikation och lärande
    Purpose: This thesis investigates the integration of Artificial Intelligence (AI) in secondary education by examining educator perceptions, with the aim to understand how AI might either support or complicate educational practices. It explores potential trajectories, both optimistic and pessimistic, regarding AI’s role in enhancing educational productivity and transforming teacher-student dynamics. Theory: This thesis is based on the Social Construction of Technology (SCOT) framework, with a focus on the concept of interpretive flexibility, a key aspect of SCOT (Pinch & Bijker, 1984). Specifically, I draw on the work of Doherty, Coombs, & Loan-Clarke (2006), who redefine interpretive flexibility by considering both the technology's capacity to accommodate different perspectives and the stakeholders' initial interpretations of the technology's capabilities. Users then utilize and adapt the technology, allowing for these different interpretations to be expressed and maintained. As a result, interpretive flexibility suggests that technology can be understood and employed in various ways, underscoring the influence of social processes in shaping technological tools (Doherty, Coombs, & Loan-Clarke, 2006). In this research, interpretive flexibility serves as a framework to explore how AI technologies might either enable or limit teachers' abilities to interpret and utilize these tools within diverse and often conflicting educational and social contexts. This concept sheds light on the differing views of AI among teachers, shaped by their unique experiences, values, and the specific educational settings in which they operate. Understanding the full impact of AI in education on productivity and teacher skills requires acknowledging that AI can both support and constrain various interpretations and meanings. Method: The research methodology combines speculative design with empirical qualitative analysis. Future scenarios – both optimistic and pessimistic – were crafted based on extensive literature review and existing theoretical frameworks. These scenarios were employed as narrative tools in semi-structured interviews with secondary education teachers to elicit their perceptions, concerns and hopes regarding the integration of AI in education. This approach allows for an in-depth exploration of the potential impacts of AI from the educators’ viewpoints. Results: The findings reveal a dual perspective among educators. On the positive side, AI is seen as having the potential to significantly reduce teacher workload, personalize learning experiences, and enhance administrative efficiency, potentially boosting educational productivity by allowing teachers more time for direct instruction and student 3 engagement. However, concerns are significant regarding ethical implications, potential increases in educational inequity, and the erosion of teacher autonomy. Educators are particularly wary of AI-driven approaches that prioritize efficiency and standardization, fearing these could undermine the depth of learning and critical engagement essential in education. The study underscores the complexity of how AI could influence educational productivity, highlighting the need for metrics that recognize deeper learning and critical thinking over simplistic output efficiency. This emphasizes the necessity for a balanced approach that includes ethical considerations, teacher involvement in AI tool development, and ongoing policy support to ensure AI enhances rather than undermines educational goals.