Session Information
99 ERC SES 03 C, Interactive Poster Session
Poster Session
Contribution
The use of artificial intelligence (AI) in teaching is no longer a novel concept, however, the use of AI in basic education still remain some issues. This study concerning education equality is part of findings of exploring teachers and principals experiences of using AI in their daily teaching work as well as the practical issues they are facing in state schools in China.
The key research question is: Why has AI not been implemented school-wide? Following by two sub-questions: (1) what is the current status of AI usage within schools, (2) why teachers and principals support or oppose AI integration? To answer these questions, I choose the teacher agency framework (Priestley et al., 2015) as the theoretical basis for my empirical research. Interviews and questionnaires were conducted with teachers and principals across three dimensions in the teacher agency framework: their past life and teaching experiences, current resources, and perspectives on future teaching (Priestley et al., 2015). During the data collection process, principals and teachers frequently mentioned that whether every student within a school or a class could have equal access to intelligent devices is one of their concerns of using AI school-wide, especially in schools with a large number of classes and students whose family situation are significantly different. It is evident that both principals and teachers take the issue of educational equality into account when using AI.
Method
This study is part of a larger project exploring how teachers mediate the use of AI in Chinese education, the data collection methods align with the larger project. Multiple case study has been chosen to explore different findings (Yin, 2018) based on students from different ages. The maximum variation (Patton, 2014) case selection strategy has been adopted to gain a comprehensive understanding of the research questions since applying AI technology in classroom teaching is still emerging in China. On a voluntary basis, four schools agreed to participate in the study, including one primary school, one middle school, one high school, and one nine-year integrated school, covering all stages of basic education. A combination of methods are applied in each case, includes desk-based research, questionnaire surveys, semi-structured interviews and observations. Desk-based research was first undertaken to analyze policies from the national to the local level. Surveys and interview questions were designed based on policy analysis and the theoretical framework. The surveys were distributed to selected schools, after teachers completed them within two weeks, the interview questions were slightly adjusted accordingly based on the survey data. Following this, interviews with teachers and principals were conducted. Subsequently, observations were carried out after obtaining schools ethical approval and securing the consent of both teachers and principals. The findings for this study were derived through thematic analysis of the interview content.
Expected Outcomes
In terms of educational equality, my empirical study reveals two main findings. First, from the perspective of school principals, some schools are unable to equip every classroom with intelligent teaching devices. Under such circumstances, these schools choose not to implement the technology widely but instead reserve its use for demonstration lessons to avoid inequality where some classes have access to the technology while others do not. Second, from the perspective of teachers, recognizing that not all students have access to devices that support AI software, they choose not to assigning homework that requires the use of such devices. The significance of this study lies in its examination of current challenges associated with the use of AI from the perspectives of teachers and school principals. Although the research is situated in the Chinese educational context, whether the use of AI hinders or promotes address educational inequality continues to be debated (OECD, 2019; UNESCO, 2019, 2021). These findings will be relevant to primary and secondary education world-wide, highlighting the challenges that frontline educators face in the current education context.
References
OECD. (2019). Recommendation of the Council on Artificial Intelligence. OECD. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449 UNESCO. (2019). Beijing consensus on artificial intelligence and education. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000368303 UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000380455 Yin, R. K.(2018). Case study research and applications: Design and methods. Priestley, M. R., Biesta, G., & Robinson, S. (2015). Teacher Agency: An Ecological Approach. Bloomsbury Academic. https://books.google.co.uk/books?id=DwZbEAAAQBAJ Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. Sage publications.
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