Chinese EFL teachers’ AI literacy and motivation for AI-related CPD activities: a comparison between public and private schools

Thesis

As artificial intelligence (AI) is revolutionizing education, bringing unprecedented opportunities yet reshaping teachers’ roles, AI literacy has become essential for teachers to ensure effective, critical, and ethical integration of AI into teaching. Situated in four tier-1 mega-cities (Beijing, Shanghai, Guangzhou, and Shenzhen) in China, this study examined differences between public and private secondary school English teachers in AI literacy, the school support they receive for its development, and their motivation for AI-related CPD, and explored the relationships between these three areas. A mixed-method approach was used for a comprehensive understanding, combining an online-questionnaire (n=133 participants) and nine follow-up semi-structured interviews. Drawing on Ng et al.’s four- dimension conceptualization, teachers’ AI literacy was assessed in terms of Knowing and Understanding AI (KUAI), Applying AI (AAI), Evaluating AI Application (EAIA), and AI Ethics (AIE). Three types of need-satisfaction support (competence, autonomy, and collegial support) and five types of motivation (amotivation, external, introjected, identified, and intrinsic) were also examined based on Ryan and Deci’s (2020) Self-Determination Theory.

Quantitative findings revealed that public school teachers scored significantly higher than their private school counterparts in overall AI literacy, KUAI, EAIA, collegial support, overall motivation, and controlled motivation (external and introjected). Qualitative findings supported these disparities and highlighted the need for joint action from government and schools. Across school types, teachers reported relatively high levels of overall AI literacy, school support, and motivation for AI-related CPD, although qualitative data to some extent contradicted the results for the first two aspects. Teachers experienced most collegial support, and were largely motivated by autonomous motivation (identified and intrinsic). Significant positive correlations were consistently observed among the three domains, as well as among some of their sub-dimensions. These findings revealed the inequitable resource distribution between the two school types and the associated disparities in teachers’ AI literacy, school support, and motivation for AI-related CPD, although caution is needed in interpreting the results.

Authors: Wang, L

• DOI: 10.5287/ora-nym0mg97k
• Type of award: MSc taught course
• Level of award: Masters
• Awarding institution: University of Oxford
• Language: English
• Keywords: ALSLA; AI literacy; school support; online questionnaire; motivation; ALSLA2025; mixed-method approach; Self-Determination theory (SDT); Continuous professional development (CPD); EFL teachers; Chinese private schools; Chinese public schools; interview
• Subjects: Second language acquisition; English language--Conversation and phrase books--Mandarin; Artificial intelligence; Private schools; English language--Study and teaching--Mandarin speakers; Questionnaires--Data processing; Schools, Chinese; Interviews; English language--Study and teaching--Foreign speakers; Public schools; Applied linguistics; Education--China; School support teams; Mixed methods research