Objective: The study aimed to investigate factors affecting Chinese teachers' concerns about teaching artificial intelligence (AI), focusing specifically on the relationships between teachers' knowledge, perceived social good, and concerns about teaching AI.
Methods:
- Surveyed 269 K-12 AI teachers in southern China
- Used structural equation modeling (SEM) to test hypothesized relationships
- Conducted confirmatory factor analysis (CFA) to validate measurement scales
- Supplemented with semi-structured interviews of 18 teachers
- Measured teachers' knowledge (pedagogical AI knowledge and conceptual AI knowledge), perceived social good, and concerns about teaching AI
- Applied the concerns-based adoption model (CBAM) and pedagogical content knowledge (PCK) framework
Key Findings:
- Teachers' perceived social good of teaching AI partially mediated relationships between:
- Pedagogical AI knowledge and refocusing concerns
- Conceptual AI knowledge and management concerns
- Teachers' knowledge predicted higher stages of concern when mediated by perceived social good
- General pedagogical knowledge showed no significant effects on perceived social good or concerns
- Teachers with higher proficiency possessed greater pedagogical AI knowledge
- Medium/low-proficiency teachers focused more on conceptual knowledge
- The mean scores indicated participants were well-trained for AI education
Implications:
- Provides theoretical framework for understanding teachers' concerns in AI education reform
- Offers insights for designing professional development programs
- Suggests different strategies needed for novice vs experienced teachers
- Highlights importance of incorporating social good aspects in teacher training
- Demonstrates need for both technical and ethical components in AI teacher education
- Supports development of targeted support systems based on teachers' experience levels
Limitations:
- Focus only on teachers from southern China limits generalizability
- Self-reporting may introduce bias in measurements
- Cultural factors like academic competition emphasis may affect results
- Limited exploration of generative AI applications
- Resource availability and AI literacy levels vary across regions
- Time constraints in implementing AI curriculum
Future Directions:
- Expand research to diverse cultural and educational settings
- Incorporate generative AI in future studies
- Conduct longitudinal observations of interventions
- Explore additional relationships between knowledge and concerns
- Test effectiveness of social good-focused training programs
- Investigate impact of varying economic conditions and resources
- Study implementation in different educational priorities contexts
Title and Authors: "Exploring Chinese teachers' concerns about teaching artificial intelligence: the role of knowledge and perceived social good" by Xiao-Fan Lin, Weipeng Shen, Sirui Huang, Yuhang Wang, Wei Zhou, Xiaolan Ling, and Wenyi Li
Published On: January 10, 2025
Published By: Asia Pacific Education Review