Teachers’ Perceptions of Teaching Sustainable Artificial Intelligence: A Design Frame Perspective

Xiao-Fan Lin, Lu Chen, Kan Kan Chan, Shiqing Peng, Xifan Chen, Siqi Xie, Jiachun Liu and Qintai Hu
Additional contact information
Xiao-Fan Lin: Guangdong Provincial Engineering and Technologies Research Centre for Smart Learning, Guangdong Provincial Institute of Elementary Education and Information Technology, Guangzhou 510631, China
Lu Chen: School of Education Information Technology, South China Normal University, Guangzhou 510631, China
Kan Kan Chan: Faculty of Education, University of Macau, Macau, China
Shiqing Peng: School of Education Information Technology, South China Normal University, Guangzhou 510631, China
Xifan Chen: School of Education Information Technology, South China Normal University, Guangzhou 510631, China
Siqi Xie: School of Education Information Technology, South China Normal University, Guangzhou 510631, China
Jiachun Liu: School of Education Information Technology, South China Normal University, Guangzhou 510631, China
Qintai Hu: New Engineering Education Research Center, Guangdong University of Technology, Guangzhou 510090, China

Sustainability, 2022, vol. 14, issue 13, 1-20

Abstract: Teaching artificial intelligence (AI) is an emerging challenge in global school education. There are considerable barriers to overcome, including the existing practices of technology education and teachers’ knowledge of AI. Research evidence shows that studying teachers’ experiences can be beneficial in informing how appropriate design in teaching sustainable AI should evolve. Design frames characterize teachers’ design reasoning and can substantially influence their AI lesson design considerations. This study examined 18 experienced teachers’ perceptions of teaching AI and identified effective designs to support AI instruction. Data collection methods involved semi-structured interviews, action study, classroom observation, and post-lesson discussions with the purpose of analyzing the teachers’ perceptions of teaching AI. Grounded theory was employed to detail how teachers understand the pedagogical challenges of teaching AI and the emerging pedagogical solutions from their perspectives. Results reveal that effective AI instructional design should encompass five important components: (1) obstacles to and facilitators of participation in teaching AI, (2) interactive design thinking processes, (3) teachers’ knowledge of teaching AI, (4) orienteering AI knowledge for social good, and (5) the holistic understanding of teaching AI. The implications for future teacher AI professional development activities are proposed.

Keywords: artificial intelligence; grounded theory; teachers’ experience; teachers’ professional development; K-12 education (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/13/7811/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/13/7811/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:13:p:7811-:d:848770

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().