By: Rick Voithofer
About CDLI AI Research Update: Each month CDLI presents five recent research articles on Artificial Intelligence and Education and provides a synthesis of the issues raised and contributions of each study. A full APA citation is provided along with a direct link to the PDF on the OSU library site at the end of this page.
This month’s five research articles address the following three topics related to AI and K12 education: 1) early Childhood AI competencies (Su, Ng and Chu, 2023); 2) teacher acceptance and attitudes towards AI (Darayseh, 2023; Yau, et al. 2023), and 3) teacher’s AI competencies (Ng, et al. 2023; Celik, 2023). You’ll notice the international nature of the articles. Most of the research being conducted with teachers is being done outside the US.
While AI captured the public’s imagination in early 2023, educators and scholars have been crafting frameworks for AI literacy since 2016 (Burgsteiner, Kandlhofer, and Steinbauer, 2016). These frameworks enable educators to assess and harness AI for enhanced collaboration and communication. Su, Ng, and Chu (2023) conducted a literature review on AI competencies in early childhood, offering insights into various AI literacy approaches, encompassing content, assessment methods, skills, and teaching strategies. Many articles in this review evaluated student outcomes in AI literacy, consistently highlighting teachers’ lack knowledge, skills, and confidence regarding AI. This study serves as a foundational reference for determining when and how AI literacy should be embedded within general and specific curriculum standards. The remaining four articles shed light on teachers’ receptiveness and proficiency in relation to AI.
Darayseh (2023) used the popular technology acceptance model (TAM) to study the acceptance of AI by 83 science teachers in Abu Dhabi, UAE. The study revealed that teachers expected benefits of AI, AI ease of use, and their attitude toward AI applications highly predicated their intentions to AI. Interestingly, the study found no difference based on gender, teaching experience, or qualifications related to the participants’ intentions to use AI in teaching science. The article covered a wide range of applications including smart private teaching, adaptive learning environments, AI-based assessment, and smart content. Thinking ahead UAE revised its teacher standards to include AI competencies in 2018 and undertook a systemic integration of it into teacher preparation and adoption of learning tools. Studying secondary teachers in Hong Kong after implementing an AI curriculum Yau el al (2023) identified both technical and non-technical elements the teachers found important when integrating AI. These elements included providing students with awareness of AI applications in their daily life, teaching students about the underlying technologies of AI, promoting students interest in AI and AI careers, advocated for an awareness of AI ethics, and building students AI knowledge systematically across subject areas and grades. Given the centralized nature of both educational systems (UAE and Hong Kong), the studies point to the need for coordinated efforts and support for teachers to both accept and effectively use AI in their teaching in the US.
Ng et al. (2023) showed how AI competencies might fit into broader digital competencies. Possessing AI competencies allows individuals to critically assess AI technologies, interact and cooperate seamlessly with AI, and utilize AI tools online, at home, and professionally. Building on different models of AI competencies, the authors present two AI digital competency models for teachers, DigCompEdu and P21’s framework. Both frameworks make program guidance for professional engagement, digital resources, teaching and learning, assessment, empowering learners, subject area learning, and learning and innovation skills, and subject area learning.
TPACK is a popular framework used to train teachers to integrate technology into subject areas. Through a study of 700 teachers in Turkey, Celik (2023) provides a scale to measure AI TPACK that also integrates ethical components to measure instructional AI knowledge. The study indicates that a deeper understanding of AI tools enhances comprehension of their educational value. Additionally, grasping how AI transforms pedagogy was linked to increased adoption of AI systems in instruction. Conversely, insufficient technical knowledge can result in teachers placing excessive trust in AI. The more educators identify ethical concerns in AI, like fairness, the better informed they become about incorporating AI-driven tools in education.
Studies Referenced
- Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. In Computers and Education: Artificial Intelligence (Vol. 4, p. 100132). Elsevier BV. https://doi.org/10.1016/j.caeai.2023.100132
Link to PDF: https://journals-ohiolink-edu.proxy.lib.ohio-state.edu/apexprod/f?p=1507:200::::200:P200_ARTICLEID:389993189
- Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. In Computers in Human Behavior (Vol. 138, p. 107468). Elsevier BV. https://doi.org/10.1016/j.chb.2022.107468
- Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational technology research and development, 71(1), 137-161. https://link.springer.com/article/10.1007/s11423-023-10203-6
Link to PDF: https://proxy.lib.ohio-state.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edselc&AN=edselc.2-52.0-85148524893&site=eds-live&scope=site
- Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial intelligence (AI) literacy in early childhood education: The challenges and opportunities. Computers and Education: Artificial Intelligence, 4, 100124. https://doi.org/10.1016/j.caeai.2023.100124
Link to PDF: https://proxy.lib.ohio-state.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edselp&AN=S2666920X23000036&site=eds-live&scope=site
- Yau, K. W., Chai, C. S., Chiu, T. K., Meng, H., King, I., & Yam, Y. (2023). A phenomenographic approach on teacher conceptions of teaching Artificial Intelligence (AI) in K-12 schools. Education and Information Technologies, 28(1), 1041-1064. https://doi.org/10.1007/s10639-022-11161-x
Other Articles Citied
Burgsteiner, H., Kandlhofer, M., & Steinbauer, G. (2016, March). Irobot: Teaching the basics of artificial intelligence in high schools. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1), 4126–4127.