This session will present our discoveries about factors that motivate teachers to use AI. We will discuss how these factors can be measured and how the data can be used to provide opportunities of support for in-service and pre-service educators that impacts their motivation to use AI within their practice.

Artificial intelligence (AI) has become a prominent topic within the field of education fueling a wave of hype and expectations around its potential to transform the profession (Al-Zahrani, 2024). However, misconceptions about AI abound leading to confusion and hesitation among educators to use the technology (Lindner & Burges, 2020; Antonenko & Abramowitz). Teachers are unsure about what AI actually is and what it can realistically do. Additionally, they are unclear about when and how they can incorporate it into their curriculum and how to ethically do so (Jowallah, 2024; Jowallah & Bennett; 2025). A gap exists between the perceived potential of AI and teachers’ technical knowledge about AI technologies that contributes to a wide spectrum of responses among educators that go from enthusiastic adoption to seemingly absolute avoidance (Lindner & Burges, 2020). Educators often feel ill-prepared to experiment with AI-driven tools. Therefore, they keep the technology at arm’s length. Fearful of what they perceive as a complex, unknown, or even threatening technology, they move away from integrating it into their professional practice (Nazeretsky et al., 2022). Pervasive in the media and profession, AI is sometimes framed as a tool that may potentially replace teachers adding to the fear (Alwaqdani, 2024). At the same time, there are many teachers who are embrace AI and have found ways to effectively integrate it into their classrooms (Uzumcu, 2024). This raises key questions:

What are the differentiators between teachers who are hesitant to use AI use and those who are motivated to adopt it?

What factors support teachers who are motivated to explore and use AI?

Our initial exploration sought to answer the above questions and uncover the factors that support motivation in teachers to embrace AI technologies as a part of their professional teaching practice. Five key factors emerged as key indicators that support the motivation of K-12 teachers to use AI.

The theoretical foundation for this exploration was grounded in motivational theory as described by Omrod (2006). A key component of which is understanding the psychological processes that initiate and sustain goal-directed behavior (Omrod, 2006; Wiles et al., 2023; Bennett et al., 2024). In the context of education, motivation shapes how teachers engage with new technologies. Smart and Linder (2018) applied motivational theory to teacher education, and found connections to the alignment of professional goals and experiences with training opportunities. By applying motivational theory to the study of AI adoption, we can better understand what drives some teachers to explore AI while others hesitate.

This session will discuss the findings of our exploration and the emergence of the five key factors. Additionally, we will provide insight into possible support mechanisms for both in-service and pre-service teachers that can impact their motivation to use AI. We will discuss ways in which these factors can be measured and the how the data can be used to inform the development of teachers. Interactive polling and discussion about application to the context of everyone in attendance will be used to engage the audience and solicit feedback for further exploration.

References

Alwaqdani, M. (2024). Investigating teachers’ perceptions of artificial intelligence tools in education: potential and difficulties. Education and Information Technologies, 1-19.

Al-Zahrani, A. M. (2024). Unveiling the shadows: Beyond the hype of AI in education. Heliyon, 10(9).
Antonenko, P., & Abramowitz, B. (2023). In-service teachers’(mis) conceptions of artificial intelligence in K-12 science education. Journal of Research on Technology in Education, 55(1), 64-78.

Bennett, L., Smart, J., Wiles, D., Morrison, A., Zhang, Z., Bowman, A., ... & Doan, B. (2024). The Development and Validation of the Pre-Service Teacher Online Teaching Motivation Scale (PST-OTMS). Online Learning, 28(3).

Jowallah, R. J. (2024). Integrating Artificial Intelligence (AI) Into the Curriculum: Empowering the Next Generation Through Proactive AI Education. In Generative AI in teaching and learning (pp. 355-368). IGI Global.

Jowallah, R. & Bennett, L. (in press). Bloom’s Taxonomy: A proposed application to instructional design. The SAGE handbook of higher education instructional design. In S. Wa-Mbaleka, B. Chen, Petre, G.-E., deNoyelles, A. SAGE.

Lindner, A., & Berges, M. (2020, October). Can you explain AI to me? Teachers’ pre-concepts about Artificial Intelligence. In 2020 IEEE Frontiers in education conference (FIE) (pp. 1-9). IEEE.

Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers' trust in AI‐powered educational technology and a professional development program to improve it. British journal of educational technology, 53(4), 914-931.

Uzumcu, O., & Acilmis, H. (2024). Do innovative teachers use AI-powered tools more interactively? A study in the context of diffusion of innovation theory. Technology, Knowledge and Learning, 29(2), 1109-1128.

Wiles, D., Morrison, A., Smart, J., Bennett, L., & Peters, S. (2023). The Online Teaching Motivation Scale (OTMS): Development and Validation of a Survey Instrument. Online Learning, 27(4), 6-25.