Exploring pre-service teachers’ generative AI readiness and behavioral intentions: A pilot study

Phuong Bui; Tiina Korhonen; Sini Kontkanen; Sorella Karme; Satu  Piispa-Hakala; Marjaana Veermans

doi:10.31129/LUMAT.13.1.2755

Authors

Phuong Bui Karlstad University, Sweden https://orcid.org/0000-0001-9278-0515
Tiina Korhonen University of Helsinki, Finland https://orcid.org/0000-0003-2875-4915
Sini Kontkanen University of Eastern Finland, Finland https://orcid.org/0000-0002-3026-4037
Sorella Karme University of Helsinki, Finland https://orcid.org/0000-0002-0611-9059
Satu Piispa-Hakala University of Eastern Finland, Finland https://orcid.org/0000-0002-1852-3729
Marjaana Veermans University of Turku, Finland

DOI:

https://doi.org/10.31129/LUMAT.13.1.2755

Keywords:

generative AI, teacher education, preservice teacher, technological adoption

Abstract

Generative Artificial Intelligence (GenAI) has rapidly emerged as a field capable of creating unique content across various areas. While offering significant potential, it presents challenges including ethical concerns, content inaccuracies, and increased challenges for educators who must adapt to fast-evolving technologies. Integrating GenAI tools into teacher education represents an urgent global research priority. This pilot study explores GenAI readiness, experiences, perceptions, and behavioral intentions among Finnish pre-service teachers while examining the feasibility of the GenAI Readiness Scale as a measurement instrument. Using a mixed-methods approach combining quantitative survey data (N=77) with qualitative responses (n=56) from open-ended questions, the research provides a nuanced analysis of future educators’ positioning toward GenAI integration in educational settings. Findings reveal a significant adoption gap, with 27% of participants never used GenAI tools as of April-June 2024, while majority engaged sporadically. Despite low perceived accuracy, frequent users continued utilizing GenAI, suggesting that usability, efficiency, and creative support outweigh accuracy concerns. Ideation and content creation emerged as the most common GenAI-supported tasks, while self-regulated and adaptive learning remained underutilized, indicating limited awareness of GenAI’s broader potential. Challenges primarily involved output quality and prompting difficulties. Participants preferred modifying AI outputs rather than refining prompts, employing strategies like output modification and external verification, though critical evaluation wasn’t always explicit. These findings highlight the need for structured AI literacy training in teacher education, emphasizing prompt engineering, evaluative judgment, and strategic AI integration. This study underscores the importance of developing GenAI competencies among pre-service teachers to ensure effective, responsible, and pedagogically meaningful AI adoption. Future research should explore longitudinal adoption trends, and impact of AI literacy training on teaching and learning practices.

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