The rapid development and widespread use of artificial intelligence (AI) in recent years have had a profound impact on education, including higher education, affecting both teaching and learning (Rawas, 2024; Sova et al., 2024). The majority of university students use AI-based tools in their studies, and nearly one quarter report using them on a daily basis (Digital Education…, 2024). Artificial intelligence is thus becoming an integral part of higher education. While AI offers new opportunities for enhancing learning, it also raises significant challenges, including concerns related to academic integrity (Ahma & Kadriu, 2025), knowledge construction, and the evolving role of the teacher (Zawacki-Richter et al., 2019).

The Digital Education Action Plan 2021–2027 (European Commission, 2020) emphasizes the importance of the responsible use of AI and the development of teachers’ digital competences. Student teachers play a key role here. Their perceptions and attitudes about AI influence how they use it in their own learning, and later, working in the field of education, they serve as role models whose practices and beliefs shape both the ways AI is used and the attitudes toward AI adopted by their students. For this reason, particular attention should be paid to student teachers as a target group. Previous research has shown that teachers’ beliefs and attitudes toward technology significantly influence its pedagogical integration (Ertmer & Ottenbreit-Leftwich, 2010; Tondeur et al., 2016).

It is therefore essential to understand how student teachers perceive AI: whether they view it primarily as a learning-support tool, a substitute for the teacher, or a factor that may have negative effects on learning, and also how these perceptions are related to their use of AI. Artificial intelligence should not be understood merely as a technical tool; its use requires critical thinking and ethical awareness (European Commission, 2019). Therefore, teacher education need to foster student teachers’ knowledge, skills, and also attitudes related to the responsible and meaningful use of AI.

Although perceptions of AI have been studied in previous research, less is known about how student teachers understand and make sense of AI in the context of their studies. Findings regarding AI-related perceptions have been mixed. For example, some studies have distinguished between positive and negative attitudes toward AI (Schepman & Rodway, 2023), while others have identified acceptance- and fear-related factors (Sindermann et al., 2021). More recently, Nazaretsky et al. (2025) proposed a four-component structure of AI perceptions, including perceived benefits and obstacles, AI readiness, and trust.

This study is theoretically based in the Technology Acceptance Model (TAM) and its extensions (e.g., UTAUT), which emphasize the role of perceived usefulness, ease of use, attitudes in predicting individuals’ intentions to use technology and their actual usage behaviour (Davis, 1989; Venkatesh et al., 2003).

The aim of this study is to provide an overview of first-year student teachers’ perceptions of artificial intelligence, their reported use of AI for learning, and the relationships between the perceptions, AI use and learning outcomes. The study has four research questions:

1. What are student teachers’ perceptions of artificial intelligence and its use in learning?
2. How frequently do student teachers use artificial intelligence for learning purposes?
3. How do student teachers’ perceptions of AI predict the reported frequency of AI use in learning?
4. What is the relationship between AI perceptions, reported frequency of AI use, and learning outcomes?

The study involved 201 first-year student teachers enrolled in a compulsory Developmental Psychology course at a university in Estonia. Participants represented five teacher education curricula, ranging from early childhood to the lower secondary education. The sample included early childhood education student teachers (n = 81), primary school student teachers (n = 44), and lower secondary school student teachers for multiple subjects across three curricula (n = 92). Consistent with the gender distribution in the teaching profession in Estonia—where men constitute approximately 15% of teachers in general education and only 1.4% in early childhood education—the sample included a relatively small number of male participants (n = 17). Regarding teaching experience, 75% of the respondents had no prior teaching experience, 21% were currently working as teachers, and 4% had worked as teachers in the past. Data were collected using a self-report questionnaire at the end of the course. Participation in the study was voluntary, and informed consent was obtained from participants prior to completing the questionnaire. Students were informed about the purpose of the study and that their responses would be treated confidentially and used for research purposes in an aggregated form. Participation or non-participation in the study had no impact on students’ assessment in the course or final grade. Students’ perceptions of artificial intelligence were measured using a 15-item AI perceptions scale, which included statements reflecting both positive and negative aspects of AI use. The items addressed general perceptions of AI as well as perceptions specifically related to learning. Examples of the items: AI helps me save time, AI provides me with step-by-step explanations, Using AI does not support the development of my own thinking. Responses were recorded on a 5-point Likert-type scale. To assess the frequency of AI use for learning, participants were asked how often they had used artificial intelligence during the Developmental Psychology course. Response options ranged from no use at all, through occasional use in individual learning activities, to regular use across many learning activities. Learning outcomes were operationalised using the final course grade, awarded on a six-point grading scale. Data analysis was conducted using quantitative statistical methods. Confirmatory factor analysis (CFA) was used to validate the factor structure of the AI perceptions scale. Correlation analysis and multiple regression analysis were used to investigate the relationships among student teachers’ AI perceptions, the reported frequency of AI use for learning, and learning outcomes.

The following section provides a brief overview of the preliminary findings and further plans of the study. Regarding the frequency of AI use, 20% of respondents reported not using AI during the course, while 52% reported using AI in individual learning activities, and 27% reported occasional use across different tasks. According to self-reports, AI was most frequently used to gain clearer explanations of concepts or topics, generate ideas, and support deeper understanding when the topic was difficult to understand. Confirmatory factor analysis supported a four-factor structure of AI perceptions, including general attitudes toward AI, perceived usefulness for learning, general obstacles, and learning-related obstacles. This multidimensional structure suggests that student teachers’ perceptions of AI are more nuanced than simple positive–negative dichotomies. The expected outcomes focus on examining how student teachers’ AI perceptions predict the reported frequency of AI use, as well as how AI use frequency and the range of learning activities supported by AI correlate with learning outcomes. It is anticipated that perceived usefulness will positively predict AI use, whereas perceived learning-related obstacles may limit both frequency and diversity of use. Overall, the study contributes to teacher education by helping to understand how future teachers understand the benefits and limitations of AI and how these perceptions are related to their own use of AI for learning. By framing AI as an integrated component of teacher competence rather than a merely technical issue, the findings support a broader rethinking of the teacher’s role in the age of artificial intelligence. The Estonian context offers a valuable reference point for European and international discussions on AI integration in teacher education.

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