The combination of Artificial Intelligence (AI) and Formative Assessment (FA) in Teacher Education explores how emerging technologies can enhance teaching practices and professional development. AI tools can provide personalized feedback, identify learning needs, and support reflective practice among educators. Integrating AI-driven formative assessment methods allows for continuous evaluation of teaching competencies, promoting adaptive learning, data-informed decision-making, and improved instructional quality in teacher education programs. The purpose of this study was to conduct a systematic review of the use of Formative Assessment (FA) and Artificial Intelligence (AI) in Teacher Education (TE) during the period 2020–2025 (inclusive). The review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, which ensures a rigorous, transparent, and reproducible process in the selection and analysis of studies. To this end, scientific articles published in the Scopus, Web of Science and Dialnet databases were reviewed, considering publications in English and Spanish. The objective was to identify trends, methodological approaches, results, and research gaps that show how AI is being integrated, or not, into FA processes in TE. The review also sought to analyze the impact of AI on student participation in assessment, feedback, decision-making, and the learning and assessment process itself, synthesizing the current evidence on the relationship between AI and FA in TE.
We can consider AI as a set of technologies that is changing the way we access, produce, and share knowledge. AI is already widely present in society, and its use is growing exponentially across all fields, including education
[1].
In higher education, the use of AI by students is widespread. According to the Chegg Global Student Survey
[2], 80% of university students report having used generative AI tools to support their studies; this figure rises to 92% according to the Higher Education Policy Institute + Kortext Student Generative AI Survey
[3], with 88% using it for tasks that involve assessment. With regard to university teaching staff, the Digital Education Council report
[4] indicates that 61% of faculties report having already used AI in their teaching, and 86% see themselves using it in the future. In this context, learning to coexist with AI in higher education is key. AI is prompting a rethinking of the teaching–learning process among university faculties, who are often overwhelmed by a lack of knowledge about a technology that is advancing at great speed.
A recent study
[5] analyzes the different uses of AI by university faculties, finding that preparation and instructional design (developing materials, generating ideas, etc.) is the most widespread use. However, other uses can also be identified, such as: (a) support for teaching and learning (virtual assistants, intelligent tutoring systems, or personalized learning systems); (b) analysis and monitoring of learning (analyzing learning data, detecting difficulties, adjusting instruction, etc.); (c) automatic generation of feedback and assessment (assessing tasks, providing faster and more detailed feedback, etc.); (d) reduction of administrative time (materials management, content preparation, internal planning processes); (e) AI training as instructional content (courses and workshops to develop AI literacy, spaces for dialogue on the ethics of AI use, etc.); and (f) promotion of educational equity (improving access to resources, offering personalized support, facilitating learning for people with diverse needs).
Furthermore, recent studies
[6][7][8][6,7,8] show that university students are using AI very actively to study, resolve doubts, complete assignments, seek explanations, summarize readings, and explore related topics—the main motivation for its use being time savings, though they do not always have a deep understanding of the implications involved.
The use of AI in Teacher Education (TE) is also becoming increasingly widespread; practically all students use different AI applications in the preparation of their academic work
[9], and in recent years, research on the use of AI in TE has grown considerably
[10][11][12][13][14][15][16][17][10,11,12,13,14,15,16,17]. Studies agree on the need for AI literacy in TE for both teachers and students. Given the barrier posed by AI literacy and its responsible integration, it is essential that TE faculties have the knowledge and strategies necessary to navigate an ever-evolving technological landscape
[14].
Among the competencies and knowledge that TE students must acquire and know how to apply, assessment methods stand out. The use of formative assessment (FA) systems in TE appears to have several advantages, based on evidence collected in numerous studies
[18][19][20][21][22][18,19,20,21,22]. The FA model seeks to generate processes for improvement and learning in three directions: (a) improving students’ learning processes and the quality of their outputs; (b) improving and refining teachers’ instructional competencies; and (c) redirecting and improving the teaching–learning activities carried out in the classroom, both during the course itself and after its completion, with a view to the following academic year
[22].
Moreover, several studies seem to indicate that there is considerable transfer between experiences in TE and professional practice as teachers, given that future teachers in training tend to reproduce in their teaching practice the same methods they experienced as students
[19][23][24][25][19,23,24,25]. However, some recent works provide evidence that there does appear to be transfer between the use of FA systems during TE and the subsequent application of this type of assessment in their practice as teachers
[19][23][26][27][19,23,26,27].
The emergence and widespread use of AI in TE makes it necessary to rethink many of the learning and assessment activities that have been used in recent years. The use of AI linked to formative assessment (FA) processes generates new learning opportunities
[9] that need to be investigated in a systematic way. Unlike broader reviews on AI in education, this study specifically focuses on AI applied to FA in TE and synthesizes the literature by identifying five key pedagogical uses of AI within FA processes, thereby providing a structured and novel analytical framework.
As the guiding question of this research, we asked: Is AI being used in FA processes in TE? Therefore, the objective was to identify trends, methodological approaches, results, and research gaps that show how AI is being integrated—or not—into FA processes within TE.
Accordingly, this systematic review aimed to answer the following research question: How is AI being used in formative assessment processes in Teacher Education?
