[Objective] This study aims to analyze the current state of research on artificial intelligence (AI) in mathematics education, its applications, and its role in teaching and learning processes. [Methodology] A systematic review of the literature was conducted in three stages: identification, selection, and inclusion of articles from three recognized databases, resulting in 29 articles. These articles were thoroughly analyzed to identify participants, instruments used, the country of the authors' affiliation, year of publication, type of research, methodological approach, and the role of AI in these studies. [Results] There is a noticeable increase in research related to AI in mathematics education, with most studies being empirical and quantitative. The most frequently used instruments are questionnaires and interviews, with half of the studies employing at least two data collection instruments. Additionally, most studies focused on intelligent learning systems to enhance learning and support teaching, particularly for online assessment. [Conclusions] The reviewed articles show no evidence of research at the early childhood education level and very little related to teacher training. Few studies demonstrate the use of theoretical frameworks or approaches from the Didactics of Mathematics.

La introducción de la modelación matemática en el currículo de enseñanza media chileno, hace ya una decena de años, ha relevado la necesidad de estudios relativos a las prácticas de su enseñanza en la formación inicial de profesores en el país. Este trabajo presenta los resultados de una investigación que utiliza el marco conocimiento especializado del profesor de matemáticas (MTSK, por sus siglas en inglés) para analizar los conocimientos y la reflexión sobre modelación puestos en juego por estudiantes en formación inicial durante un ciclo de 14 sesiones de 90 minutos cada una. Los resultados muestran un progreso en el conocimiento matemático y en el conocimiento pedagógico del contenido. El trabajo discute posibles maneras de establecer modelos de enseñanza de la modelación y un momento propicio para hacer uso de una propuesta sobre la materia.

School is a space where learning mathematics should be accompanied by the student’s preferences; however, its valuation in the classroom is not necessarily the same. From a quantitative approach, we ask from the mathematical thinking styles (MTS) theory about the correlations between preferences of certain MTS and mathematical performance. For this, a valid test instrument and a sample of 275 16-year-old Chilean students were used to gain insight into their preferences, beliefs and emotions when solving mathematical tasks and when learning mathematics. The results show, among other things, a clear positive correlation between mathematical performance and analytical thinking style, and also evidence the correlation between self-efficacy, analytical thinking and grades. It is concluded that students who prefer the analytical style are more advantageous in school, since the evaluation processes have a higher valuation of analytic mathematical thinking.

The objective of this research was to identify persistent errors of mathematical concepts and procedures, in particular, the incorrect application of the linearity concept, as well as implement learning situations to address errors of linearity by designing learning tasks in the context of modeling and the use of mathematical software. The study was applied to 42 students in the mathematics education program, during the 2019-2020 period. Research was conducted under a qualitative and longitudinal approach. Data collection began with a test upon entering the program and a second test after completing a semester of classes. Later on, collaborative workshops were implemented to address linearity errors, and a post-test was applied at the end. Upon entering the initial teacher math training, students showed a high percentage of errors, mistakenly applying linearity to roots, powers, logarithms, and trigonometry. After a semester of classes, these errors persisted. After the workshops, none of the participants made linearization errors in roots, powers and trigonometry, and only some continued applying linearity to logarithms. It was concluded that at the beginning of the program, students were still making errors in concepts and procedures that should have been overcome in school, and many of these errors are highly persistent. Using software in learning situations related to modeling would help students remedy those errors.

Currently, there is ambiguity regarding the volume of empirical research that delves into gender stereotypes linked to STEM in primary and secondary education. To shed light on the prevailing literature and pertinent challenges, we undertook a systematic review, adhering to the PRISMA guidelines. This examination encompassed empirical studies sourced from Scopus and Web of Science, culminating in the inclusion of twenty-four studies. The findings underscore the imperative to augment prior research that provides empirical insights into social and scientific narratives surrounding gender stereotypes. This review illuminates gender disparities within the educational system and the reduced female engagement in STEM. Concurrently, it advocates for formulating public policies and early-stage interventions to highlight how STEM disciplines can bridge these gender imbalances at the primary and secondary school stages. From our vantage point, this systematic review will catalyse forthcoming empirical inquiries. Whether stemming from real-world practices or geared towards enriching a specific theoretical framework, such research, especially involving students from Latin American nations, is of the essence. Notably, our search did not uncover any studies addressing the themes broached in this investigation about these regions. It is thus pressing to accelerate efforts in these territories to acknowledge the cultural and societal hurdles impinging upon STEM education.