Cognitive Processes and Mental Models in Mathematics Teaching Using Artificial Intelligence

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23420%2F25%3A43976557" target="_blank" >RIV/49777513:23420/25:43976557 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.el-journal.org/index.php/journal/article/view/78/29" target="_blank" >https://www.el-journal.org/index.php/journal/article/view/78/29</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.57125/ELIJ.2025.09.25.01" target="_blank" >10.57125/ELIJ.2025.09.25.01</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cognitive Processes and Mental Models in Mathematics Teaching Using Artificial Intelligence

Popis výsledku v původním jazyce

In the context of increasing digitisation of education, this study examines the level of digital competence of recent high school graduates in the use of generative language models and equation editing tools in mathematics teaching. The research aimed to verify how effectively students can work with artificial intelligence tools in creating and solving word problems, primarily through systems of linear equations, and to identify the mental models they apply in doing so. The research was conducted on a sample of 25 first-year students from the Faculty of Education. The methodology employed a combination of quantitative and qualitative approaches, with the key analytical unit being the so-called semantic-logical structure (S-L structure), which enabled the monitoring of the transformation of scientific content (A), the teaching task (B), and the student&apos;s mental schema (C). The results showed that students&apos; digital skills are predominantly at the basic level – only 28% of participants were able to adjust the prompt and use AI effectively and critically. Most were satisfied with the output generated without more profound reflection. Using the equation editor was a completely new experience for more than half of them. The data also shows that while students can handle basic technical tasks, they often lack knowledge of more advanced tools, confidence, and tend to be satisfied with the first result without conducting a thorough check. On the other hand, it was confirmed that students who reflected on their mistakes and actively adjusted prompts were able to gradually improve their outcomes, which suggests that these skills can be developed in a targeted manner. The study highlights the need for the systematic development of digital literacy, problem-solving skills, and reflective thinking in future teachers. The S-L structure has proven to be an effective tool for monitoring cognitive processes in digitally oriented teaching and learning. Practical strategies include prompt engineering workshops, guided reflection, and subject-specific AI training to improve learning outcomes and e-learning quality.

Název v anglickém jazyce

Cognitive Processes and Mental Models in Mathematics Teaching Using Artificial Intelligence

Popis výsledku anglicky

In the context of increasing digitisation of education, this study examines the level of digital competence of recent high school graduates in the use of generative language models and equation editing tools in mathematics teaching. The research aimed to verify how effectively students can work with artificial intelligence tools in creating and solving word problems, primarily through systems of linear equations, and to identify the mental models they apply in doing so. The research was conducted on a sample of 25 first-year students from the Faculty of Education. The methodology employed a combination of quantitative and qualitative approaches, with the key analytical unit being the so-called semantic-logical structure (S-L structure), which enabled the monitoring of the transformation of scientific content (A), the teaching task (B), and the student&apos;s mental schema (C). The results showed that students&apos; digital skills are predominantly at the basic level – only 28% of participants were able to adjust the prompt and use AI effectively and critically. Most were satisfied with the output generated without more profound reflection. Using the equation editor was a completely new experience for more than half of them. The data also shows that while students can handle basic technical tasks, they often lack knowledge of more advanced tools, confidence, and tend to be satisfied with the first result without conducting a thorough check. On the other hand, it was confirmed that students who reflected on their mistakes and actively adjusted prompts were able to gradually improve their outcomes, which suggests that these skills can be developed in a targeted manner. The study highlights the need for the systematic development of digital literacy, problem-solving skills, and reflective thinking in future teachers. The S-L structure has proven to be an effective tool for monitoring cognitive processes in digitally oriented teaching and learning. Practical strategies include prompt engineering workshops, guided reflection, and subject-specific AI training to improve learning outcomes and e-learning quality.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

50301 - Education, general; including training, pedagogy, didactics [and education systems]

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

E-Learning Innovations Journal

ISSN

2957-2207

e-ISSN

2957-2207

Svazek periodika

3

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

27

Strana od-do

4-30

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—