Artificial Intelligence in Mathematics Education: A Systematic Review

Inteligência artificial na educação matemática: Uma revisão sistemática

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

(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.

Keywords: AI; artificial intelligence; intelligent tutoring systems; mathematics; mathematics education; systematic review

Database	Search items
Scopus	TITLE-ABS-KEY (“artificial intelligence” OR “AI”) AND TITLE-ABS-KEY (education) AND TITLE-ABS-KEY (math*)
Wos	ALL= ((“AI” OR “artificial intelligence”) AND (“education”) AND (math*))
SciELO	(“AI” OR “artificial intelligence”) AND (“education”) AND (math*)

Criterios de inclusión	Criterios de exclusión
I1: Studies at all school levels of mathematics education	E1: I study in a discipline other than mathematics
I2: Studies focused on artificial intelligence in future teachers, students of school education or tertiary education related to mathematics.	E2.1: Studies that do not focus on incorporating artificial intelligence into mathematics education
	E2.2: Studies that mention intelligence, but don't focus on it
I3: Studies published in English or Spanish	E3: Studies not published in English or Spanish
I4: Articles	E4: Conference proceedings, books, press articles and book chapters
I5: Articles in the final stage of publication	E5: Articles in the press
I6: Studies indexed in the Wos, Scopus and SciELO databases	E6: Studies not indexed in any of the databases included in the study
I7: Studies conducted between 2019-2023	E7: Studies conducted before 2019

Countries	Frequency	Percentage
Germany	13	12.5
Canada	3	2.9
China	17	16.3
Colombia	2	1.9
South Korea	2	1.9
United Arab Emirates	2	1.9
Fiji	2	1.9
Indonesia	3	2.9
Italy	5	4.8
Jordan	1	1
Kazakhstan	7	6.7
Norway	3	2.9
Oman	1	1
Portugal	4	3.8
United Kingdom	1	1
Russia	1	1
Sweden	4	3.8
Taiwan	2	1.9
United States	31	29.8
Total	104	100

Sample size	Frequency	Percentage
0-50	7	24.1
51-100	2	6.9
101-200	7	24.1
201-500	4	13.8
>500)	3	10.3
Not mentioned	1	3.4
Not Applicable	5	17.2
Total	29	100

Concepts	Frequency	Percentage
Automatic learning	6	20.7
Chatbot	2	6.9
Robotics	2	6.9
Intelligent tutoring systems	14	48.3
Not mentioned	1	3.4
Not Applicable	1	3.4
Another	3	10.3

Authors/year	Diploma	Some results
Kong et al. (2023)	Evaluating an artificial intelligence literacy programme for empowering and developing concepts, literacy and ethical awareness in senior secondary students	Knowledge of programming provide advantages for the deep learning course but not for other courses, such as AI application projects. In addition, some ethical principles may be too complex for upper secondary school students to understand.
Huang y Qiao (2022)	Enhancing Computational Thinking Skills Through Artificial Intelligence Education at a STEAM High School	AI education with STEM is beneficial in promoting students' creativity, cooperation, critical thinking, and problem-solving in computational thinking skills. It also improves the learning motivation and self-efficacy of the students in the experimental group.
Lee y Yeo (2022)	Developing an AI-based chatbot for practicing responsive teaching in mathematics	They developed a chatbot with knowledge of concepts and operations between fractions. They show that this chat reasonably and adequately covered the questions of future teachers and provided answers that seemed realistic.
Zhai et al. (2022)	Applying machine learning to automatically assess scientific models	Using assessments incorporating drawn and textual models, they achieved excellent scoring accuracy through machine learning. They also identified five characteristics of the drawn models that can significantly affect the accuracy of the machine score.
Bekmanova et al. (2021)	Personalized training model for organizing blended and lifelong distance learning courses and its effectiveness in Higher Education	The results of a distance learning course, based on personalized learning, indicate that the course meets expectations and is innovative. In addition, they found that 100% of students were successfully certified compared to a traditional classroom course.
Shin (2022)	Teaching Mathematics Integrating Intelligent Tutoring Systems: Investigating Prospective Teachers’ Concerns and TPACK	Future teachers recognize that they have solid pedagogical knowledge (PK) and pedagogical content knowledge (PCK) they need to teach mathematics. However, when PK and PCK were integrated with technological knowledge, they were less likely to recognize that they had sound knowledge for effective teaching with technology.
Azevedo et al. (2022)	Mathematics learning and assessment using MathE platform: A case study	When asking students about the extent to which the MathE platform is a valuable aid to their studies, they found that 40.6% of students considered it useful, while 9.4% said it was not useful, and 9.4% thought the platform was helpful. The main difficulties experienced with the platform were organization and language.
Moltudal et al. (2022)	Adaptive Learning Technology in Primary Education: Implications for Professional Teacher Knowledge and Classroom Management	Teachers describe the technology as promising but feel that, to use it fully, their students must spend more time solving tasks in the program than teachers are willing to allocate.
Zhou (2023)	Integration of modern technologies in higher education on the example of artificial intelligence use	There is a significant difference in student performance in five subjects before and after the introduction of the Raptivity personalized learning platform.
Wang et al. (2023)	When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction	Students using Aquirrel AI learning independently outperformed students enrolled in a course taught by expert teachers. They also outperformed students who received both whole-class and small-group instruction.
Sperling et al. (2022)	Still w(AI)ting for the automation of teaching: An exploration of machine learning in Swedish primary education using Actor-Network Theory	The study shows that AI technologies designed to personalize and automate require mutual adaptation of human and non-human actors in the network.
Wang et al. (2022)	Development and Application of an Intelligent Assessment System for Mathematics Learning Strategy among High School StudentsTake Jianzha County as an Example	They found that assessment and implementation systems are effective in providing teachers with techniques to help assess and improve mathematics learning strategies.
Shin et al. (2021)	Analyzing students’ performance in computerized formative assessments to optimize teachers’ test administration decisions using deep learning frameworks	The model created helps predict whether the next test will be significant based on the performance scores of two previous high-pressure tests.
Ferro et al. (2021)	Gea2: A Serious Game for Technology-Enhanced Learning in STEM	They found that the effectiveness of the game, as a learning tool, did not yield good overall results. The game was expected to improve understanding of topics explained in class but ended up being a replacement for face-to-face lectures.
Hsu et al. (2021)	Is it possible for young students to learn the Ai-STEAM application with experiential learning?	They showed that the use of experiential learning integrated into an AI-STEM course improves learning effectiveness.
Robles y Quintero (2020)	Intelligent system for interactive teaching through videogames	For each video game it is shown that the implementation of the intelligent system, with two computational techniques implemented, enables the user to obtain a better performance in the subjects addressed.
Yannier et al. (2020)	Active Learning is About More Than Hands-On: A Mixed-Reality AI System to Support STEM Education	AI agent-guided inquiry helped students formulate better and more scientific theories of the phenomena they experience. Additionally, children who receive guidance during inquiry can learn to apply science in engineering tasks better.
Büscher (2020)	Scaling up qualitative mathematics education research through artificial intelligence methods	They report that the model performed with 76% accuracy on the test set, meaning that it labeled 76% of the data in the same way as a research team would have done.
Cung et al. (2019)	Getting Academically Underprepared Students Ready through College Developmental Education: Does the Course Delivery Format Matter?	When using a well-developed intelligent tutoring system, the learning gains are even more significant when combined with in-person lectures.
So y Lee (2023)	Pedagogical exploration and technological development of a humanoid robotic system for teaching to and learning in young children	They found that the NAO Robot can build a positive and friendly relationship with children while achieving math learning outcomes.
Denes (2023)	A case study of using AI for General Certificate of Secondary Education (GCSE)grade prediction in a selective independent school in England	They showed that predictions are more accurate for STEM subjects and those with more students. In addition, they found that STEM and non-STEM teachers have different perceptions when awarding grades.
Soesanto et al. (2022)	Indonesian students’ perceptions towards AI-based learning in mathematics	Some of the students’ perceptions are the following: they see AI robots as intelligent machines that can detect something; they understand AI as a robot created to do something; and AI is seen as a simulation of intelligence modeled on a machine.
Yang et al. (2021)	Can Crowds Customize Instructional Materials with Minimal Expert Guidance?Exploring Teacher-guided Crowdsourcing for Improving Hints in an AI-based Tutor	In one of the studies conducted (2), they report that teachers perceive self-written suggestions as better and more satisfactory than existing AI tutor suggestions. However, they did not perceive personalized, crowd-produced suggestions as an improvement on the original suggestions.
Walkington y Bernacki (2019)	Personalizing Algebra to Students’ Individual Interests in an Intelligent Tutoring System: Moderators of Impact	Deeper personalization tended to result in lower efficiency, but more positive affective states, whereas situational interest and play were unaffected. Additionally, students who were more deeply engaged with their interests performed better on measures of efficiency.
Wardat et al. (2023)	ChatGPT: A revolutionary tool for teaching and learning mathematics	They found that ChatGPT is a useful tool, but caution is needed when using it and guidelines for its safe use should be developed.

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Universidad Nacional, Costa Rica Heredia, Costa Rica, Heredia, Costa Rica, Heredia, Costa Rica, CR, 86-3000, 88942130, 25626029 - E-mail: revistauniciencia@una.cr

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[TFN1] Note: Own research source.