Artificial intelligence in mathematics education: A systematic literature review — TEI-tagged view

Mohamed, Hidayat, Suhaizi, Sabri, Mahmud, Baharuddin · 2022 · education · doi:10.29333/iejme/12132 · full text at the source · TEI xml · tagged edition · index · concepts · authors
Where this record's text came from. faceted 2026-08 by reading the locally held text (pdftext); every evidence string verified as an exact substring of that text before merging. Claim type is 'existence' because the paper's contribution is an inventory of what the literature contains (which AI approaches, countries, methods, themes exist) rather than a causal account or a new technique; 'systematic-review' captures the method exactly as the paper names it. The Zhong and Xia relation is tagged 'replicates' in the weak sense the paper itself uses ('in line with' a prior systematic review's conclusion) - it is agreement of review findings, not a re-run of a study. The text is clean pdftext with only page-header intrusions (e.g. '10 / 11 Mohamed et al. ...') mid-sentence in a few places; quotes were chosen to avoid those. faceted 2026-08 by reading the locally held text (pdftext); every evidence string verified as an exact sub 2026-08-14 and 2026-08-15 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node.
This record’s own method is systematic-review, and the tags below follow from that. A review’s findings, quantities and statistics are things stated IN this paper, and some of them report results obtained elsewhere. No tag on this page names a different author. Measured across every review in this corpus, 81% of a review’s tags carry no citation at all — most of them are the review speaking. The corpus does not adjudicate the rest: read an unmarked tag as a claim this paper makes, not necessarily one it made first.
Other fields define these differently the same word, defined another way in another discipline
artificial intelligence 4 fields · systematic-review 4 fields · PRISMA 3 fields
Tags on this paper every tag the record carries, whether or not the words occur in the text below — a number is how many papers in the corpus carry that tag; a tag with no number is unique to this paper
artificial intelligence10 machine intelligence2 expert systems intelligent tutoring system computer program systematic-review50

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonartificial intelligence in mathematics education— “The goal of this review is to add to the conversation by offering a complete overview of AI in mathematics teaching and learning for students at all levels of education. A”
claim-typeexistence— “The findings of the SLR indicate that AI approach used in mathematics education for the samples studied were through robotics, systems, tools, teachable agent, autonomous agent, and a comprehensive approach.”
method — evidence— “A systematic literature review (SLR) was conducted using established and robust guidelines.”
mechanismthe high maintenance cost of AI equipment restricts who can run such studies, which is why the reviewed research clusters in developed countries and is absent from Southeast Asia— “Meanwhile, AI is also difficult to apply comprehensively due to relatively high maintenance costs. Therefore, it is believed that this was why many studies were only conducted in developed countries such as the United States, China, and Australia while zero studies in Southeast Asia, especially in Malaysia.”mechanisms[0]
builds-on Ouyang and Jiao (2021) conceptual framework of AI-directed / AI-supported / AI-empowered learning— “Therefore, in the current work, we follow the idea of Ouyang and Jiao (2021) as conceptual framework namely AI-directed (learner-as-recipient), AI-supported (learner-as-collaborator), and AI-empowered 9 learner-as-leader).”claim_relations[0]
builds-on Moher et al. (2009) PRISMA flow diagram— “Figure 2 displays the PRISMA flow chart in this study adapted and modified from Moher et al. (2009).”claim_relations[1]
replicates Zhong and Xia (2020) systematic review of robotics in mathematics learning— “The result was in line with Zhong and Xia (2020) who conducted a systematic review that stated that there is a potential for future research and the rapid development of evidence-based research on teaching and learning mathematical content knowledge through robotics.”claim_relations[2]
senseAI - THREE CHARACTERISATIONS AND NO RULING: the review reports that there are diverse concepts of AI in the literature and that defining it is a problem, then quotes a confluence of technologies, computers that do cognitive functions, and a process that produces human intelligence through machines - three sources, one review, and no statement of which the review is using— “To date, there are diverse concept of AI in the current literature. The problem in defining AI, known as machine intelligence (Poole et al., 1998), is to define artificiality’s parameters, or the manners in which computers differ from human intelligence”senses[0]
senseAI, OPERATIONALLY - A LIST OF EIGHT SEARCH STRINGS: what the review actually counts is papers matching artificial intelligence, AI, robotics, machine learning, neural networks, fuzzy logic, natural language processing or expert systems, so a table of search synonyms is doing the work that none of the three definitions does— “Synonyms and alternatives terms for main search terms Artificial intelligence AI Robotics Machine learning Neural networks Fuzzy logic Natural language processing Expert systems”senses[1]
sensemachine intelligence - THE ALTERNATIVE NAME, GIVEN IN PASSING: the review introduces the problem of defining AI as the problem of defining artificiality's parameters, and names the field 'machine intelligence' in the same clause, attributing it to Poole et al. 1998— “The problem in defining AI, known as machine intelligence (Poole et al., 1998), is to define artificiality’s parameters, or the manners in which computers differ from human intelligence”senses[2]
controlA PROCEDURAL SAFEGUARD ON A SYSTEMATIC REVIEW: every one of the 845 articles read through title, abstract, methodology, results and discussion against the stated selection criteria, rather than screened on title and abstract alone — held — rules out articles that match the search terms but not the research question - a review screened on titles inherits whatever the titles happen to say, and the synthesis then describes the search rather than the literature— “to ensure that all 845 articles fit the study’s selection criteria and objectives, each article’s title, abstract, methodology, results, and discussion were thoroughly reviewed”controls[0]
edgeintelligent tutoring system —is-a→ computer program— “An intelligent tutoring system (Hasanein & Abu-Naser, 2018) is a computer program designed to simulate the behavior and guidance of a human teacher”concept_edges[0]
outcome (stated by the tagger, no quotation)control — heldcontrols[0]
for which uselimitation — any count reported in this review: the authors state there are diverse concepts of AI in the literature and describe defining AI as a problem, without settling on one definition for the review— “To date, there are diverse concept of AI in the current literature. The problem in defining AI, known as machine intelligence (Poole et al., 1998), is to define artificiality’s parameters, or the manners in which computers differ from human intelligence”limitations[4]
for which uselimitation — the scope of what this review counts as AI: the search treated artificial intelligence, AI, robotics, machine learning, neural networks, fuzzy logic, natural language processing and expert systems as synonyms and alternative terms for one main search term— “Synonyms and alternatives terms for main search terms Artificial intelligence AI Robotics Machine learning Neural networks Fuzzy logic Natural language processing Expert systems”limitations[5]
for which uselimitation — the PRISMA flow reported here: 864 results from the databases plus 20 from other sources are stated to give a total of 885, and the printed numbers do not sum— “864 results were identified using search strategies, while additional papers (n=20) were identified from other sources. As a result, a total of 885 journal articles had been classified at this stage”limitations[6]
for which uselimitation — any claim about how much AI research exists in mathematics education: only English-language journal articles published between 2017 and 2021 were considered— “we were only concentrating on English-language journal articles made it less likely that complex or uncertain translations would be required. Then, we looked at articles published within the previous five years (between 2017 and 2021)”limitations[7]
replicationuntested
evidence status notephenomenon — This quote ends with the stray word 'A', the first word of the next sentence. It is not trimmed, because the quote without it occurs more than once in the paper's text and could not be tied to one place. The claim is carried by the sentence before 'A'.
limitationOnly a small number of eligible articles exist, so broad conclusions cannot be drawn— “The first limitation is that only a limited number of articles are available for research.”limitations[0]
limitationAI is rarely investigated in mathematics education, limiting the results obtained— “Because AI is a topic that is rarely investigated in mathematics education, the results gained are limited, making it difficult to draw broad conclusions from the findings.”limitations[1]
limitationDe-duplication across search engines reduced the number of relevant studies acquired— “As previously stated, AI topics have a limited number of studies; therefore, eliminating duplicate articles amongst search engines decreases the acquisition of relevant research studies.”limitations[2]
limitationOnly journal articles were included; proceedings and books were excluded— “Since the studies included in the analysis focused exclusively on journal articles, future research should consider using conference proceedings and books to enhance the findings and analysis.”limitations[3]
limitationThe review's central term is stated to have no agreed definition, and the review adopts none.— “To date, there are diverse concept of AI in the current literature. The problem in defining AI, known as machine intelligence (Poole et al., 1998), is to define artificiality’s parameters, or the manners in which computers differ from human intelligence”limitations[4]
limitationThe population is defined by a list of search synonyms rather than by a concept.— “Synonyms and alternatives terms for main search terms Artificial intelligence AI Robotics Machine learning Neural networks Fuzzy logic Natural language processing…limitations[5]
limitationThe three article counts printed for the search stage are inconsistent with each other.limitations[6]
limitationThe corpus is restricted by language and by a five-year window.limitations[7]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingRobotics was the most common AI approach in the 20 reviewed mathematics-education studies, accounting for 45% (n=9) of them.
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content_tags.findings[0]
findingThe USA contributed the most reviewed studies (n=3).
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content_tags.findings[1]
findingQuantitative designs were the most used methodology among the reviewed studies, at 40% (n=8).
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content_tags.findings[2]
finding2021 was the peak publication year for AI-in-mathematics-education articles in the review window, with 30% of the corpus.
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content_tags.findings[3]
findingEffectiveness of AI was the most studied theme, in 60% (n=12) of the reviewed papers.
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content_tags.findings[4]
finding834 articles were rejected for not fully explaining AI in mathematics education or not clearly reporting findings.
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content_tags.findings[5]
quantity45%, n=9 — share of reviewed studies using a robotics approach to AI
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content_tags.quantities[0]
quantity40%, n=8 — share of reviewed studies using quantitative methods
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content_tags.quantities[1]
quantity60%, n=12 — share of reviewed studies whose theme was effectiveness of AI
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content_tags.quantities[2]
quantity30% — share of the reviewed corpus published in 2021, the peak year
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content_tags.quantities[3]
quantity864 — records retrieved by the database searches, plus 20 from other sources
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content_tags.quantities[4]
quantity834 — articles rejected at the eligibility stage
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content_tags.quantities[5]
quantity20 — articles retained for the final synthesis
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content_tags.quantities[6]
definitionartificial intelligence (AI)
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content_tags.definitions[0]
definitionsystematic literature review (SLR)
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content_tags.definitions[1]
definitionPRISMA
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content_tags.definitions[2]
definitionintelligent tutoring system
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content_tags.definitions[3]
definitionresearch methodology
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Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptartificial intelligence
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concepts[0]
conceptmachine intelligence
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concepts[1]
conceptexpert systems
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concepts[2]
conceptintelligent tutoring system
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concepts[3]
conceptcomputer program
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concepts[4]

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