Current practices and future direction of artificial intelligence in mathematics education: A systematic review

Awang, Yusop, Danaee · International Electronic Journal of Mathematics Education · 2025 · education · 10.29333/iejme/16006 · record awang2025-current-practices-and-future-direc
tag layer · index · concepts · authors · עברית
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 contribution is an inventory of what tools exist and are in use, not a causal or methodological claim; the review does also carry a methodological component (a documented SLR protocol) but its stated results are enumerations. The three claim relations are all gap-statements against explicitly named prior reviews, which is why 'extends' rather than 'critique' was chosen for claim type overall. The paper's stated limitations concern only the search window and language; it does not discuss reviewer bias, single-coder extraction, or the absence of a formal quality appraisal, so those are not recorded. Note the extraction contains typos from the original ('Hoewever', 'neccssarily', 'Sudies', 'Tertier') which were not corrected in any quote used. 2026-08-14, 2026-08-15 and 2026-08-25 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.
How to read this page. The article itself is not reproduced here — this paper is not cleared for redistribution, so what follows is the tag layer alone. The evidence sentences are withheld with the article. The table at the foot lists every tag the record carries; each row names the facet it belongs to, and the whose words column says whether the tag records the paper's own wording or the tagger's sentence about the paper. Every tag links to each other paper in the corpus carrying it. The machine-readable edition of the same thing is the TEI file beside this one, where the tags live in standOff, and the article is at its publisher via the link below. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.
Other fields define these differently the same word, defined another way in another discipline
systematic review 4 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 4 concept tags on this paper, all 4 are grounded in a quoted sentence.
commercialized AI tools systematic review market-ready AIME learning analytics turns large volumes of student data into identification of struggle points, which lets teachers intervene in a targeted way and so improves outcomes tool type is matched to learner maturity: chatbots require the capacity to handle complex interactive systems and so suit older students, while AR/VR carries engagement for younger ones artificial intelligence (AI) Generative AI Tools Pedagogical AI Tools publication period literature type search category frequency count systematic-review

Legend

relation to other workclaim_relationshow this paper stands to another: agrees, contradicts, extends, qualifies
claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
conceptconceptsan idea the paper works with, quoted where the paper uses it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
phenomenonphenomenonthe effect or regularity under study
sensesensesa distinct use of a term, quoted where the paper fixes it

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://doi.org/10.29333/iejme/16006. The reading view exists for the papers whose licence permits it.

Every tag on this page

claim_relations 3 · claim_type 1 · concepts 4 · content_tags 24 · controls 1 · limitations 5 · mechanisms 2 · method 1 · phenomenon 1 · senses 3

This record holds no relations between concepts. A layer is empty when the tagger recorded nothing in it — which is not the same as the paper having nothing to record.

facettagfieldconf.whose words
claim_typeexistence
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claim_typethe paper's words
phenomenonartificial intelligence in mathematics education
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phenomenonthe paper's words
methodsystematic-review
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methodthe paper's words
mechanismsmechanisms
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mechanisms[0]the tagger's words
mechanismsmechanisms
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mechanisms[1]the tagger's words
limitationsThe five-year (2019-2024) window may miss longer-term developments in the field.
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what this limits: the review's temporal coverage: THIS TIMEFRAME MAY NOT CAPTURE LONG-TERM DEVELOPMENTS
limitations[0]the tagger's words
limitationsOnly English-language publications were included, excluding multilingual research.
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what this limits: the linguistic coverage: THIS STUDY FOCUSED ON ENGLISH-LANGUAGE ARTICLES DUE TO PRACTICAL CONSTRAINTS
limitations[1]the tagger's words
limitationsThe review's map of the field is a map of the market: it covers only tools that are already commercialised and market-ready.
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what this limits: this review as a survey of AI in mathematics education: it is scoped to commercialised, market-ready tools, so non-commercial and research-only systems are outside it by design
limitations[2]1.0the tagger's words
limitationsThe literature searched excludes non-English publications and every non-journal form, so the picture also excludes conference and book literature. Quote is from the criteria table.
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what this limits: the coverage of this review: the criteria table excludes non-English publications, book series, books, book chapters and conference proceedings
limitations[3]1.0the tagger's words
limitationsThe evidence base is 32 articles, reached after 24 were deleted by the exclusion criteria and 25 removed as redundant - so the synthesis rests on a set whose size is set as much by the filters as by the literature.
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limitations[4]0.9the tagger's words
controls81 remaining articles screened on the complete text, with 24 deleted against the stated exclusion criteria
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eliminates: articles that meet the search string but not the question - screening on the full text rather than the abstract is what catches them · outcome (stated by the tagger, no quotation): held
controls[0]0.85the tagger's words
conceptscommercialized AI tools
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concepts[0]the paper's words
conceptssystematic review
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concepts[1]the paper's words
conceptsmarket-ready
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also in: market-ready
concepts[2]the paper's words
conceptsAIME
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also in: AIME
concepts[3]0.9the paper's words
sensesAI in mathematics education - AS THIS REVIEW MAPS IT: commercialised, market-ready tools, reported as a deliberate bridge from research to practice rather than as a survey of the research
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senses[0]1.0the tagger's words
sensesthe gap this review addresses - stated as a gap in ACCESSIBILITY rather than in knowledge: the synthesis missing is of tools educators can readily adopt
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senses[1]1.0the tagger's words
sensesthe gap this review fills - stated as a gap in ACCESSIBILITY rather than in knowledge: a shortage of syntheses of tools 'that educators can readily adopt', which makes adoptability the criterion of relevance
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senses[2]0.95the tagger's words
claim_relationsextends → Hwang (2022)
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claim_relations[0]the tagger's words
claim_relationsextends → Mohamed et al. (2022)
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claim_relations[1]the tagger's words
claim_relationsextends → Niño-Rojas et al. (2024)
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claim_relations[2]the tagger's words
content_tagsfrequency count
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content_tags.statistics[0]the tagger's words
content_tagsMost studies of AI in mathematics education were conducted at the middle/high school level.
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content_tags.findings[0]the tagger's words
content_tagsThe AI tools used in mathematics education fall into eight named types: adaptive learning systems, intelligent tutoring systems, chatbots, augmented reality, virtual reality, serious games, learning analytics and mathematics problem solvers.
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content_tags.findings[1]the tagger's words
content_tagsChatbots were the most frequently studied AI tool category in the 2019-2024 window, with virtual reality tools second.
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content_tags.findings[2]the tagger's words
content_tagsChatbot studies appear only at university and adult-learner level, never in school-level mathematics classrooms.
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content_tags.findings[3]the tagger's words
content_tagsAI-driven serious games and mathematics problem-solver applications are the two least-researched categories in the corpus.
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content_tags.findings[4]the tagger's words
content_tagsMost commercialized tools identified are pedagogical rather than generative, seventeen of them being teaching-and-learning platforms such as GeoGebra, Khan Academy and Squirrel AI.
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content_tags.findings[5]the tagger's words
content_tagsAugmented and virtual reality technologies raise students' motivation and problem-solving skills in mathematics.
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content_tags.findings[6]the tagger's words
content_tagsThe reviewed literature converges on a call for longitudinal work measuring long-term effects of AI in mathematics education on learning outcomes, motivation and educational equity.
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content_tags.findings[7]the tagger's words
content_tagsartificial intelligence (AI)
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content_tags.definitions[0]the tagger's words
content_tagssystematic review
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content_tags.definitions[1]the tagger's words
content_tagsGenerative AI Tools
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content_tags.definitions[2]the tagger's words
content_tagsPedagogical AI Tools
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content_tags.definitions[3]the tagger's words
content_tagspublication period
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content_tags.variables[0]the tagger's words
content_tagsliterature type
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content_tags.variables[2]the tagger's words
content_tagssearch category
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content_tags.variables[3]the tagger's words
content_tagsN = 32 — number of primary studies included in the final synthesis
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content_tags.quantities[0]the tagger's words
content_tags36 out of 117 — articles removed at title-and-abstract screening as irrelevant
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content_tags.quantities[1]the tagger's words
content_tags43.8%, N=14 — share of studies conducted at middle/high school level, against 31.2% (N=10) at university/adult level and 25% (N=8) at primary level
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content_tags.quantities[2]the tagger's words
content_tagsseven categories — number of AI tool categories the 32 studies were classified into
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content_tags.quantities[3]the tagger's words
content_tagsN=35 — total category frequency count, higher than the 32 studies because some studies used more than one tool type
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content_tags.quantities[4]the tagger's words
content_tagstwenty-one (N=21) — commercially available AI mathematics-education tools found, out of 28 identified tools
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content_tags.quantities[5]the tagger's words
content_tagsN=17 — commercialized tools classified as Pedagogical AI Tools
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content_tags.quantities[6]the tagger's words
content_tagsseven (N=7) — studies from China, the highest of any country in the corpus
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content_tags.quantities[7]the tagger's words

Interpretive choices

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