Studies on AI-supported mathematical learning have predominantly employed qualitative methodologies and case studies
Research gap analysis derived from 3 education papers in our local library.
The gap
Studies on AI-supported mathematical learning have predominantly employed qualitative methodologies and case studies; quantitative experimental analyses with robust statistical evaluation of AI generative tools' impact on student performanc
Evidence profile
Sourced from the open questions and limitations and future work of the source papers, classified as methodology gap, spanning 2 journals.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Desafios e aprendizagens na mediação com chatbot ao inserir inteligência artificial na aula de Matemática (2026) · Boletim GEPEM · doi
The study lacks systematic investigation of how to effectively train mathematics teachers to develop competencies for guiding generative AI use in classroom settings. While teacher qualification is identified as essential for conducting generative AI mediation ethically and critically, no concrete pedagogical framework or teacher training protocol for AI literacy in mathematics education is presented or evaluated.
methodology gapopen questionsevidence 5/5Keywords: teacher qualification generative AI mathematics education AI literacy pedagogical mediation - Caminhos da Inteligência Artificial na Educação Matemática: uma revisão narrativa sobre abordagens e práticas da produção nacional (2026) · Boletim GEPEM · doi
Studies on AI-supported mathematical learning have predominantly employed qualitative methodologies and case studies; quantitative experimental analyses with robust statistical evaluation of AI generative tools' impact on student performance, mathematical engagement, and learning outcomes in mathematics are scarce in the national production.
methodology gaplimitationsevidence 5/5Keywords: AI generative tools mathematics learning experimental design quantitative analysis student performance - Nadarjeni učenci v dialogu z generativno umetno inteligenco: primer oblikovanja individualiziranih programov na OŠ Hajdina (2026) · Revija Inovativna pedagogika · doi
Teacher mediation practices in prompt engineering and critical evaluation of AI outputs are described qualitatively from diary reflections; systematic comparison of different teacher guidance strategies (varying levels of directiveness, questioning techniques, information verification protocols) and their effects on student learning outcomes is needed.
methodology gapfuture workevidence 5/5Keywords: teacher mediation prompt engineering critical evaluation generative AI pedagogical guidance gifted learners
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