Asian Journal of Education and Social Studies

Digital technology is now embedded in mathematics classrooms across most school systems, yet the pedagogical question of how such technology should be used to support mathematical learning remains unsettled. Quantitative syntheses report effects ranging from negligible to substantial, large-scale international assessments frequently associate school technology use with weaker mathematical performance, and teacher-level studies show that availability of tools rarely translates into changes in mathematical practice. This critical narrative review examines the state of knowledge on mathematics teaching methodology in digitally mediated settings and develops an integrated pedagogical framework for meaningful technology integration. The review draws on peer-reviewed literature identified through structured searching of open scholarly databases and indexes, supplemented by backward and forward citation searching and by examination of authoritative institutional reports. Evidence was appraised for design adequacy, duration, outcome validity, sample representativeness and consistency with independent findings, and was synthesised thematically rather than study by study. Four critical findings emerge. Aggregate effect estimates are dominated by short, small, researcher-implemented studies, and the largest reported effects cluster in precisely those conditions. Tool-class comparisons show that effects depend on the epistemic function a tool performs rather than on its technical sophistication. Correlational evidence from international assessments cannot be read as evidence of harm, because usage measures conflate remediation, compensation and pedagogy. Teacher enactment, rather than tool provision, is the principal determinant of whether digital mediation alters mathematical activity. The proposed framework organises integration around mathematical purpose, instrumental orchestration, representational congruence, epistemic authority, feedback calibration and equity by design, and distinguishes propositions that are well supported from those that remain conjectural. Generative artificial intelligence intensifies rather than resolves these questions, because it relocates the production of mathematical justification. Priorities for research include longer classroom-embedded trials, process-sensitive outcome measures, and studies that treat teacher orchestration as a manipulated variable rather than as background noise.