A Meta Synthesis of Digital and AI Tools for Conceptual Understanding in Higher Mathematics Education

Abstract

This review synthesises empirical research on digital technologies in higher mathematics education and examines their effects on conceptual understanding, motivation, and barriers to digital tool use. The study employed a qualitative meta-synthesis of forty-three peer-reviewed studies published between December 2000 and October 2025, incorporating qualitative and mixed-methods research; quantitative studies were included descriptively to contextualise the findings. Data were analysed through descriptive coding and thematic analysis, considering publication year, country, methodology, digital tools, outcomes, and barriers. The reviewed papers covered technologies such as CAS, GeoGebra, AI tools, LMS platforms and digital assessment systems. The findings demonstrated significant, positive impacts of digital tools on visual-based understanding, advanced cognitive processes, motivation and engagement. Numerical analysis showed that 65% of studies reported improvements in conceptual understanding, 56% reported increased motivation and engagement, and 30% identified barriers such as technical challenges. Additionally, AI tools exhibit potential yet present risks due to erroneous outputs. Principal obstacles encompassed technical challenges, insufficient digital competencies, teacher preparedness and students' excessive dependence on technology. Digital technologies augment learning when complemented by adequate training, infrastructure and pedagogically effective integration. A major limitation lies in the uneven methodological distribution across studies. Further research is required regarding AI-driven systems, cross-cultural implementation and technology-aligned assessment.