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Assistive and adaptive technologies for dyscalculia: a bibliometric review and narrative synthesis of AI-supported
Mehmet Koray Serin1, Aydin Bulut1, İbrahim Kepceoğlu2
1Department of Primary Education, Kastamonu University, Kastamonu, Türkiye.
Purpose:
This study aimed to map the development of assistive and adaptive technology-supported approaches to dyscalculia between 2005 and 2025 and to clarify how artificial intelligence, adaptive learning, and assistive technologies are positioned within this research field.
Methods:
A bibliometric review, complemented by a narrative synthesis, was conducted using 83 unique journal publications indexed in Web of Science and Scopus. The analysis included keyword frequency analysis, title-term distribution, keyword co-occurrence networks, thematic mapping, thematic evolution, and country-level collaboration analysis.
Results:
The findings show that early research was largely grounded in neurocognitive explanations of dyscalculia, particularly numerical processing, working memory, and developmental learning difficulties. Recent publications indicate a broadening towards digital interventions, adaptive learning environments, serious games, augmented reality applications, and AI-supported assistive technologies. Studies using machine learning, deep learning, and probabilistic models suggest potential uses of AI for early screening, personalised feedback, individualised support, and cognitive support. However, AI remains an emerging rather than fully central organising theme. The review identified international collaboration patterns across Asia, the Middle East, Europe, and North America, alongside methodological and ethical issues, including limited longitudinal evidence, variability in intervention designs, biases in database and keyword selection, data privacy, and algorithmic transparency.
Conclusions:
Technology-assisted dyscalculia research has expanded from cognitive and developmental explanations towards more intervention-oriented, adaptive, and assistive technology-informed approaches. AI-supported assistive technologies appear promising for early identification, accessibility, individualised learning support, and participation in mathematics-related activities, but require stronger empirical validation, ethical implementation, user-centred evaluation, and integration into educational and rehabilitation-related support contexts.
