Future studies should involve larger, more diverse samples, use longer intervention periods, and investigate additional variables

The gap

Evidence profile

Sourced from the recommendations and future work and inline gaps of the source papers, classified as general, spanning 5 journals.

Supporting evidence — 5 representative gaps

• Reframing Constructivist Mathematics Pedagogy through Artificial Intelligence for Core Mathematics Topics in the FET Phase, Gauteng North, South Africa (2026) · International Journal of Learning, Teaching and Educational Research · doi

  The recommendations presented in this section are conceptually derived from the integrated framework developed in this study and are not based on empirical testing or implementation evaluation. Their purpose is to illustrate how the framework may inform pedagogical reasoning, professional learning, and system level thinking concerning AI enhanced constructivist mathematics pedagogy in the FET phase. The recommendations therefore articulate theoretically grounded directions rather than operational prescriptions, and they are explicitly aligned with the three framework domains of constructivist pedagogy, AI mediation, and implementation science. This positioning ensures coherence with the conceptual implying evidence of effectiveness or scope of the study and avoids implementation success. 8.1 Pedagogy Constructivist pedagogy should remain the primary organising logic when considering the integration of AI into FET mathematics teaching. Inquiry oriented task design, opportunities for learner explanation and justification, and structured dialogic engagement are conceptually aligned with the epistemic demands of advanced mathematics content and should guide instructional reasoning (Chuang, 2021; Kiesler, 2022). AI may be conceptually aligned with these practices when it supports exploration, representation, and reflection rather than procedural completion. Attention to pedagogical sequencing is especially important in the FET phase, where conceptual progression and assessment http://ijlter.org/index.php/ijlter 438 pressures coexist. These recommendations emphasise pedagogical coherence rather than instructional routines, reinforcing the framework’s focus on professional judgement and pedagogical intent. 8.2 Artificial Intelligence Mediation Artificial intelligence should be conceptualised as a mediating resource whose educational value is defined through its relationship to pedagogical design rather than through technological capability alone. Adaptive feedback may support constructivist learning when it prompts reflection, comparison, and conceptual refinement, while collaborative digital environments may support shared reasoning and peer explanation aligned with social constructivist principles (Toktarova & Semenova, 2020; Do et al., 2023; Pham et al., 2025). These recommendations do not imply that such outcomes will occur automatically. They instead highlight the conceptual conditions under which AI may align with constructivist pedagogy. Teacher judgement remains central in determining whether mediation supports or constrains learner agency (Anderson, 2020; Gilje, 2024). 8.3 Teacher Learning Sustained professional learning is conceptually necessary for teachers to interpret and mediate AI within constructivist mathematic

  generalrecommendations
  Keywords: constructivist pedagogical pedagogy recommendations conceptually framework learning rather aligned conceptual implementation reasoning professional mathematics mediation
• Visualizing Mathematics Learning: A Science Mapping of Augmented Reality and Immersive Learning Technologies in Mathematics Education (2026) · International Journal of Learning Teaching and Educational Research · doi

  In light of the bibliometric results and thematic trends revealed in this study, several future research, policy, and practice directions are suggested for augmented/assisted technologies for mathematics education. learning First, future research must have a better theoretical underpinning and therefore more explicitly position augmented and immersive learning designs in theories (i.e., embodied cognition, constructivism, established metacognition). In so doing, this work has the potential to advance the field beyond into-theory interpretation, while supporting explications of how and why such technologies afford mathematics learning. To operationalize this, the development of a standardized framework for AR mathematics design is suggested that aligns instrument-determined experimentation and toward http://ijlter.org/index.php/ijlter specific immersive technologies with corresponding mathematical competencies. 860 to explore learning retention, In the second instance, researchers are invited to conduct both longitudinal and transfer of large-scale empirical studies mathematical understanding, and learners’ motivation as well as spatial reasoning and problem-solving skills. Designs like that would counteract the current short-term-intervention fad and build stronger evidence base for deciding what to do in education. Additionally, researchers should target underrepresented mathematical topics, where AR’s ability to visualize complex data distributions, such as in Statistics, could provide unique pedagogical value currently missing in the literature. long-term consequences on Third, more emphasis should be placed on teacher training and development. Preservice and in-service teacher education that supports teachers in designing, implementing, and critically reflecting on augmented and immersive learning activities is vital for pedagogically sound classroom utilization. Studies targeting teachers’ pedagogical beliefs, technological skills and access to technology will help ensure more sustainable uptake. Furthermore, policymakers must prioritize necessary VR/AR infrastructure in schools to prevent digital divide. Fourth, it would be advisable for further studies to broaden their sample and focus across different educational contexts and learner profiles, especially in underprivileged or resource-limited situations. Comparative, cross-cultural research may help in understanding conditions of effectiveness and equity for technology-enhanced mathematics learning. To support this global equity, adopting specific “Open Access” practices to improve global knowledge sharing may be employed. Lastly, inter-disciplinary cooperation involving mathematics educators, learning science researchers, and technology developers is strongly advised. Such collaborations may enable the creation of learner-centered, curriculum-aligned, and ethically led immersive learning spaces that underpin the use of new technologies to drive innovation but also ensure access and quality for all learners. Crucially, this collaboration must establish ethical guidelines for AI and data-driven immersive environments which safeguards students’ privacy. Collectively, these recommendations seek to inform the subsequent wave of research in support of more meaningful, replicable, and theoretically grounded implementations of immersive technologies in mathematics education.

  generalrecommendations
  Keywords: learning mathematics immersive technologies education augmented must mathematical researchers access technology future designs development ijlter
• ARTIFICIAL INTELLIGENCE AND DIGITAL INNOVATION IN MATHEMATICS EDUCATION FOR CRITICAL THINKING: A SYSTEMATIC LITERATURE REVIEW (2026) · ACADEMIA: Jurnal Inovasi Riset Akademik · doi

  strands into a coherent framework for mathematics education in the digital era. The research gaps presented in Table 4 indicate that the existing literature has not yet fully explained how artificial intelligence and digital innovation can be systematically integrated into mathematics learning to foster critical thinking. Although the reviewed studies highlight the potential of digital tools, AI-supported learning, and innovative pedagogical approaches, most of them remain fragmented in terms of theoretical grounding, empirical validation, and classroom implementation. In particular, limited attention has been given to how AI-based learning environments influence students’ mathematical reasoning, argumentation, problem-solving processes, and critical evaluation of mathematical ideas. Therefore, future studies need to move beyond general discussions of digital innovation and provide stronger empirical evidence on how AI can be pedagogically designed, implemented, and evaluated in mathematics classrooms.

  generalfuture work
  Keywords: digital mathematics learning innovation critical empirical mathematical strands coherent framework education gaps presented indicate existing
• Improving Spatial Ability Using GeoGebra and Kahoot-Assisted Guided Discovery Learning Models (2026) · Jurnal Pendidikan MIPA · doi

  Future studies are recommended to involve larger, more diverse samples, use longer intervention periods, and investigate additional variables such as learning motivation, digital literacy, and students’ retention of spatial ability over time. Future research is recommended to involve larger, more diverse samples, use random sampling techniques, and explore the long-term effects of technology-assisted Guided Discovery Learning in mathematics education.

  generalinline gaps
  Keywords: future recommended involve larger diverse samples learning longer intervention periods investigate additional variables motivation digital
• The Applications of Augmented Reality for Enhancing Motivation and Spatial Skills in Elementary Mathematics (2026) · Journal of Information Technology Education Innovations in Practice · doi

  Practitioners are encouraged to integrate AR tools to transform abstract geometric concepts into tangible experiences. However, adequate teacher training and a hybrid curriculum combining AR with physical manipulatives are recommended for optimal implementation. The paper recommends that researchers conduct longitudinal studies to exam- ine the long-term retention of learning gains and the “novelty effect.” Addition- ally, investigating the integration of AR with adaptive learning systems for per- sonalized scaffolding is suggested. Integrating AR into education can democratize access to high-quality spatial training. This supports equity in STEM pathways, fosters inclusive learning en- vironments, and contributes to a more technologically skilled future workforce. Future research should explore the long-term effects of AR on students’ learn- ing retention, its impact across different age groups, and how personalized AR experiences can further enhance learning outcomes.

  generalfuture work
  Keywords: learning experiences training long term retention future practitioners encouraged integrate tools transform abstract geometric concepts

Questions about this gap