Future studies should consider longitudinal analyses to understand long-term impacts, comparative cross-cultural research to validate findings and deeper exploration of algorithmic biases, fairness an

The gap

Consensus across the literature

Supporting evidence — 8 representative gaps

• Adopt, adapt, or reject? Analysing student-AI interaction in university curriculum-aligned writing tasks (2026) · doi

  This small-scale pilot study demonstrates how AI can be integrated into academic writing through structured and reflective design. It offers practical guidance for educators and to build pedagogically grounded exploratory directions for researchers seeking approaches to AI-assisted language and literacy learning. For educators, the study highlights AI literacy development through task design. Structured prompts that require students to justify whether they adopt, adapt, or reject AI suggestions help transform assistance into reflection. By embedding these scaffolds within authentic, curriculum-linked tasks, teachers can cultivate students’ critical awareness and ethical reasoning. These findings also point to the need for professional learning that enables educators to model responsible prompting and engagement with AI tools. For researchers, the innovation offers a replicable methodological model for examining student-AI interaction. The use of AI-interaction logs and thematic coding demonstrates how process-level analysis can capture the dynamics of regulation, evaluation, and adaptation. Future research should extend this approach to explore the longitudinal development of AI literacy and test the scalability of different scaffold types across disciplines. Effective integration of AI in language education requires both pedagogical intentionality and empirical grounding. When guided by structured design and reflective practice, AI can serve as a catalyst for ethical judgment, learner agency, and deeper engagement with academic discourse. Lee et al., Applied Language Sciences, 2026 Page 8 of 10

  Keywords: structured design educators language literacy demonstrates academic reflective offers researchers learning development students ethical model
• Artificial Intelligence in Assessment: A Bibliometric Review of Research Development, Thematic Patterns and Research Clusters (2026) · doi

  Additionally, future research should explore how AI tools, such as large language models and machine learning, can be integrated into existing curricula without displacing essential teacher roles, ensuring that feedback remains pedagogically meaningful. As the field continues to evolve, future research should explore scalable and equitable solutions, ensuring that technological innovations benefit diverse educational contexts.

  Keywords: future explore ensuring additionally tools large language models machine learning integrated existing curricula without displacing
• Reframing Constructivist Mathematics Pedagogy through Artificial Intelligence for Core Mathematics Topics in the FET Phase, Gauteng North, South Africa (2026) · doi

  This study is conceptual in nature and does not draw on primary empirical data from classrooms, teachers, or learners. The framework and interpretations advanced therefore constitute theoretically grounded propositions rather than empirically validated claims. Conceptual research offers value through theoretical integration and clarification of complex educational relationships, yet the absence of empirical evidence limits conclusions regarding classroom enactment, learner experience, or instructional outcomes across varied settings (Anderson, 2020; Brownson et al., 2022). The framework is explicitly grounded in the context of Gauteng North, South Africa, and focuses on core mathematics topics within the FET phase. This contextual and phase specific focus strengthens conceptual precision, although it limits direct transferability to other phases, subject areas, or policy environments. Application in settings with more severe infrastructural constraints or differing governance arrangements may require conceptual adaptation. Implementation research consistently demonstrates that contextual variation shapes how innovations are interpreted and sustained in practice (Rapport et al., 2021; Sarkies et al., 2022). Ethical considerations related to AI are addressed at a conceptual level rather than through empirical investigation of lived experiences. Issues such as algorithmic bias, data governance, and professional judgement require context specific empirical exploration to understand how they materialise in everyday pedagogical decision making (Touloukian et al., 2024; Purwadi & Suhana, 2025). These limitations do not diminish the conceptual contribution of the study. Instead, they clarify boundary conditions and identify priorities for future empirical research aimed at testing, refining, and extending the framework across diverse educational contexts. http://ijlter.org/index.php/ijlter

  Keywords: conceptual empirical framework grounded rather educational limits across settings context phase contextual specific governance require
• ARTIFICIAL INTELLIGENCE IN EDUCATION: EXPLORING OPPORTUNITIES AND CHALLENGES IN THE TEACHING-LEARNING PROCESS (2026) · doi

  i. Capacity Building and Teacher Professional Development: Developing teacher preparation programs with an emphasis on digital pedagogy and artificial intelligence is crucial. Initiatives for ongoing professional development should be developed to improve instructors’ technological proficiency, pedagogical knowledge, and self-assurance in successfully incorporating AI tools into teaching methods. ii. Ensuring Equity and Access to Technology: To deal with the problem of digital inequality, it is necessary to make sure that everyone has equal access to technology. Particularly in rural and poor regions, governments and educational institutions should make investments in digital infrastructure, such as devices, internet connectivity, and AI-enabled platforms. iii. Ethical Governance and Data Protection: The proper application of AI in education depends on the development of strong data protection regulations and unambiguous ethical standards. To promote confidence among all parties involved, regulatory frameworks must ensure accountability, openness, and the security of student data. Page | 413 The Social Science Review A Multidisciplinary Journal. March-April, 2026. Vol. 4. Issue 2. 410-415 Published by: Pather Dabi Educational Trust, (Regn No: IV-1402-00064/2023), Under Govt. of West Bengal, India iv. Collaborative and Multi-Stakeholder Approach: Teachers, legislators, and IT developers must work together to integrate AI successfully. These collaborations can help ensure that technology developments are in line with educational requirements by facilitating the development of context-specific and pedagogically relevant AI solutions. v. Balanced Integration of AI and Human Interaction: It is important to promote a balanced approach to AI implementation, where technology enhances human connection rather than takes its place. The social, emotional, and ethical aspects of education can be maintained through blended learning models that integrate AI-driven technologies with traditional instructional methods.

  Keywords: development technology digital educational ethical teacher professional successfully access make protection education promote must ensure
• Developing AI-Based Mathematics Learning Media to Foster Learning Independence in Early Childhood Education: An ADDIE-Based Study (2026) · doi

  Based on the findings of this study, it is recommended that early childhood education institutions integrate AI-based learning media into mathematics instruction to foster children’s learning independence from an early age. The adaptive features of AI, such as immediate feedback and personalized learning challenges, have proven effective in supporting children’s understanding of basic mathematical concepts and should therefore be utilized as complementary tools alongside conventional teaching methods. Teachers are encouraged to receive continuous training and professional development related to the use of AI-based learning media. Adequate guidance will enable educators to optimally design learning activities, interpret data generated by AI systems, and provide appropriate pedagogical support that aligns with children’s developmental stages and individual learning needs. Future research is recommended to involve larger and more diverse samples across different early childhood education settings to strengthen the generalizability of the findings. Longitudinal studies are also suggested to examine the long- term impact of AI-based learning media on children’s learning independence, mathematical achievement, and other developmental aspects. In addition, policymakers and educational stakeholders should consider supporting the development and implementation of AI-based learning media through appropriate infrastructure, funding, and regulations. Such support will ensure that the integration of AI in early childhood education is sustainable, ethical, and accessible, ultimately improving the quality of learning in mathematics and other subjects.

  Keywords: learning based early media children childhood education recommended mathematics independence supporting mathematical development appropriate support
• Challenges of secondary mathematics pre-service teachers in using ChatGPT in mathematics learning (2026) · doi

  Researchers recommend enhancing ChatGPT’s integration into mathematics learning by addressing several key areas. Educational institutions should invest in reliable internet connectivity and updated hardware to facilitate their practical use. Educators would benefit from training sessions and workshops that enable them to integrate ChatGPT into their teaching practices. For students, ChatGPT should be used as a supplementary resource, with an emphasis on cross- validating its responses and consulting other sources, especially for complex problems. Collaboration between AI developers and educators is essential to ensure ChatGPT’s features align with pedagogical practices and curriculum standards. At the same time, policymakers should advocate for AI tools as complementary resources rather than replacements for traditional methods. Communities can also support technological literacy by promoting programs that provide resources and training on AI tools like ChatGPT, local organizations and educational institutions. Emphasizing ChatGPT as a complementary tool within a broader educational framework is important, and further research should assess its long-term impact on students’ mathematical skills, using a mixed-method approach to explore its potential, challenges, and benefits. involving Author contributions: JMMD contributed to conceptualization, data curation, investigation, visualization, writing of the original draft, and review and editing of the manuscript. MRDS contributed to formal analysis, investigation, methodology, software, and writing – review & editing. JMA contributed to project administration, resources, supervision, validation, and writing – review & editing. FCK contributed to supervision, validation, and writing – review & editing. All authors approved the final version of the article. Funding: The authors received no financial support for the research and/or authorship of this article. Acknowledgments: The authors would like to thank the CTU-AC bachelor of secondary education major in mathematics participants, research teachers, and advisers. Ethics declaration: The authors adhered to research ethics protocols throughout this study. All participants signed a consent form before participating in the study. Participants’ names were anonymized to ensure confidentiality. AI statement: The authors acknowledge the use of AI tools, specifically Grammarly for grammar and style enhancement and ChatGPT for vocabulary refinement and conceptual development, during the preparation of this manuscript. All AI-generated content was thoroughly reviewed, fact- checked, and revised by the authors, who retain full responsibility for the accuracy and integrity of the published work. Declaration of interest: Authors declared no competing interest. Data availability: Data generated or analyzed during this study are available from the authors on request. REFERENCES Adel, A., Ahsan, A., & Davison, C. (2024). ChatGPT promises and challenges in education: Computational and ethical perspectives.

  Keywords: chatgpt authors contributed writing review editing educational tools resources participants mathematics institutions educators training practices
• Visualizing Mathematics Learning: A Science Mapping of Augmented Reality and Immersive Learning Technologies in Mathematics Education (2026) · 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.

  Keywords: learning mathematics immersive technologies education augmented must mathematical researchers access technology future designs development ijlter
• Examining the use of artificial intelligence in pre-service teacher education (2026) · doi

  The studies analyzed in this review were mainly carried out in particular geographical contexts, which could restrict the applicability of the findings to different educational systems and cultures. In addition, the swift progress in AI technologies has the potential to affect the relevance of some findings over time, as the emergence of new tools and applications continues to reshape the educational landscape. It should also be acknowledged that this review was based on the existing published literature, and there may be further unpublished studies or ongoing research that could offer additional insights into the subject. Despite these limitations, this systematic review provides valuable insights into the current state of AI integration in PST education and establishes a foundation for future research and practice. Although there are certain limitations, this systematic review gives significant insights into the status of AI integration in PST education and establishes a basis for future research and implementation. Given the ongoing progress and influence of AI technologies in education, it is imperative for teacher education institutions, policymakers, and academics to work together and tackle the obstacles and possibilities that come with integrating AI into education. By providing PSTs with the necessary skills to effectively use AI technologies and understand the ethical considerations, we can guarantee that the future generation of educators can utilize AI’s promise to improve teaching and learning in the 21st century. Author contributions: Conceptualization: EBP, OVS, MRZ; Data curation: OVS, KMB; Formal Analysis: EBP, KMB, MVM; Methodology: EBP, MRZ, RLB; Writing – original draft: OVS, MRZ; Writing – review & editing: EBP, RLB, MVM. All authors approved the final version of the article. Funding: The authors received no financial support for the research and/or authorship of this article. Ethics declaration: This article is a systematic review of previously published studies and does not involve any direct research with human participants or animals. Therefore, ethical approval and informed consent were not required AI statement: Artificial intelligence was not used in the creation of the content in the study. Declaration of interest: The authors declared no competing interests. Data availability: The data generated or analyzed during this study are available from the authors upon request. REFERENCES Adams, C., Pente, P., Lemermeyer, G., & Rockwell, G. (2023). Ethical principles for artificial intelligence in K-12 100131. Intelligence, Education:

  Keywords: review education authors technologies insights systematic future ethical article intelligence analyzed educational progress published there