Publication activity
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
Research across participating journals · All insights follow your collection filters.
Publication activity alongside incoming citations.
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Leading affiliations and themes in this collection.
Full distributions & methodology ↓Independent sources. Clear time scales.
Crossref and OpenAlex · separate totals for the same publication cohorts
Hover over a year or focus the chart and use arrow keys to inspect values.
Latest lifetime totals, grouped by the cited article’s year. These are not citations received in that year.
OpenAlex · incoming citation links grouped by citing-work year
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.
Articles by publication year
Latest lifetime totals · grouped by publication year
Latest lifetime totals · grouped by publication year
By year of the citing work · 2017–2026
2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Crossref citation coverage: 4 / 4 publications (100.0%). Latest observation: 21 Oct 2026, 00:14 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 22 Oct 2026 01:10 UTC — 22 Oct 2026 04:39 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
| Publication year | Journal / scope | Publications | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 0 | 2 | 2/2 | 2/2 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 4 / 4 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-22T01:10:53.771662+00:00 — 2026-10-22T04:39:58.923999+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 0 | 4 | 4/4 |
| 2018 | All selected publications | 0 | 4 | 4/4 |
| 2019 | All selected publications | 0 | 4 | 4/4 |
| 2020 | All selected publications | 0 | 4 | 4/4 |
| 2021 | All selected publications | 0 | 4 | 4/4 |
| 2022 | All selected publications | 0 | 4 | 4/4 |
| 2023 | All selected publications | 0 | 4 | 4/4 |
| 2024 | All selected publications | 0 | 4 | 4/4 |
| 2025 | All selected publications | 2 | 4 | 4/4 |
| 2026 | All selected publications | 1 | 4 | 4/4 |
Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
Publications by publication year. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2025 | 2 |
| 2026 | 2 |
Latest Crossref totals for the selected publications; not citations received in the selected years.
Unknown counts are excluded from the mean, median and cited-publication rate.
Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.
| Journal / publisher | Publications | Crossref citations | OpenAlex citations | OpenAlex coverage | Crossref mean | Crossref median | Crossref cited %* | Crossref coverage |
|---|---|---|---|---|---|---|---|---|
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 4 | 3 | 3 | 4/4 | 0.75 | 0.0 | 25.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
This study explores Artificial Intelligence’s potential in promoting social inclusion in South African tertiary institutions. Despite advancements in expanding access to higher education, historical disparities continue to exclude marginalised groups from accessing equal opportunities in education and human capital development. Artificial Intelligence (AI) offers a promising avenue to bridge this gap by enhancing accessibility, promoting personalised learning, and streamlining administrative processes. The study adopts a desktop literature review to explore how AI-powered tools can leverage intelligent assistive technologies, personalised learning experiences and automated administrative tasks that reduce bureaucratic barriers to enhance physically challenged students’ accessibility and address their diverse learning styles and needs. Despite the potential benefits of AI tools, challenges such as the digital divide, infrastructure constraints, AI bias, cultural sensitivity, and institutional capacity persist to hinder massive adoption. The study recommends infrastructure development, digital literacy programs, culturally sensitive AI design, and capacity-building initiatives for educators to fully harness AI's potential for social inclusion in higher education institutions. The study contributes new knowledge to the ongoing discourse on AI-driven social inclusion in higher education. It emphasises opportunities and challenges prevalent in South Africa. Hence, the study demonstrates how leveraging AI can foster a more inclusive and equitable learning environment
Sophisticated AI applications like ChatGPT and Grammarly offer distinct advantages for enhancing writing proficiency. They assist users in conceptualising ideas, organising material, and refining writing style, whereas Grammarly focuses on improving grammar, punctuation, and clarity with context-aware recommendations. This is particularly relevant for students who are non-native English speakers, as it supports them and helps bridge educational disparities. While these instruments markedly enhance scholarly writing abilities, their efficacy in tertiary education remains a subject of debate. Recent occurrences of academic dishonesty associated with the improper use of generative AI technologies have negatively impacted higher education. This study assesses the impact of artificial intelligence on enhancing academic writing abilities in students who are not native English speakers. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, this study consolidates evidence from scholarly literature, policy reports, and institutional case analyses. Keywords, synonyms, and subject headings were employed to query three electronic databases from 2021 to 2025. The electronic repositories included Scopus, Emerald Insight, Google Scholar, and ProQuest. The evaluation of the publications was conducted through titles and abstracts, followed by an assessment of the full-text papers against the qualifying criteria. The results were derived and amalgamated into a narrative synthesis from selected publications. The results underscored AI's ability to customise and improve the educational experience for learners.
This systematic review examines artificial intelligence (AI)-powered digital transformation in mathematics education as a strategic business ecosystem issue rather than only a classroom technology concern. The objective is to synthesise how AI-supported pedagogies create educational value, what ecosystem risks accompany adoption, and which organisational capabilities and governance conditions enable responsible implementation. Guided by PRISMA 2020 principles, peer-reviewed empirical and conceptual studies published between 2018 and 2025 were identified from Scopus, Web of Science, ERIC, Education Source and Google Scholar. A total of 3,241 records were initially retrieved; after duplicate removal, screening and full-text eligibility assessment, 203 studies were included for structured extraction, quality appraisal and thematic synthesis. The evidence was analysed through a business ecosystem lens focusing on value co-creation, stakeholder interdependence, digital governance, teacher capability, infrastructure readiness and equity. The findings show that AI tools can strengthen adaptive practice, formative feedback, diagnostic analytics, multimodal representation and teacher decision-making when they are embedded in coherent institutional strategies. However, strategic value is weakened by digital inequality, data privacy risks, algorithmic opacity, cognitive offloading, platform dependence, weak curriculum alignment and insufficient teacher capability. The study contributes an ecosystem strategy framework that positions AI as a pedagogical and organisational mediator within a network of learners, teachers, institutions, technology providers, policy bodies, infrastructure actors and communities. It concludes that AI adoption in mathematics education should be strategy-led, ethically governed and capability-driven. Sustainable value creation requires inclusive infrastructure, teacher professional development, data governance, assessment redesign, transparent procurement and partnerships that protect educational equity while enabling innovation in resource-constrained and digitally uneven contexts.
A transformative shift is imminent as generative artificial intelligence facilitates a synthetic and highly individualised approach to information creation and transmission. Technological breakthroughs and innovations necessitate the integration of technology across all facets of life, operations, and education to improve efficiency and effectiveness. Numerous institutions worldwide have adopted Generative AI (GenAI) as a revolutionary tool in educational dialogue, encompassing teaching, learning, student participation, and research. The study sought to assess digital innovations using GenAI, video platforms, and social media within the educational sphere. A mixed-methods design was employed including 225 participants. Research indicated that AI improves personalisation and engagement, while video and social media tools promote collaboration and digital literacy. GenAI can automate administrative functions, support multidisciplinary research, and enhance dynamic and interactive learning experiences. Artificial intelligence can automate and improve several processes, facilitating support for both learners and educators, from managing routine enquiries to optimising resource allocation via predictive analytics. Video educational platforms facilitate the creation of interactive and customised content. Social media may augment initiatives, cultivate digital literacy, and promote user-generated content. The research suggests that effective integration necessitates ethical AI knowledge and institutional capability.
Sophisticated AI applications like ChatGPT and Grammarly offer distinct advantages for enhancing writing proficiency. They assist users in conceptualising ideas, organising material, and refining writing style, whereas Grammarly focuses on improving grammar, punctuation, and clarity with context-aware recommendations. This is particularly relevant for students who are non-native English speakers, as it supports them and helps bridge educational disparities. While these instruments markedly enhance scholarly writing abilities, their efficacy in tertiary education remains a subject of debate. Recent occurrences of academic dishonesty associated with the improper use of generative AI technologies have negatively impacted higher education. This study assesses the impact of artificial intelligence on enhancing academic writing abilities in students who are not native English speakers. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, this study consolidates evidence from scholarly literature, policy reports, and institutional case analyses. Keywords, synonyms, and subject headings were employed to query three electronic databases from 2021 to 2025. The electronic repositories included Scopus, Emerald Insight, Google Scholar, and ProQuest. The evaluation of the publications was conducted through titles and abstracts, followed by an assessment of the full-text papers against the qualifying criteria. The results were derived and amalgamated into a narrative synthesis from selected publications. The results underscored AI's ability to customise and improve the educational experience for learners.
This systematic review examines artificial intelligence (AI)-powered digital transformation in mathematics education as a strategic business ecosystem issue rather than only a classroom technology concern. The objective is to synthesise how AI-supported pedagogies create educational value, what ecosystem risks accompany adoption, and which organisational capabilities and governance conditions enable responsible implementation. Guided by PRISMA 2020 principles, peer-reviewed empirical and conceptual studies published between 2018 and 2025 were identified from Scopus, Web of Science, ERIC, Education Source and Google Scholar. A total of 3,241 records were initially retrieved; after duplicate removal, screening and full-text eligibility assessment, 203 studies were included for structured extraction, quality appraisal and thematic synthesis. The evidence was analysed through a business ecosystem lens focusing on value co-creation, stakeholder interdependence, digital governance, teacher capability, infrastructure readiness and equity. The findings show that AI tools can strengthen adaptive practice, formative feedback, diagnostic analytics, multimodal representation and teacher decision-making when they are embedded in coherent institutional strategies. However, strategic value is weakened by digital inequality, data privacy risks, algorithmic opacity, cognitive offloading, platform dependence, weak curriculum alignment and insufficient teacher capability. The study contributes an ecosystem strategy framework that positions AI as a pedagogical and organisational mediator within a network of learners, teachers, institutions, technology providers, policy bodies, infrastructure actors and communities. It concludes that AI adoption in mathematics education should be strategy-led, ethically governed and capability-driven. Sustainable value creation requires inclusive infrastructure, teacher professional development, data governance, assessment redesign, transparent procurement and partnerships that protect educational equity while enabling innovation in resource-constrained and digitally uneven contexts.
A transformative shift is imminent as generative artificial intelligence facilitates a synthetic and highly individualised approach to information creation and transmission. Technological breakthroughs and innovations necessitate the integration of technology across all facets of life, operations, and education to improve efficiency and effectiveness. Numerous institutions worldwide have adopted Generative AI (GenAI) as a revolutionary tool in educational dialogue, encompassing teaching, learning, student participation, and research. The study sought to assess digital innovations using GenAI, video platforms, and social media within the educational sphere. A mixed-methods design was employed including 225 participants. Research indicated that AI improves personalisation and engagement, while video and social media tools promote collaboration and digital literacy. GenAI can automate administrative functions, support multidisciplinary research, and enhance dynamic and interactive learning experiences. Artificial intelligence can automate and improve several processes, facilitating support for both learners and educators, from managing routine enquiries to optimising resource allocation via predictive analytics. Video educational platforms facilitate the creation of interactive and customised content. Social media may augment initiatives, cultivate digital literacy, and promote user-generated content. The research suggests that effective integration necessitates ethical AI knowledge and institutional capability.
This study explores Artificial Intelligence’s potential in promoting social inclusion in South African tertiary institutions. Despite advancements in expanding access to higher education, historical disparities continue to exclude marginalised groups from accessing equal opportunities in education and human capital development. Artificial Intelligence (AI) offers a promising avenue to bridge this gap by enhancing accessibility, promoting personalised learning, and streamlining administrative processes. The study adopts a desktop literature review to explore how AI-powered tools can leverage intelligent assistive technologies, personalised learning experiences and automated administrative tasks that reduce bureaucratic barriers to enhance physically challenged students’ accessibility and address their diverse learning styles and needs. Despite the potential benefits of AI tools, challenges such as the digital divide, infrastructure constraints, AI bias, cultural sensitivity, and institutional capacity persist to hinder massive adoption. The study recommends infrastructure development, digital literacy programs, culturally sensitive AI design, and capacity-building initiatives for educators to fully harness AI's potential for social inclusion in higher education institutions. The study contributes new knowledge to the ongoing discourse on AI-driven social inclusion in higher education. It emphasises opportunities and challenges prevalent in South Africa. Hence, the study demonstrates how leveraging AI can foster a more inclusive and equitable learning environment
Source coverage refers to this result set; a publication may occur in several sources. Sync times refer to contributing journals.
Missing abstracts: 0 · Missing authors: 0 · Missing publication dates: 0 · Dates without day precision: 0
| Source | Most recent successful journal sync | Latest attempt |
|---|---|---|
| crossref | 21 Oct 2026 00:14 UTC · ijbes | complete · ijbes · 21 Oct 2026 00:14 UTC |
| ojs-rest | 21 Oct 2026 00:14 UTC · ijbes | complete · ijbes · 21 Oct 2026 00:02 UTC |
| oai-pmh | 20 Oct 2026 19:49 UTC · ijbes | complete · ijbes · 20 Oct 2026 19:48 UTC |
| openalex-enrichment | 21 Oct 2026 00:14 UTC · ijbes | complete · ijbes · 21 Oct 2026 00:14 UTC |