The discussion highlights the study's contribution to addressing a gap in the literature by providing a comprehensive and specialized assessment tool for the non-university educational stage, and situ

The gap

Consensus across the literature

Supporting evidence — 8 representative gaps

• Artificial intelligence and strategic communication (2026) · doi

  Findings related to (RQ9): What are the future academic directions for the use of AI in DM and SC education? First, the vast majority of the surveyed literature predicts that higher education institutions will develop 201312110510152025Fitting JobmarketInteractivelearningEnhancedcontentEfficiencyOpportunities and Benefits1711101024681012141618Challenges and Considerations14 G. SEIF curricula for AI skills. For example, Luttrell and Lee (2021, pp. 1–43) highlighted the universities’ need to offer different courses related to AI and digitalization, such as natural language processing, machine learning, network analysis, and artificial intelligence. This means that PR, SC, and DM curricula need to be updated and aligned with new emerging technologies to fit the media industry landscape. The second direction focused on learners and con- stituted the development of specific AI-related skills such as data, analytics, and technology, which aligns with the results of Yang (2024) and Babu and Manoharan (2024), who highlighted the development of Future-Skills, thus enabling educational institutions to equip students with essential skills that make them more qualified for the job market, preparing them for future career opportunities. The third direction is related to institutions and educators and focuses on ensuring effective and equita- ble learning environments. Certain research studies have emphasized that PR educational institutions must adapt by offering education tailored to the age of AI (Yang, 2024). Kim et al. (2021) recommended faculty development and pedagogical practices to develop the curricula. The fourth direction is related to institutions and educators. Few studies have adopted this direction, highlighting the importance of strengthening industry- academia partnerships and collaboration (Allil, 2024). From my point of view, this can be applied through two dimensions: faculty development/training or workshops and curriculum development to be aligned with rapid digitalization changes. The fifth direction is the rare number of screened studies expected to focus on fostering ethical and responsible AI use/human oversight, particularly with respect to DM courses, e.g., Acar (2024) strongly recom- mended reimagining the utilization of AI in marketing education with oversight by human experts. in personalizing student support. The most important benefits of AI integrating into SC and DM education are enhancing students’ qualifications to fit job market require- ments (Sadi & Álvarez-Nobell, 2024) and utilizing AI tools learning experiences, increasing students’ engagement, and providing real- time feedback (Singh & Pathania, 2024). The results showed that ethical concerns (bias, plagiarism, integ- rity, privacy) and the quality and reliability of AI (content, accuracy) were the most prominent chal- lenges. Administrative hurdles include technological infrastructure and access, and concerns of curricu- lum adaptation, such as integrating metaverse (Kaddoura & Al Husseiny, 2023). I propose a conceptual framework (Figure 10) that provides a structured approach for applying AI to DM and SC education strategies across three levels: higher education institutions, educators, and students. Each component was informed by the themes identified in the analysis. Generally, the opportunities associated with AI out- weigh these challenges. One of the most expected direc- tions is adapting curricula, students, and educators for AI skills, which will require industry-academia collaboration (Allil, 2024). Previous studies have shown relatively little interest in these explanations, highlighting the signifi- cance of the current study within the broader body of literature on the integration of AI into education in general, and in SC and DM education in particular. The current study is limited to reviewed literature published during 2020–2025 and the integration of cur- rent AI technologies into DM and SC education in higher education. Further research should explore dif- ferent fields and levels of education, as well as future AI tools and comparing the AI integration in SC and DM education to its integration in other areas of business and communication.

  Keywords: education institutions related skills direction development students future curricula educators integration literature higher learning industry
• Artificial intelligence and strategic communication (2026) · doi

  Discussion and Conclusion The aim of this research study is to comprehensively elucidate how AI is currently being used in educa- tion, specifically SC/PR and DM education, identify the challenges and opportunities associated with its implementation, and provide insights into future trends in adopting the use of AI in SC and DM education. Studies have highlighted several methods for employing artificial intelligence in teaching SC and DM, the most important of which are virtual reality applications, interactive learning tools, gami- fication in Education, AI-powered chatbots for The practical recommendations based on this study advocate for the use of (AI) to teach SC and DM. A comprehensive and all-inclusive tangible and intan- gible facility that fosters accessibility and builds pro- fessional development programs customized to provide “ease” and “usefulness” of AI tools should be developed. Higher education institutions are encouraged to establish strategies, regulations, and guidelines to ensure that the integration of AI into SC and DM education aligns with human values and ethical standards to foster trust and credibility, and safeguard learners’ data. MARKETING EDUCATION REVIEW 15 Figure 10. Conceptual framework for integrating AI into SC and DM Education. (*) e.g. (Acar, 2024; Allil, 2024; Luttrell & Lee, 2021; Zahay et al., 2021). (**) e.g. (Hou & Chaidaroon, 2022; Pressgrove & Kinsky, 2023; Singh & Pathania, 2024). (***) e.g. (Babu & Manoharan, 2024; Cheng & Lee, 2024; Schneider, 2023; Yang, 2024).

  Keywords: education provide tools discussion conclusion comprehensively elucidate currently used educa tion specifically identify challenges opportunities
• Assessment of Artificial Intelligence Awareness Level and Utilization Strategies Among Mathematics Students in Tertiary Institutions in Imo State (2026) · doi

  1. Tertiary institutions in Imo State should organize regular workshops, seminars, and training programmes to improve students’ and lecturers’ knowledge of specialized AI mathematical tools such as Wolfram Alpha, GeoGebra, Symbolab, and Maple. 2. Educational authorities should integrate AI education into teacher-training curricula to ensure that pre-service teachers acquire practical skills in the use of AI technologies for teaching and learning mathematics. 3. Institutions should provide adequate ICT infrastructure, internet access, and digital learning facilities to support effective utilization of AI tools in mathematics education. 4. Mathematics lecturers should encourage students to adopt AI-driven personalized learning and intelligent tutoring systems to improve problem-solving abilities and academic performance. 5. Government and institutional administrators should develop policies and support systems that promote ethical and effective use of artificial intelligence in tertiary education. 71 International Journal of Advanced Academic Research | ISSN: 2488-9849 Vol. 12, Issue 5 (2026) | www.ijaar.org

  Keywords: education learning mathematics tertiary institutions training improve students lecturers tools support effective systems academic state
• Digital Technology Utilisation and Career Readiness Among Educational Technology Students in Tai Solarin University Of Education (2026) · doi

  Based on the findings of this study, the following recommendations are made: 1. Integration of Digital Skills into Curriculum Universities should systematically integrate relevant digital technology skills such as data analysis, artificial intelligence, cybersecurity, user experience design, and cloud computing into the Educational Technology curriculum. This directly addresses the study’s finding that these competencies are essential for career readiness. 2. Emphasis on Practical and Experiential Learning Lecturers and academic departments should adopt practice-oriented teaching approaches that promote active utilisation of digital technologies through project-based learning, simulations, and real-world tasks. This is necessary given the study’s finding that utilisation, not mere access, significantly influences career readiness. 3. Provision and Effective Use of Digital Infrastructure University management should not only invest in up-to-date digital infrastructure but also ensure that these facilities are actively utilised by students through structured academic KIJER 3(2) 278 Kontagora International Journal of Educational Research (KIJER) Volume 3, Issue 2, March 2026 https://fuekjournals.org/index.php/kijer activities. This aligns with the established significant relationship between digital technology utilisation and career readiness.

  Keywords: digital technology career readiness utilisation kijer based skills curriculum educational finding learning academic infrastructure following
• Usage Pattern of AI-Driven Adaptive Learning and Analytics: Transforming Higher Education through Intelligent Instructional Systems (2026) · doi

  Based on the findings and challenges identified, the following recommendations are proposed to enhance the effective integration of intelligent instructional systems in higher education: 1. Higher education institutions should invest in reliable internet connectivity, stable electricity and modern ICT infrastructure to support effective deployment of AI-driven instructional systems and learning analytics. Collaboration between the government and institutions is essential for sustainability. KIJER 3(2) 374 Kontagora International Journal of Educational Research (KIJER) Volume 3, Issue 2, March 2026 https://fuekjournals.org/index.php/kijer 2. Continuous professional development should be provided to improve lecturers’ digital literacy and competence in using AI tools, learning analytics and adaptive learning systems, with emphasis on practical application and instructional integration. 3. Institutional leadership should implement awareness and sensitization programmes to reduce resistance to technological change and promote acceptance of AI-driven teaching innovations among academic staff. 4. Clear institutional and national policies should be established to guide data privacy, ethical use of student information, funding and strategic implementation of digital transformation in higher education.

  Keywords: instructional systems higher education learning kijer effective integration institutions driven analytics digital institutional based challenges
• Higher Education in the Age of Artificial Intelligence: An Empirical Review of Curriculum Relevance and Labour Demands in Nigeria (2026) · doi

  In view of the above findings, the paper suggested the following recommendations: 1. Higher education regulatory bodies, particularly the National Universities Commission, should undertake an immediate review of existing university curricula to mandate the integration of artificial intelligence literacy, data analytics, computational reasoning, and ethical technology use as cross-disciplinary components across all undergraduate programmes rather than restricting such competencies to computer science and engineering-related fields. 2. Universities should establish sustained faculty development programmes through partnerships with technology-driven organisations such as Microsoft, Google, and leading Nigerian digital firms to retrain academic staff on AI-related pedagogies, practical applications, and innovative assessment models that reflect workplace problem- solving demands. 3. Higher education institutions should strengthen university-industry collaboration by embedding compulsory project-based learning, industrial simulations, internship pathways, and employer-led curriculum co-design mechanisms into academic programmes to ensure that students gain practical exposure to AI-enabled work environments before graduation. 29https://doi.org/10.5281/zenodo.20254611IJO JournalsVolume 09 | Issue 05 | May 2026 | https://ijojournals.com/index.php/er/index IJO - INTERNATIONAL JOURNAL OF EDUCATIONAL RESEARCH (ISSN: 2805-413X) Edime YUNUSA * https://ijojournals.com/ Volume 09 || Issue 05 || May, 2026 || “Higher Education in the Age of Artificial Intelligence: An Empirical Review of Curriculum Relevance and Labour Demands in Nigeria” REFERENCES Acemoglu, D., & Restrepo, P. (2021). Tasks, automation, and the rise in US wage inequality. Econometrica, 89(5), 1973–2016. Adebayo, T., & Salau, O. (2024). Artificial intelligence awareness and curriculum preparedness among university students in Nigeria. Education and Information Technologies, 29(2), 2451–2473. Adeyemi, A., & Okolie, U. (2024). Graduate employability and digital competence mismatch in Nigerian technology firms. Industry and Higher Education, 38(1), 41–56. Aina, C. (2024). Graduate employability and digital skills gaps in Nigerian higher education. Higher Education, Skills and Work-Based Learning, 14(2), 301–317. Aina, C., & Mhlongo, S. (2024). Curriculum responsiveness and artificial intelligence readiness in Nigerian universities. Higher Education Policy, 37(1), 89–111. Ashwin, P. (2023). Transforming university education: A manifesto (2nd ed.). Bloomsbury Academic. Autor, D. H. (2022). The labour market impacts of technological change: From unbridled enthusiasm to qualified optimism to vast unc

  Keywords: education higher university artificial intelligence nigerian curriculum universities technology programmes digital academic https review related
• Influence of Artificial Intelligence Integration On Workforce Readiness Among Undergraduate Students of Benue State University, Nigeria (2026) · doi

  1. Integration of AI Technologies in University Learning Environments: Universities should integrate AI-supported learning tools such as intelligent tutoring systems, adaptive learning platforms, and AI-driven educational technologies to enhance students’ learning experiences and workforce readiness competencies. 2. Development of Digital Literacy Programs: Higher education institutions should incorporate structured digital literacy training into their curricula to ensure that students develop the technological competencies required for participation in AI- driven workplaces. 3. Promotion of Problem-Solving and Critical Thinking through AI-Based Learning: Educators should design learning activities that utilise AI technologies to support inquiry-based learning, simulations, and interactive problem-solving tasks that enhance students’ analytical reasoning skills. 4. Further Research on AI Integration in Higher Education: Future studies should investigate the impact of AI integration across multiple universities and explore additional workforce competencies such as creativity, collaboration, and adaptability. journal.iaiiea.org 24 Journal of Innovation in Educational AssessmentReferences Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. Bawden, D. (2015). The dimensions of digital literacy. In J. Thomas & A. L. F. Brown (Eds.), e-Learning and digital media (pp. 1–15). Sage. Caballero, C., Walker, A., & Fuller-Tyszkiewicz, M. (2019). The work readiness scale. Journal of Teaching and Learning for Graduate Employability. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. Cope, B., Kalantzis, M., & Müller, C. (2020). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(12), 1229–1245. Facione, P. (2015). Critical thinking: What it is and why it counts. Insight Assessment. Fullan, M., & Quinn, J. (2016). Coherence: The right drivers in action for schools, districts, and systems. Corwin. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education. Center for Curriculum Redesign. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Jonassen, D. (2015). Supporting problem-solving in digital learning environments. Educational Technology Research and Development. 202 Kandlhofer, M., et al. (2016). Artificial intelligence literacy in education. Computers & Education. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence unleashed: An argument for AI in education. Pearson. National Bureau of Statistics Nigeria.

  Keywords: learning education intelligence digital artificial educational literacy journal integration technologies students competencies problem solving assessment
• Hybrid AI-Integrated Smart Learning Platforms for Career-Aligned Tertiary Education and Student Lifecycle Management (2026) · doi

  4.7 This study has several limitations. First, the pilot duration was only one semester; long term retention and career placement effects could not be assessed. three Second, sample was drawn universities limiting generalisability to other regions. Third, while the control group used a traditional LMS, it is possible that some instructors in the control group adopted alternative digital tools, introducing confounding. Fourth, self reported satisfaction data may be subject to social desirability bias. Fifth, the digital divide meant that some students in the experimental group had intermittent internet access, potentially reducing the platform’s effectiveness. Future research should address these limitations through multi year, multi country studies with objective career outcome measures. V. CONCLUSION AND RECOMMENDATIONS 5.1 Conclusion This study successfully designed, developed, and evaluated a hybrid AI integrated smart learning EduCareer AI platform for career aligned tertiary education and student lifecycle management. The platform improved engagement, completion rates, career alignment satisfaction, and administrative efficiency. It provides a scalable, open source reference model for institutions seeking to leverage AI holistically, particularly in developing countries. 5.2 Recommendations IRE 1718727 ICONIC RESEARCH AND ENGINEERING JOURNALS 1111 © JUN 2026 | IRE Journals | Volume 9 Issue 12 | ISSN: 2456-8880 DOI: https://doi.org/10.64388/IREV9I12-1718727 1. For TEIs: Adopt integrated AI platforms rather than standalone tools. Invest in faculty training and infrastructure. Ensure interoperability with existing systems (e.g., legacy SIS). 2. For policymakers: Develop national standards for AI in education, including data privacy, security, and for interoperability. Provide connectivity in underserved areas. funding 3. For developers: Prioritise user centred design and offline capabilities (progressive web apps) for low bandwidth environments. Release core modules as open source to encourage adaptation. 4. For researchers: Conduct longitudinal studies on career outcomes (e.g., salary, job retention). Evaluate equity impacts across gender, socio economic status, and rural/urban divides. 5.3 Contribution to Knowledge • A validated framework for AI integrated student lifecycle management. • Empirical evidence on the effectiveness of career aligned smart learning in a developing country context. 5.4 Suggestions for Further Research • Extend the study to multiple countries and • educational levels (secondary, vocational). Investigate long term career outcomes (e.g., skills & to gained, organizational growth, starting salary, promotion rates, job satisfaction). contribution experience • Explore fairness and bias mitigation in AI recommendation algorithms (e.g., using fairness metrics like demographic parity). • Develop lightweight versions for feature phones and offline use in very remote areas. the regarding 5.5 Conflict of Interest Statement The authors declare that they have no conflicts of interest research, authorship, or publication of this article. The EduCareer AI platform was developed and evaluated solely for academic research purposes, and participation by faculty and students from the selected Nigerian universities was entirely voluntary. This study was funded independently by the author for educational purposes; no external corporate funding, sponsorship, or commercial interest influenced the study design, data collection, analysis, or interpretation of the results. REFERENCES [1] Almaiah, M.A., Al-Otaibi, S. and Alrawashdeh, M. (2022) ‘Examining the internet of educational things adoption using an extended unified theory of acceptance and use of technology’, Internet of Things, 19, p. 100558. at: https://doi.org/10.1016/j.iot.2022.100558.

  Keywords: career platform group satisfaction internet integrated educational interest limitations long term retention universities control digital