Existing studies often conceptualize AI readiness

The gap

Evidence profile

Stated in the recommendations and limitations sections of the source papers, classified as general, spanning 8 journals.

Supporting evidence — 8 representative gaps

• Philosophical and critical perspectives of integrating AI into STEM curriculum design: Opportunities and challenges in African educational contexts (2026) · Eurasia Journal of Mathematics, Science and Technology Education · doi

  to guide frameworks Based on this study, two recommendations are proposed. First, educational institutions in Africa should adopt curriculum design models informed by the AI- TPACK and UTAUT the integration of AI in STEM education. This approach ensures that technological tools are aligned with pedagogical strategies and CK, promoting personalized, adaptive, and inclusive learning experiences (Ijiga et al., 2021; Mosoa & van der Westhuizen, 2025). Second, successful AI adoption requires investment in digital infrastructure, strategic collaboration with industry stakeholders. These efforts will enhance user confidence, address barriers to technology acceptance, and ensure that AI-enhanced curricula remain relevant to evolving workforce demands and educational goals (Falebita & Kok, 2024). These recommendations provide a strategic foundation for advancing AI integration in African STEM education, ensuring is both innovation pedagogically sound and socially equitable.

  generalstated in recommendationsevidence 5/5
  Keywords: integration education educational design stem tools relevant african like authors frameworks based recommendations institutions africa
• Psychological mechanisms of AI integration in ESL teaching: teacher self-efficacy and classroom practice in Ghanaian senior high schools (2026) · Frontiers in Psychology · doi

  This study set out to examine how psychological factors shape ESL teachers’ integration of AI tools in Ghanaian senior high schools. The findings indicate that AI adoption in instructional contexts is not primarily determined by access to technology or positive perceptions alone, but by teachers’ confidence in their ability to use these tools meaningfully within classroom practice. In particular, teacher self- efficacy emerged as a central mechanism linking perceptions of AI to pedagogical enactment, while contextual constraints influenced how and when this confidence could be translated into instruc- tional action. By focusing on teachers rather than students, the study contrib- utes to the growing literature on AI in education by highlighting the importance of teacher cognition as a mediating layer between techno- logical potential and instructional reality. The findings suggest that commonly used models such as the Technology Acceptance Model provide only a partial explanation of AI adoption unless comple- mented by constructs that capture teachers’ sense of competence and their situated engagement with technology. In this sense, the study advances a more integrated understanding of AI use in education as a process shaped by the interaction between perception, confidence, and context. From a practical perspective, the findings point to the need for a shift in how AI integration is approached in educational settings. Teacher development initiatives should move beyond raising aware- ness of AI tools toward building practical confidence and pedagogical competence. This includes providing opportunities for teachers to engage with AI in authentic instructional contexts, reflect on their practice, and develop strategies for guiding students’ interaction with AI outputs. In practical terms, this could involve the establishment of peer learning communities in which teachers collaboratively share experiences and experiment with AI-supported activities. Short, hands-on workshops focusing on accessible and low-cost AI tools may further support teachers in developing confidence through guided practice. In addition, in-class coaching or mentoring models, where more experienced or confident teachers demonstrate AI inte- gration strategies in real classroom settings, could help bridge the gap between theoretical understanding and pedagogical enactment, par- ticularly in resource-constrained contexts. Without such support, AI is likely to remain underutilized or confined to surface-level applications. At the institutional level, the findings highlight the impor- tance of aligning technological initiatives with the realities of classroom practice. Investments in infrastructure, such as reliable internet access, remain essential, but they must be accompanied by sustained professional support that addresses both technical and pedagogical challenges. Policies that assume immediate or widespread adoption of AI without considering teachers’ readi- ness and contextual constraints risk overestimating the impact of these technologies. Despite its contributions, the study has several limitations. First, the relatively small sample size limits the transferability of the findings and calls for cautious interpretation beyond similar contexts. Second, while the study draws on multiple qualitative data sources, including interviews, classroom observations, and stimulated recall, the findings remain context-specific and shaped by the conditions under which the data were generated. Third, the focus on a single national context means that the findings may not be directly applicable to other educational settings with different structural and cultural conditions. Future research could address these limitations by con- ducting comparative studies across contexts and by further examining how different institutional environments shape teachers’ engagement with AI. The study underscores that the integration of AI in ESL education depends not only on technological innovation but also on the psy- chological and contextual conditions that shape how these tools are enacted in practice. By foregrounding the role of teacher psychology, the study provides a basis for more grounded and context-sensitive approaches to AI-supported teaching and learning.

  generalstated in limitationsevidence 5/5
  Keywords: teachers tools contexts confidence practice classroom teacher pedagogical context shape integration adoption instructional technology contextual
• Determinants fo AIEd Success: An Extended UTAUT2 Perspective with CB-SEM (2026) · Higher Education Studies · doi

  Policy Recommendations Based on the research findings, educational policymakers and institutional leaders should prioritise the following strategic actions: Develop trust-centered AI integration policies that address data privacy, algorithmic transparency, and ethical AI governance, as trust was found to be a critical mediating construct in the AIEd adoption pathway. Invest in facilitating conditions, including AI-enabling infrastructure, faculty training programmes, and technical support services, given that FC demonstrated significant effects on both adoption intent and adoption success. Design AI adoption incentive schemes that emphasise both the pedagogical value (PE) and the enjoyment dimensions (HM) of AI use, while ensuring that cost-effectiveness (PV) is clearly communicated to faculty stakeholders. Embed self-efficacy development programmes for AI use within faculty professional development curricula, as SE showed a direct positive effect on both IA and SAU. 7.2 Recommendations for Future Research The following directions for future research are recommended: Revise and validate the IA construct: Future research should develop new items for measuring Intent to Adopt AI for Teaching with higher factor loadings (ideally ≥ 0.70), and should consider whether a bifactorial or formative specification better reflects the multidimensional nature of behavioral intention toward AI in educational contexts.

  generalstated in recommendationsevidence 5/5
  Keywords: adoption faculty future recommendations educational following develop trust construct programmes intent development policy based policymakers
• The use of Artificial Intelligence as a Motivational Factor in Enhancing Teachers’ Job Performance in Senior Secondary Schools in Ikpopa-Okha Local Government area, EDO State. (2026) · International Journal of Latest Technology in Engineering Management & Applied Science · doi

  Based on the findings and conclusion of this study, the followings recommendations are offered. 1. School management through training and retraining policy should encourage teachers to familiarize with AI system in order to enhance their performance and productivity. 2. Teachers should not see AI-powered adaptive system as a challenge, but rather as virtual tools that can boot their performance to deliver instructions through virtual means in order for them and students to complete favourably with their counterparts globally. REFERENCES 1. Abraham, M. (2019): Hierarchy of Needs Theory in Harold Koontz (Ed) 2. Alderfer, C. (2021): “The ERG Theory” in L.S Henry (Ed) management Organization, south- Western Corporation, U.S.A 1983 ps 54-60 3. Ali B. (2020): Grammar of Local Government in Nigeria, university press plc, Lagos p.45-50 4. Booth, S. (2023). Public Confidence spots exam board sing AI. Springer 5. Bryant, J. et (2020). How artificial intelligence will impact K-12 teachers, McKinsey & Company. 6. Bryan, L. (1989). Corporate personnel management: pitman publishing, inc 128, long Accre, London WC2E9AN. 7. Cole, G.A (1990): Management Theory and practice (5th edition) Ashford 8. Hassan, B. (1991): Manpower Development in Nigerian University, case study of University of Sokoto. M P A Thesis A.B.U Zaria (unpublished) 9. Joiner, I.A (2018). Artificial Intelligence: AI is nearby. Chandos Publishin. 10. Looke, E.A. (1969): Toward, a theory of Task, motivation and incentive 11. Mcgregor, D. M. (1980): “The Human side of Enterprises” in S.M. Ngu (ed) Motivation theory and workers compensation in Nigeria, Gaskiya Corporation, Zaria p. 5-11 12. Ngu, S.M. (1994): Motivation and workers compensation in Nigeria, Gaskiya Corporation limited Zaria. 13. Robbins, S.P. (1990): motivation Theories, chigago University of Chigago.p4 14. Reiss, M. J. (2021). The yse of AI in education: Practicalities and ethical considerations. London Review of Education, 19(1), 1-14. https://doi.org/10.14324/LRE.19.1.05 Page 2543 www.rsisinternational.org INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING, MANAGEMENT & APPLIED SCIENCE (IJLTEMAS) ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026 15. Turoff, M. (2021). Designing a virtual classroom, department of computer and Institute of technology 16. Towers, E. (2021). Stayers: A qualitative study exploring why teachers and head teachers stay in challenging London primary schools. PhD thesis, King’s college London. 17. Tira, N.F. (2021). Artificial Intelligence (AI) in education: Using ai tools for teaching and learning process, https://www.research.net/publication/357447234. 18. Vroom, et’al. (2012): Management and Motivation in Organization, Hallvein West publisher Ltd. New York p. 361-364. 19. Christopher, I.

  generalstated in recommendationsevidence 5/5
  Keywords: management teachers theory motivation university london virtual corporation nigeria artificial intelligence zaria education system order
• Generative Artificial Intelligence in Tertiary Level Education in Bangladesh: Practices, Benefits, Challenges, and Prospects (2026) · Journal of Information Technology Education Research · doi

  for Researchers This study contributes empirical evidence on the adoption of GenAI in a South Asian tertiary education context, enriching the body of knowledge on technology acceptance, digital pedagogy, and GenAI in education policy. By revealing the pictures of relevant variables of Generative Artificial Intelli- gence in Education (GenAIEd) in a unique context, such as Bangladesh, the findings have implications for similar situations. They can inform others about possible challenges and the usefulness of GenAIEd. Teachers and students are both moderately familiar with GenAI. The teach- ers primarily use it to prepare courses and materials, while students sporadi- cally engage with GenAI, mainly for academic problem-solving, and they em- phasize its role in personalized, learner-centered learning. GenAI familiarity is found to be a strong predictor of usage frequency. However, teachers ex- press concerns about the reliability of GenAI, ethical implications, and the potential for deskilling. While the benefits and usefulness dominate, possible challenges and threats are marginally associated with the future adoption and use of GenAI. This finding is unique because, despite the overpowering ‘ease of use’ of the TAM model, ‘benefits or usefulness’ of the TTF model, chal- lenges, and threats have been found as catalysts for GenAI adoption. Practitioners are to utilize GenAI to support, rather than replace, their teach- ing expertise. They should also encourage students to strike a balance be- tween GenAI-assisted learning, critical thinking, and independent work. Fur- thermore, the institutions should introduce guidelines to ensure the ethical use of GenAI and academic integrity. Researchers should explore the longitudinal effects of GenAI adoption on learning outcomes and skill development. They can also conduct compara- tive studies across different universities and disciplines. Investigating the role of GenAI in inclusive education and support for learners from disadvantaged backgrounds also demands research focus. Impact on Society The findings highlight how GenAI can transform higher education in Bang- ladesh and similar contexts. It shows the importance of addressing the risks of overreliance and the unethical use of GenAI for effective learning. A bal- anced adoption could strengthen human–technology collaboration in educa- tion. On the other hand, it has revealed the aspects of GenAI, preferred by educators, that AI developers should consider.

  generalstated in recommendationsevidence 5/5
  Keywords: genai adoption education learning usefulness students researchers context technology genaied unique implications similar possible challenges
• <b>ARTIFICIAL INTELLIGENCE AND STATE OF ACADEMIC INTEGRITY AMONG PRE-SERVICE TEACHERS IN ABUBAKAR TAFAWA BALEWA UNIVERSITY (ATBU), BAUCHI STATE, NIGERIA</b> (2026) · VUNOKLANG MULTIDISCIPLINARY JOURNAL OF SCIENCE AND TECHNOLOGY EDUCATION · doi

  1. Structured AI Literacy Training: Universities should incorporate compulsory AI literacy and ethical use courses into teacher education programmes to equip pre-service teachers with skills for responsible and pedagogically sound AI use. 2. Development of Clear Institutional AI Policies: ATBU and similar institutions should develop and communicate clear guidelines on acceptable and unacceptable uses of AI in academic work, including citation requirements and assessment boundaries. 3. Promotion of Balanced AI Use: Teacher educators should emphasize AI as a supportive learning tool rather than a substitute for independent thinking, creativity, and reflective practice. 4. Assessment Reform: Assessment strategies should be redesigned to prioritize originality, critical thinking, practical teaching demonstrations, and reflective tasks that minimize unethical AI reliance. ©2026 Vunoklang Multidisciplinary Journal of Science and Technology Education (VMJSTE) Vunoklang Multidisciplinary Journal of Science and Technology Education, Volume 14 Issue 3, 2026 129 REFERENCES Aina, J. K., & Ogundele, A. G. (2020). Teachers’ awareness and use of emerging technologies for teaching in Nigerian 459–476.

  generalstated in recommendationsevidence 5/5
  Keywords: education assessment literacy teacher teachers clear thinking reflective teaching vunoklang multidisciplinary journal science technology structured
• Primary School Teachers Concerns About Technology-Induced Unemployment and Their Attitudes Toward Artificial Intelligence in Education (2026) · International Technology and Education Journal · doi

  Based on the findings, several recommendations can be made at the end of the study. 1. The fact that primary school teachers have a positive attitude towards the use of artificial intelligence in education and low concerns about technology-induced unemployment indicates that they do not perceive artificial intelligence as a threat. For this reason, materials explaining how artificial intelligence tools can be used in primary education should be produced. Sample lesson plans should be included in these materials. 2. An examination of the study's findings reveals that classroom teachers who have received training in technology have a more positive attitude towards the use of artificial intelligence in education. For this reason, in-service training courses covering artificial intelligence literacy should be provided to classroom teachers. 3. The fact that primary school teachers' attitudes towards the use of artificial intelligence in education are moderate, along with their concerns about technology-induced unemployment, indicates that teachers also have various concerns regarding artificial intelligence. Furthermore, the existence of both positive and negative views on the use of artificial intelligence in primary school lessons supports this idea. For this reason, artificial intelligence tools should be integrated into primary school education without neglecting human aspects. 4. A moderate negative relationship has been observed between primary school teachers' attitudes towards the use of artificial intelligence in education and their concerns about technology-induced unemployment. New studies could be designed incorporating variables such as artificial intelligence literacy, awareness, and self-efficacy, which may mediate this relationship. This would allow for a more in-depth examination of the subject.

  generalstated in recommendationsevidence 5/5
  Keywords: artificial intelligence primary teachers education school towards concerns technology positive induced unemployment reason fact attitude
• Overcoming Inertia: A systematic review of the stagnant integration of Artificial Intelligence in Online Learning at Universities of Technology in South Africa (2026) · Journal of Education and Learning Technology · doi

  Overcoming this stagnation necessitates a concerted national effort that moves beyond mere technological acquisition. It requires a strategic commitment to: 1. Policy and Empowerment: Crafting clear, actionable policies that demystify the use of AI, while simultaneously investing in comprehensive training that empowers lecturers to become confident and critical users of these new tools. 2. Infrastructure and Equity: Committing to significant investment not only in institutional infrastructure but also in national initiatives aimed at closing the digital literacy and access gaps for all students. Ultimately, for South Africa, the path forward is not about simply "adopting AI." It is about thoughtfully and equitably weaving it into the educational fabric. Failing to address these core challenges of educator empowerment and student equity will ensure AI remains a source of exclusion, further cementing the nation's position on the wrong side of the global innovation divide. CONCLUSION The stagnant adoption of Artificial Intelligence in South Africa’s online teaching and learning environments results from a convergence of structural, institutional, and human-related challenges. This issue represents a dual crisis of confidence and access, both of which impede progress and collectively constrain the potential of AI in higher education. A significant crisis of confidence persists among academic staff and decision-makers. The absence of well-defined institutional policies, ethical standards, and governance frameworks has generated uncertainty regarding the appropriate use of AI in teaching and learning. Insufficient training and professional development leave many lecturers unprepared and hesitant to adopt AI tools. This uncertainty undermines pedagogical authority and intensifies concerns about academic integrity and the ethical use of generative AI. Without structured support and capacity-building initiatives, academic staff remain reluctant to integrate AI into their practices, which contributes substantially to stagnation across higher education institutions. Alongside this is a persistent crisis of access, shaped by South Africa’s longstanding socio-economic disparities. Many students continue to face significant barriers, including limited device availability, unreliable internet connections, and unaffordable data costs that hinder their ability to fully engage with AI- enabled learning. Despite institutional efforts to address these inequities, economic pressures and structural inequalities continue to impede meaningful progress. As long as these obstacles continue, the benefits of AI-enhanced education will remain out of reach for a substantial portion of the student population. The interdependence of these two crises perpetuates a cycle of stagnation.

  generalstated in recommendationsevidence 5/5
  Keywords: institutional stagnation significant access south africa learning crisis education academic continue national empowerment policies training

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