Although its potential is widely acknowledged, further investigation is needed into K-12 teachers’ attitudes and perceptions towards integrating AI applications into educational practice, as well as t

The gap

Consensus across the literature

Supporting evidence — 7 representative gaps

• 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) · 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 M P A Thesis A.B.U Zaria (unpublished) 9.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. E., & Famou

  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) · 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.

  Keywords: genai adoption education learning usefulness students researchers context technology genaied unique implications similar possible challenges
• Philosophical and critical perspectives of integrating AI into STEM curriculum design: Opportunities and challenges in African educational contexts (2026) · 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. technological training, teacher that and CONCLUSION it learning, can personalize This paper explored how AI can be integrated into STEM curriculum design in Africa. The analysis was both critical and context-sensitive. AI offers many benefits: improve assessments, and support inclusive education. The study was guided by the AI-TPACK and UTAUT frameworks. These helped explain how AI tools can align with teaching goals and user behavior. However, successful just technology. It integration requires more than demands strong infrastructure, comprehensive teacher training, and ethical planning. Philosophy plays a key role in guiding this integration. Paulo Freire’s ideas remind us that education should empower learners. African philosophies like ubuntu emphasize community, fairness, and shared growth. These values must inform the design and use of AI tools. A framework-based approach can help build AI-enhanced curricula that are both ethical and relevant. Future research should focus on real-world applications, long-term impact, and equitable access across diverse African educational settings. Author contributions: OSA & DP: conceptualizing, literature review, data analysis, results, writing – original draft, writing – review & editing. Both authors have read and agreed to the published version of the manuscript. Funding: No funding source is reported for this study. Acknowledgments: The authors would like to thank all researchers whose works have contributed to the development of this study and the academic institutions and journal publishers that provided access to relevant literature and data. The authors would also like to thank the colleagues and mentors who offered valuable insights during the conceptualization and w

  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) · 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.

  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) · doi

  7.1 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. the sample

  Keywords: adoption faculty future recommendations educational following develop trust construct programmes intent development policy based policymakers
• Exploring K-12 teachers’ attitudes and perceptions towards the use of AI applications in the teaching process (2026) · doi

  Although its potential is widely acknowledged, further investigation is needed into K-12 teachers’ attitudes and perceptions towards integrating AI applications into educational practice, as well as the factors that shape these attitudes and influence their intention to adopt such tools.

  Keywords: attitudes potential widely acknowledged further investigation needed teachers perceptions towards integrating applications educational practice well
• Teaching practices and organisational aspects associated with the use of ICT (2024) · doi

  The results indicate that the presence of ICT in the classroom is associated with self-efficacy in teaching and the cognitive activation of students and with the organisational aspects of the school, which are scarcely addressed by the existing literature on this topic of interest, such as school climate, educational innovation and cooperation among teachers.

  Keywords: school indicate presence classroom associated self efficacy teaching cognitive activation students organisational aspects scarcely addressed