The use of AI in different educational contexts

The gap

Evidence profile

Stated in the future work and recommendations and cells future research and cells research gap sections of the source papers, classified as general, spanning 7 journals.

Supporting evidence — 8 representative gaps

• Rethinking Science Education for Innovation: Challenges, Opportunities, and Future Directions (2026) · International Journal of innovative inventions in Social Science and Humanities · doi

  Esenowo, Aniebiet Jackson¹, Iwejor, Chimaroke Orguchialu², Okechukwu, Promise Ibuchim³, Tortor Blessing4 1,2,3,4Department of Science Education, Faculty of Education, Ignatius Ajuru University of Education, Rumuolumeni, Port Harcourt, Rivers State, Nigeria ARTICLE DETAILS Published On: 23 April 2026 ABSTRACT Science education stands at a critical juncture in the twenty-first century. As societies grapple with pressing challenges—climate change, public health crises, artificial intelligence transformation, and energy security—the capacity to produce scientifically literate citizens and innovative researchers has never been more urgent. Yet science education remains largely anchored to twentieth-century paradigms: content-heavy curricula, high-stakes assessments that reward recall over understanding, and pedagogies that position students as passive recipients rather than active inquirers. This conceptual paper argues that fundamental rethinking is required to align science education with the demands of innovation economies and democratic participation in technological societies. It critically examines three interrelated challenges: the persistence of what Michael Von Maltitz terms "broken proxies" in assessment, the equity gaps that pervade access to authentic scientific practice, and the disruptive potential—and peril—of artificial intelligence in science learning. The paper then explores emerging opportunities: the mainstreaming of active learning pedagogies, the integration of data science and computational thinking, the promise of phenomena-based and project-based instruction, and the potential for AI-augmented personalized learning when guided by robust pedagogical frameworks. Finally, it proposes future directions organized around four pillars: reimagining assessment systems, investing in teacher capacity and professional formation, embedding authentic research and innovation in curricula, and constructing ethical governance frameworks for AI in education. The paper concludes that the transformation of science education requires not piecemeal innovation but systemic reconceptualization—one that places inquiry, equity, and human flourishing at its centre. KEYWORDS: Artificial Intelligence, Assessment, Educational Equity, Science Education, Stem Education, Teacher Professional Development. Available on: https://ijiissh.com/ INTRODUCTION Education is universally acknowledged as the foundation upon which societies build economic prosperity, social stability, and technological advancement. Beyond formal schooling, education serves as a dynamic process that nurtures human potential and equips individuals with the knowledge and competencies needed to transform their environment.

  generalstated in future workevidence 5/5
  Keywords: education science century societies artificial intelligence innovation assessment equity potential learning promise twenty first challenges
• 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

  through a systematic review of AI integration in STEM education across African higher education institutions. They identify strategic uses of AI research, content generation, and for administration. Their findings show that tools like ChatGPT are widely used for paraphrasing, grammar checking, and self-directed learning. They advocate for increased AI literacy and targeted research to close existing gaps and ensure that AI adoption supports Africa’s SDGs. teaching, Finally, Abisoye (2023) contributes to this growing body of knowledge by examining the intersection of AI, education, and African development. His work emphasizes the ethical and policy dimensions of AI integration, urging African institutions to develop frameworks that ensure the responsible and inclusive use of emerging technologies in education. Together, these scholars present a unified vision: that Africa’s educational and communicative transformation must be rooted in cultural relevance, technological innovation, and social equity. They call for reimagined systems that not only prepare learners for global competitiveness but also empower them to lead meaningful change within their own communities. Collectively, for an educational transformation that is culturally relevant, scholars advocate these 7 / 12 Aboderin & Pietersen / Philosophical and critical perspectives of integrating AI into STEM curriculum design technologically innovative, and socially equitable. Their insights offer a roadmap for integrating AI in ways that empower align with Africa’s developmental priorities. learners and IMPLICATIONS FOR THE STUDY These implications are grounded in the AI-TPACK and UTAUT frameworks. These models emphasize the integration of technology with PK and CK. They also highlight behavioral factors that influence technology adoption. The study highlights the importance of collaborative efforts among educators, policymakers, and institutional leaders. These efforts are crucial to ensure that AI integration in STEM education is both pedagogically sound and contextually relevant across African education systems. Educators need ongoing professional development.

  generalstated in future workevidence 5/5
  Keywords: education integration african stem ensure africa across institutions advocate adoption development frameworks scholars educational transformation
• Assessment of Artificial Intelligence Awareness Level and Utilization Strategies Among Mathematics Students in Tertiary Institutions in Imo State (2026) · International Journal of Advanced Academic Research · doi

  https://doi.org/10.1186/s12909-025-07223-6⁠ artificial on Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments. Internet and Higher Education, 27, 1–13.https://doi.org/10.1016/j.iheduc.2015.04.007⁠ Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Assessment & Evaluation in Higher Education, 49(1), 1–14.https://doi.org/10.1080/02602938.2023.2180890⁠ Dergunova, Y., Aubakirova, R. Z., Yelmuratova, B. Z., Gulmira, T. M., Yuzikovna, P. N., & Antikeyeva, S. (2022). Artificial levels of students. International Journal of Emerging Technologies in Learning, 17(18), 26–37. https://doi.org/10.3991/ijet.v17i18.32195⁠ intelligence awareness Dillenbourg, P. (1999). What do you mean by collaborative learning? In P. Dillenbourg (Ed.), (pp. 1–19). Collaborative-learning: Cognitive and computational approaches Elsevier.https://doi.org/10.1016/B978-008043073-4/50001-4⁠ Dwivedi, Y. K., Kshetri, N., Hughes, L., et al. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative AI for research. International Journal of Information Management, 71,(10)26- 42.https://doi.org/10.1016/j.ijinfomgt.2023.102642⁠ El-Shara, I. A., Saeed, A. S., & Arouri, Y. M. (2025). University students’ awareness and attitudes toward the use of artificial intelligence applications in learning.

  generalstated in future workevidence 5/5
  Keywords: https learning artificial higher education international academic cotton chatgpt students journal intelligence awareness dillenbourg collaborative
• Awareness on Artificial Intelligence Tools among Secondary School Students (2026) · International Journal of Science and Research (IJSR) · doi

  • Schools should programs, workshops, and training sessions on the educational use of AI tools for Secondary school students. awareness organize • Teachers should guide students in the ethical and responsible use of AI technologies for learning purposes. Volume 15 Issue 5, May 2026 Fully Refereed | Open Access | Double Blind Peer Reviewed Journal www.ijsr.net Paper ID: SR26526140725DOI: https://dx.doi.org/10.21275/SR265261407251714 International Journal of Science and Research (IJSR) ISSN: 2319-7064 Impact Factor 2025: 7.089 https://doi.org/10.63960/sijmds-2024-119Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://doi.org/10.58863/20.500.12424/4276068 Imamguluyev, R, Hasanova, P, Imanova, T, Mammadova, A., Hajizada, S, & Samadova, Z. (2024). Ai-powered educational tools: Transforming learning in the digital era.

  generalstated in recommendationsevidence 5/5
  Keywords: learning journal https educational tools students ijsr international schools programs workshops training sessions secondary school
• IMPACT OF HANDS-ON TEACHING STRATEGY ENHANCED WITH ARTIFICIAL INTELLIGENCE ON INTEREST AND PERFORMANCE OF BIOLOGY EDUCATION STUDENTS IN FEDERAL COLLEGES OF EDUCATION IN NORTH EASTERN, NIGERIA (2026) · International Journal of Library Science and Educational Research · doi

  i. Adoption of AI-supported teaching strategies in Colleges of Education ii. Integration of digital literacy training into teacher education 39 IJLSER: E-ISSN 3027-1827 P-ISSN 3026-880X International Journal of Library Science & Education Research Published by Cambridge Research and Publications Vol. 12 No. 8 June, 2026. iii. Provision of technological infrastructure for effective learning iv. Further research on long-term impacts of AI in education REFERENCES Abdullahi, M., & Garba, S. (2023). Effects of hands-on learning strategies on students’ academic achievement in Biology. Journal of Science Education in Africa, 15(2), 45–58. Aina, J. K., & Adedoja, G. O. (2022). Innovative pedagogical approaches and students' academic achievement in science education. Journal of Science Education Research, 18(2), 45–58. Aljohani, N. R. (2020). Artificial intelligence in education: Current trends and future prospects. International Journal of Emerging Technologies in Learning, 15(10), 4–17. Bond, M., Buntins, K., Bedenlier, S., Zawacki‐Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17(1), 1–30. https://doi.org/10.1186/s41239-019-0176-8 Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97–140. https://doi.org/10.1080/10888691.2018.1537791 European Commission. (2022). The Digital Competence Framework for Citizens (DigComp 2.2). Publications Office of the European Union. Eze, C. U., & Okoli, J. N. (2021). Teaching methods and students’ academic performance in Biology in Nigerian Colleges of Education. Nigerian Journal of Educational Research, 19(1), 88–102. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410– 8415. https://doi.org/10.1073/pnas.1319030111 Hatlevik, O. E., Guðmundsdóttir, G. B., & Loi, M. (2015). Digital diversity among students: A multilevel analysis of digital competence. Computers & Education, 81, 345–353. https://doi.org/10.1016/j.compedu.2014.10.019 Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127. https://doi.org/10.1207/s15326985ep4102_4 Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Holmes, W., & Tuomi, I. (2022).

  generalstated in recommendationsevidence 5/5
  Keywords: education science journal learning educational https digital students teaching international academic strategies colleges issn publications
• Examining Biology Teachers’ Cognitive Structures Regarding Artificial Intelligence Using a Word Association Test (2026) · Journal of Education in Science Environment and Health · doi

  Artificial intelligence technology should be integrated into educational settings based on data obtained from educational psychology, educational sociology, and contemporary teaching models. An AI system that lacks foundational educational principles may cause more harm than benefit to students; therefore, it should be embedded in ways that align with the field of educational sciences. It is unrealistic to expect humans and educators to detach themselves from technology; however, technology should not overshadow the fundamental aims of education and must be used cautiously. Artificial intelligence should not replace the teacher, who serves as a guide in the learning process.

  generalstated in recommendationsevidence 5/5
  Keywords: educational technology artificial intelligence integrated settings based obtained psychology sociology contemporary teaching models system lacks
• <b>Impacto de la Inteligencia Artificial como Estrategia de Apoyo en la Enseñanza de la Química: Innovación Pedagógica y Mejora del Aprendizaje </b> (2026) · Prometeo Conocimiento Científico · doi

  The study suggests that future research should focus on addressing the challenges and risks associated with AI implementation in education. - Further investigation is needed to explore the potential of AI to enhance chemistry education, informing the development of innovative pedagogical approaches. - The study highlights the importance of interdisciplinary collaboration, combining insights from education, chemistry, and computer science to enhance learning outcomes.

  generalstated in cells future researchevidence 5/5
  Keywords: study suggests future research focus addressing challenges risks
• Artificial Intelligence in Physics Education (2015–2025): Systematic Review of Trends, Applications, and Challenges (2026) · Journal of Education in Science Environment and Health · doi

  There is a lack of comprehensive research on the pedagogical impact of AI-supported instruction in physics education. - There is a need for more studies on the effective implementation of AI-supported instruction in physics education. - The study identifies a gap in the literature regarding the long-term effects of AI-supported instruction on student learning outcomes.

  generalstated in cells research gapevidence 5/5
  Keywords: there lack comprehensive research pedagogical impact ai-supported instruction

Questions about this gap