Open research questions in Artificial Intelligence in Education

What the literature leaves open

• • Organizations are to introduce organized AI literacy education to enhance the technological and pedagogical skills of teachers. • Educational policymakers must come up with ethical AI governance models to promote transparency, equity, and privacy of data. • Institutions of higher learning and schools ought to encourage interactive human-AI instructional designs other than full automation in terms of teaching. 8. LIMITATIONS • The research involves the primary use of secondary literature and does not involve any primary empirical data. • The high rates of technological changes might restrict the generalizability of the current findings of the AI implementation in the long term 9. CONCLUSION The author concludes that Artificial Intelligence is a revolution and supplementary factor in contemporary learning. Instead of substituting teachers, AI will complement instructional performance by helping to support personal learning, automated testing, data-driven instruction, and interactive learning. The new and dynamic role of teachers as content deliverers will be altered to facilitators, mentors, and strategic decision-makers in the AI-enhanced classroom. Nevertheless, effective AIs implementation needs ethical precautions, institutional preparation, professional training, and moderated administrative systems. Collaborative human-AI pedagogy is the future of AI in education in which technology supplements human knowledge and does not limit teacher autonomy and professional judgment. REFERENCES. Jiménez, AF (2024). Integration of AI helping teachers in traditional teaching roles. European Public &Social Innovation Review, epsir.net, https://epsir.net/index.php/epsir/article/view/664. Qureshi, I (2025). The impact of AI on teacher roles: Towards a collaborative human-AI pedagogy. AI Edify Journal, researchcorridor.org, https://researchcorridor.org/index.php/aiej/article/view/243. Taufikin, MSI, Azifah, N, Nikmah, F, &... (2024). The impact of AI on teacher roles and pedagogy in the 21st century classroom. 2024 International..., ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/10617236/. Zhang, J, & Zhang, Z (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and. digital literacy. Journal of Computer Assisted Learning, Wiley Online Library, https://doi.org/10.1111/jcal.12988 Ivanashko, O, Kozak, A, Knysh, T, &... (2024). The role of artificial intelligence in shaping the future of education: Opportunities and challenges. Futurity Education, futurity-education.com, https://futurity-education.com/index.php/fed/article/view/262. Airaj, M (2024). Ethical artificial intelligence for teaching-learning in higher education. Education and Information Technologies, Springer, https://doi.org/10.1007/s10639-024-12545-x. Adhikari, DP, & Pandey, GP (2025). Integrating AI in higher education: transforming teachers’ roles in boosting student agency. Educational Technology Quarterly, acnsci.org, https://acnsci.org/journal/index.php/etq/article/view/943. Yadav, S (2025). Leveraging AI to enhance teaching and learning in education: The role of artificial intelligence igi-global.com, in modernizing classroom practices. Optimizing research techniques and learning strategies..., https://www.igi-global.com/chapter/leveraging-ai-to-enhance-teaching-and-learning-in-education/370742 Joseph, TS, Gowrie, S, Montalbano, MJ, &... (2025). The roles of artificial intelligence in teaching anatomy: a systematic review. Clinical..., Wiley Online Library, https://doi.org/10.1002/ca.24272. 60 Kiran Soni / Journal on Innovations in Teaching and Learning, Vol 5(1) 2026, 53–61.

  Reconceptualizing Teaching in The Era of Artificial Intelligence: Evidence from Contemporary Research · 2026 · DOI

• Teachers should select AI functions according to learning objectives, require verification, and assess traces of both the process and the product. 2. Schools should establish policies on AI use, data protection, equitable access, and continuing professional development. Journal of Authentic Research, August 2026 Vol. 5, No. 3 | 4941 Sukarma et al. Artificial Intelligence Integration ……… 3. Curriculum developers should integrate AI literacy, data literacy, computational thinking, engineering design, and ethics. 4. Researchers should employ longitudinal designs, transfer tasks, log analysis, and more diverse contexts. 5. Technology developers should provide transparency, teacher control, locallanguage support, and audit and appeal mechanisms. REFERENCES Aptyka, H., Großschedl, J., & Hartelt, T. (2025). Bugbear or surefire success? Secondary school students’ conceptual learning about evolution with ChatGPT. International Journal of Science Education. Advance online publication. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510 Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118 Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32, 444–452. https://doi.org/10.1007/s10956-023-10039-y Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148 Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8 Day, T. (2023). A preliminary investigation of fake peer-reviewed citations and references generated by ChatGPT. The Professional Geographer, 75(6), 1024– 1035. https://doi.org/10.1080/00330124.2023.2190373 Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846 Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A., & Prather, J. (2022). The robots are coming: Exploring the implications of OpenAI Codex on introductory programming. In Proceedings of the 24th Australasian Computing Education Conference (pp. 10–19). ACM.

  Integrasi Artificial Intelligence dalam Pembelajaran STEM: Praktik Pedagogis, Hasil Belajar, dan Tantangan · 2026 · DOI

• Future research may examine how the tripartite core and the five knowledge domains interact with this institutional layer, particularly where bureaucratic AI use shapes the conditions under which classroom AI use unfolds. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.

  Three inseparable facets and five new knowledge domains: An extended GenAI-TPACK proposal · 2026 · DOI

• From this perspective, Morocco, like most developing countries, has not yet established an AI competency framework to guide teachers in the judicious or problematic use of artificial intelligence (AI) in education.

  Teachers and AI: revolutionary educational tool or looming threat to the profession? · 2026 · 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.

  Examining Biology Teachers’ Cognitive Structures Regarding Artificial Intelligence Using a Word Association Test · 2026 · DOI

• In light of all this information, the following recommendations have been reached: This article is a systematic review of research on the use of artificial intelligence in environmental education. 17 articles were identified that focused on this topic. This demonstrates the emerging position of this topic in the relevant literature. Future studies should expand research on this topic. The majority of studies reviewed are theoretical, while research articles are mostly experimental in design. Future research should employ a wider variety of methods to strengthen sample selection, sample size, and the generalizability of findings. The correct and conscious use of artificial intelligence is considered a necessity. The integration of artificial intelligence into education is also an important and current issue. It is recommended that faculty members be supported in this area. This can increase both the use of artificial intelligence in education and the number of research on the subject. Simply using artificial intelligence to access accurate information and conscientiously address privacy will be insufficient. Artificial intelligence must be used in an environmentally friendly manner. Experts in the field should inform faculty members, principals, teachers, students—in short, all stakeholders in the educational process.

  A Systematic Review Study on the Use of Artificial Intelligence in Environmental Education · 2026 · DOI

• In light of the findings and the discussion presented, this study offers the following practical recom- mendations to enhance the effective integration of AI in science education, particularly in low-resource educational contexts such as Nigeria: 1. Invest in technological infrastructure: Policymakers and education stakeholders must prioritize equitable investment in AI-enabling infrastructure. This includes the provision of computers, tablets, reliable internet access, and electricity, particularly in underserved rural and peri-urban schools. Government-led interventions should ensure that all schools, regardless of location, have access to the baseline technologies required to adopt AI tools for science instruction. 2. Revise and enrich the science curriculum: Educational authorities should reform science curricula to explicitly incorporate AI-based learning strategies, including virtual labs, adaptive learning plat- forms, and data analysis tools. The integration of AI should be framed within local pedagogical goals, national development priorities, and global technological trends, preparing learners for the demands of the twenty-first-century workforce. 3. Implement continuous and context-specific teacher training: Professional development programs should move beyond one-off workshops and instead adopt continuous, contextually responsive models. Training should focus on both the technical and pedagogical dimensions of AI, equipping teachers with practical skills to embed AI tools into their daily instructional practices. Special emphasis should be placed on ethical AI use, equity in digital learning, and the application of AI in practical science activities. 4. Strengthen policy and institutional support mechanisms: For AI integration to be systemic and sus- tainable, education ministries and school leadership must establish clear policies and institutional frameworks that support teacher autonomy, innovation, and collaboration. This includes creating incentives for AI use, setting standards for technology adoption, and fostering communities of practice where educators can share best practices and resources. 5. Promote participatory and feedback-driven implementation: The success of AI integration depends on actively involving teachers in the design, implementation, and evaluation of AI-based programs. Feedback mechanisms—such as digital forums, needs assessments, and classroom trials—should be institutionalized to ensure that AI tools and training programs are adapted to local needs and realities. 6. Address digital equity and regional disparities: To close the digital divide, targeted interventions are necessary for schools in remote and underserved regions. National and international development agencies should collaborate to provide subsidies, grants, or public-private partnerships that support technology access in these settings. This will help bridge the gap between urban and rural schools and ensure inclusive access to AI-driven learning. Can. J. Sci. Math. Techn. Educ. (2026) 26:4 Page 21 of 23 4 By implementing these recommendations, stakeholders can address current limitations and pave the way for transformative, equitable, and sustainable integration of AI in science classrooms. This would not only enhance science learning outcomes but also foster teacher innovation and professional growth in line with global educational trends.

  Exploring the Influence of AI on the Professional Development of Science Teachers in STEM Education · 2026 · DOI

• Additionally, considering that the data for this study was collected in April and May 2025, it reveals that participants did not attend in-service training on the introduction of AI tools within the framework of the 2024-2025 action plan, that these trainings were insufficient, or that no trainings existed.

  TEACHERS’ OPINIONS ON ARTIFICIAL INTELLIGENCE TOOLS USED IN EDUCATION (THE CASE OF KIRŞEHIR) · 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.

  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

• Integrating AI into botany teaching allows for hands-on, data-driven learning through virtual plant identification, modeling ecological changes, and analyzing high-throughput phenotyping data. It transforms traditional field and laboratory work into interactive, student-centered learning experiences. To ensure successful implementation of AI in botany education, the following measures are recommended: 1. Provide digital infrastructure in educational institutions. 2. Organize teacher training programs on AI technologies. 3. Develop affordable AI educational tools. 4. Encourage blended learning approaches. 5. Ensure ethical use and data privacy. 6. Promote collaboration between educators, researchers, and technology developers. Conclusion. Artificial Intelligence has immense potential to transform botany education by making learning more interactive, personalized, and research-oriented. AI technologies such as intelligent tutoring systems, virtual laboratories, image recognition, and adaptive learning platforms significantly enhance both theoretical and practical understanding of botanical sciences. Although challenges related to infrastructure, training, and ethics remain, strategic implementation can maximize the educational benefits of AI. The integration of AI into botany teaching not only improves learning outcomes but also prepares students for future scientific and technological advancements. Therefore, educational institutions should adopt AI-driven approaches to modernize botanical education and promote innovative learning experiences.

  Role of Artificial Intelligence in Teaching Botany: Transforming Plant Science Education · 2026 · DOI

• Necessity to develop a system of AI-songs organized by curriculum topics with gradation by language levels (A1–C1) so teachers can easily select and integrate them…

  “Singing artificial intelligence” Suno in teaching Russian to Vietnamese students · 2026 · DOI

• Following the discussion, the main conclusion of the study denotes that further research is required on the use of AI-Gen during the teaching-learning process in several educational stages to prevent plagiarism and to obtain a comprehensive understanding of this technology as an educational resource.

  Generative artificial intelligence: Educational reflections from an analysis of scientific production · 2024 · DOI

• Based on the analysis of scientific research, it is concluded that there is insufficient coverage of the problems of teaching artificial intelligence technologies in high school computer science classes, in particular, there are no clear indications of the content, plan, sequence of studying artificial intelligence technologies, etc.

  Learning the basics of artificial intelligence in high school computer science classes · 2024 · DOI

• Although the potential of AI to replace teachers with all the seismic shift it created in the teaching-learning processes has sparked passionate debates, arguments over the potential influence of AI on school principals is scarce.

  Will Artificial Intelligence (AI) Make the School Principal Redundant? A Preliminary Discussion and Future Prospects · 2024 · DOI

• In 2018, most of the professionals across Kazakhstan received digital literacy training from the government, yet this is insufficient to equip teachers with the knowledge and skills to properly educate young learners in computer science or programming.

  DEVELOPMENT OF ALGORITHMIC AND PROGRAMMING THINKING AT PRIMARY SCHOOL IN STATE EDUCATIONAL PROGRAMS · 2023 · DOI

• Future research should focus on validating the framework in broader contexts and examining its long-term impact on educational quality and innovation.

  A Proposed Framework for Employing Artificial Intelligence Applications in the Educational Process to Improve the Quality of Educational Outcomes in Jordanian Public Schools in Light of Digital Transformation · 2026 · DOI

• The fact that the level of knowledge was determined by a question rather than by using a measurement tool can be considered a limitation of this study.

  Investigation of AI Anxiety and Some Personal Variables’ Effects on Teachers' Attitudes Towards AI · 2026 · DOI

• One can show that particular evidence for a claim or conclusion is insufficient without that meaning one believes the claim or conclusion itself is false.

  Review of Brave New Words: How AI Will Revolutionalize Education (and Why That's a Good Thing), by Salman Khan · 2026 · DOI

• However, in developing countries, notably Morocco, this topic is rarely addressed in the literature, and relevant initiatives are virtually nonexistent, to our knowledge.

  Integrating Artificial Intelligence into High-School Computer Science Curriculum: A Perspective Study in Morocco · 2025 · DOI

• Educational recommendation systems have not been adequately tested in real institutional platforms; most validation occurs in simulators or controlled environments, leaving a gap between laboratory performance and actual deployment effectiveness in live educational settings.

  Artificial Intelligence for resource recommendation in online education · 2024 · DOI

• Why AI tools create content that is false or misleading is not fully understood and reflects an underlying degree of uncertainty (Athaluri et al.

  Artificial intelligence and the <scp><i>Journal of Research in Science Teaching</i></scp> · 2024 · DOI

Most-cited papers in Artificial Intelligence in Education

• ChatGPT for Language Teaching and Learning · RELC Journal · 2023 · 863 citations
• Revolutionizing education with AI: Exploring the transformative potential of ChatGPT · Contemporary Educational Technology · 2023 · 714 citations
• State of the art and practice in <scp>AI</scp> in education · European Journal of Education · 2022 · 711 citations
• Algorithmic Bias in Education · International Journal of Artificial Intelligence in Education · 2021 · 515 citations
• Artificial Intelligence in Education: AIEd for Personalised Learning Pathways · The Electronic Journal of e-Learning · 2022 · 466 citations
• Exploring Teachers’ Perceptions of Artificial Intelligence as a Tool to Support their Practice in Estonian K-12 Education · International Journal of Artificial Intelligence in Education · 2021 · 319 citations
• A review of AI teaching and learning from 2000 to 2020 · Education and Information Technologies · 2022 · 305 citations
• Leading teachers' perspective on teacher-AI collaboration in education · Education and Information Technologies · 2023 · 278 citations
• A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms · Smart Learning Environments · 2023 · 273 citations
• Generative artificial intelligence empowers educational reform: current status, issues, and prospects · Frontiers in Education · 2023 · 263 citations

Most recent work

• Artificial intelligence in K-12 instruction: the role of teacher professional development · Smart Learning Environments · 2026
• Using Artificial Intelligence and the TEACH-SAN-TA Model for All Domains of Learning · Journal of Physical Education Recreation & Dance · 2026
• The Relationship Between Teachers’ Age, Gender and Their Will and Skill to Teach Artificial Intelligence · Technology Knowledge and Learning · 2026
• Matrix of artificial intelligence tools in the professional field of future computer scientists · Tambov University Review. Series: Humanities · 2026
• The pedagogical hard problem of generative AI: Socratic countermeasures · Journal of Moral Education · 2026
• Exploring the Influence of AI on the Professional Development of Science Teachers in STEM Education · Canadian Journal of Science Mathematics and Technology Education · 2026
• A Systematic Review Study on the Use of Artificial Intelligence in Environmental Education · Journal of Education in Science Environment and Health · 2026
• “Singing artificial intelligence” Suno in teaching Russian to Vietnamese students · Russian Language Studies · 2026
• Development of a Methodological System for Teaching Algebra using Python Library Capabilities · Bulletin of Science and Practice · 2026
• TRANSFORMATION OF THE PROGRAMMING TEACHER'S ROLE IN THE CONTEXT OF GENERATIVE AI AGENT INTEGRATION · Zenodo (CERN European Organization for Nuclear Research) · 2026

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