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The rapid advancement of artificial intelligence (AI) is increasingly being integrated into early childhood experiences, from AI-powered educational apps (e.g., quickdraw, zhorai) to robotic learning companions (i.e., Blue-Bot). This raises questions about how this is transforming childhood experiences (Almasri, 2024; Durrani et al., 2024; Su & Yang, 2022). AI has the potential to enhance learning and teaching (e.g., enhance problem-solving skills, improve subject understanding and motivation) however, there is a lack of educator understanding, experience and trust in AI (Durrani et al., 2024). Furthermore, there is insufficient curriculum design addressing what, why, when, and how young learners should begin learning about and from AI (Yang, 2022).
Educators play a crucial role in shaping children’s early experiences with AI therefore, their perspectives on AI benefits, risks, and ethical implications are paramount. Researchers must also consider the practicality and usefulness of AI for learning and teaching from the educator’s perspective (Condor, 2020). Yet, there is limited research on knowledge, perceptions, and needs of educators and their own AI literacy (i.e., the ability to critically understand and reflect on AI output; Kanders et al., 2024; Lee et al., 2024).
This project aims to investigate and explore the perspectives of educators on AI in early childhood development in a Northern Ireland (NI) context. By understanding their views, the research will provide insights into the perceived advantages and potential challenges of AI in early education settings. The findings will contribute to the responsible integration of AI in early childhood settings, ensuring AI aligns with developmental best practices, complimenting human interaction and investigate strategies for effectively introducing AI to young learners. Further, this project will help contribute to the achievement of the UN Sustainable Development Goals (Target 4) by promoting lifelong learning opportunities for all and could highlight areas of reasonable adjustment to the NI curriculum.
Objectives of the research:
The objectives are:
Methods to be used:
This research will adopt a mixed-methods approach, combining qualitative interviews and quantitative survey methods to gain a comprehensive understanding of educators’ perspectives on AI in early childhood education within the Northern Ireland (NI) context. The student will engage in cutting-edge research examining AI’s role in early learning. This project is ideal for those interested in developmental psychology, education, human-computer interaction, and ethical AI design and implementation.
Skills required of applicant:
Please note, the successful candidate will be required to obtain AccessNI clearance prior to registration due to the nature of the project.
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.
Appointment will be made on merit.
This opportunity is open to UK/ROI applicants only.
MRes studentships will be available to top ranked candidates to cover tuition fees and a Research Training Support Grant of £900. All applicants will be considered automatically for an MRes studentship. Applicants who do not receive a studentship but meet admission requirements may be offered admission on a self-funded basis.
Applicants who already hold an MRes or a doctoral degree or who have been registered on a programme of research leading to the award of an MRes or doctoral degree are NOT eligible to apply for an award. Applicants who hold or who are registered on a taught Master’s degree are eligible to apply.
Almasri, F. (2024). Exploring the impact of artificial intelligence in teaching and learning of science: A systematic review of empirical research. Research in Science Education, 54(5), 977-997.
Condor, A. (2020). Exploring automatic short answer grading as a tool to assist in human rating. In Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part II 21 (pp. 74-79). Springer International Publishing.
Durrani, R., Iqbal, A., & Akram, H. (2024). Artificial Intelligence (AI) in Early Childhood Education, Exploring Challenges, Opportunities and Future Directions: A Scoping Review. Qlantic Journal of Social Sciences, 5(2), 411-423.
Kanders, K., Stupple‐Harris, L., Smith, L., & Gibson, J. L. (2024). Perspectives on the impact of generative AI on early‐childhood development and education. Infant and Child Development, 33(4), e2514.
Lee, K. W., Mills, K., Ruiz, P., Coenraad, M., Fusco, J., Roschelle, J., & Weisgrau, J. (2024). AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology.
Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence, 3, 100049.
Yang, W. (2022). Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation. Computers and Education: Artificial Intelligence, 3, 100061.
Submission deadline
Monday 16 June 2025
05:00PM
Interview Date
TBC
Preferred student start date
15th September 2025
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