AI IN EDUCATION RESEARCH
ANNOTATED BIBLIOGRAPHY
AIED research, generative AI, ethics, and education policy
1
Artificial Intelligence in Education
Annotated Bibliography
AI applications, research methods, generative AI, ethical design, and education policy
RESEARCH THEME
These works examine artificial intelligence in education across five connected dimensions: the research
landscape, educational applications, large language models, ethical design, and policy guidance. The
combination provides both scholarly evidence and practical context for studying how AI is introduced
into teaching, learning, and educational institutions.
FIELD MAP
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A
systematic literature review. Expert Systems with Applications, 252, 124167.
https://doi.org/10.1016/j.eswa.2024.124167
ANNOTATION
Wang and colleagues analyze the research landscape of artificial intelligence in education through a
bibliometric study of 2,223 articles followed by a content analysis of 125 selected empirical studies.
They identify major application areas such as adaptive learning and personalized tutoring, intelligent
assessment and management, and profiling and prediction, while also examining research methods
and theoretical foundations. The article is especially useful because it gives a current map of the field
rather than focusing on one educational technology. For a research project, it can help identify major
themes, common methods, and underexplored questions before narrower studies are selected.
AIED OVERVIEW
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
ANNOTATION
Chen, Chen, and Lin review artificial intelligence applications in administration, instruction, and
learning and organize the field around educational data, algorithms, and system design. The article
surveys how AI can support areas such as teaching, assessment, and learning management while also
identifying technical and educational challenges. It is useful for establishing the scope of artificial
intelligence in education before discussing more specialized technologies. As an open-access IEEE
Access article, it also provides a readable scholarly bridge between computer science research and
education-focused questions.
AI IN EDUCATION RESEARCH
ANNOTATED BIBLIOGRAPHY
AIED research, generative AI, ethics, and education policy
2
GENERATIVE AI
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G.,
Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J.,
Poquet, O., Sailer, M., Schmidt, A., Seidel, T., ... Kasneci, G. (2023). ChatGPT for good? On
opportunities and challenges of large language models for education. Learning and Individual
Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
ANNOTATION
Kasneci and colleagues examine the educational possibilities and limitations of large language
models such as ChatGPT from both student and teacher perspectives. They discuss uses in content
creation, interaction, engagement, and personalized learning while emphasizing the need for fact
checking, human oversight, critical thinking, and awareness of model limitations. The article is
valuable because it addresses a major shift in AI-in-education research brought about by generative
systems, rather than treating AI only as older intelligent tutoring software. It is particularly useful for
current research on classroom adoption, academic work, and responsible use of generative AI.
ETHICAL DESIGN
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C.,
Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in
education: Towards a community-wide framework. International Journal of Artificial Intelligence in
Education, 32(3), 504-526. https://doi.org/10.1007/s40593-021-00239-1
ANNOTATION
Holmes and colleagues examine ethical issues specific to artificial intelligence in education, including
fairness, accountability, transparency, bias, autonomy, agency, privacy, inclusion, and unintended
consequences. The paper draws on responses from 17 researchers within the AIED community after
surveying 60 invited researchers, then uses those contributions to identify ethical questions that
require broader community attention. This source is useful because it shows that educational AI
raises questions not only about technical accuracy but also about pedagogy, teacher roles, student
agency, and access. It provides a strong framework for analyzing responsible design and deployment
in schools and universities.
EDUCATION POLICY
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO.
ANNOTATION
Miao and Holmes present UNESCO's guidance for responding to generative AI in education and
research through a human-centered approach. The guidance addresses privacy, age-appropriate use,
regulation, ethical validation, pedagogical design, and the development of institutional policy, while
also discussing possible uses of generative AI in teaching, learning, curriculum design, and research.
It is useful for an academic bibliography because it connects classroom questions with national and
institutional governance rather than treating adoption as only a technology choice. As an
international policy document, it also provides a useful contrast with peer-reviewed studies that focus
more narrowly on research findings or system design.
