Out of the laboratory and into the classroom: the future of artificial intelligence in education
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Abstract
Like previous educational technologies, artificial intelligence in education (AIEd) threatens to disrupt the status quo, with proponents highlighting the potential for efficiency and democratization, and skeptics warning of industrialization and alienation. However, unlike frequently discussed applications of AI in autonomous vehicles, military and cybersecurity concerns, and healthcare, AI's impacts on education policy and practice have not yet captured the public's attention. This paper, therefore, evaluates the status of AIEd, with special attention to intelligent tutoring systems and anthropomorphized artificial educational agents. I discuss AIEd's purported capacities, including the abilities to simulate teachers, provide robust student differentiation, and even foster socio-emotional engagement. Next, to situate developmental pathways for AIEd going forward, I contrast sociotechnical possibilities and risks through two idealized futures. Finally, I consider a recent proposal to use peer review as a gatekeeping strategy to prevent harmful research. This proposal serves as a jumping off point for recommendations to AIEd stakeholders towards improving their engagement with socially responsible research and implementation of AI in educational systems.
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Cited by
Sorted by citations
Role of AI chatbots in education: systematic literature review
Labadze, L., Grigolia, M., & Machaidze, L. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00426-1 · Cited by 1,043 · see citing works
Schiff, D. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00270-2 · Cited by 489 · see citing works
“Notably, these issues are becoming increasingly relevant as AIED applications now reach millions of students (Hao 2019) and online and remote learning have become necessary for educational institutions and governments alike (Ali 2020), opening new inroads for implementing and studying AIED (Schiff, 2021).”
BackgroundInfluentialSelf-citationLee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100221 · Cited by 402 · see citing works
Wang, X., Li, L., Tan, S. C., Yang, L., & Lei, J. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107798 · Cited by 336 · see citing works
Will generative AI replace teachers in higher education? A study of teacher and student perceptions
Chan, C. K. Y., & Tsi, L. H. Y. (2024) · Studies in Educational Evaluation · doi:10.1016/j.stueduc.2024.101395 · Cited by 387 · see citing works
Harnessing AI to Foster Equity in Education: Opportunities, Challenges, and Emerging Strategies
Roshanaei, M., Olivares, H., & Lopez, R. R. (2023) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2023.154009 · Cited by 147 · see citing works
Exploring the ethical considerations of using Chat GPT in university education
Huallpa, E. A. (2023) · Periodicals of Engineering and Natural Sciences (PEN) · doi:10.21533/pen.v11i4.3770 · Cited by 145 · see citing works
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
AI and academic integrity: Exploring student perceptions and implications for higher education
Lund, B. D., Lee, T. H., Mannuru, N. R., & Arutla, N. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-025-09613-3 · Cited by 96 · see citing works
Park, H., & Ahn, D. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642785 · Cited by 84 · see citing works
Bewersdorff, A., Seßler, K., Baur, A., Kasneci, E., & Nerdel, C. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100177 · Cited by 76 · see citing works
Qian, K., Liu, S., Li, T., Raković, M., Li, X., Guan, R., Molenaar, I., Nawaz, S., Swiecki, Z., Yan, L., et al. (2026) · Computers & Education · doi:10.1016/j.compedu.2025.105448 · Cited by 39 · see citing works
“…to efficiently incorporate task context, SRL concepts, and specific student requirements without extensive pre-programming, thus facilitating the scalability of personalised SRL scaffolding systems (Kikalishvili, 2023; Fariani et al., 2023; Schiff, 2021; Chang et al., 2023; Liu et al., 2024a; ?”
AI in Education: Shaping the Future of Teaching and Learning
Takona, J. (2024) · International Journal of Current Educational Studies · doi:10.46328/ijces.121 · Cited by 24 · see citing works
Al-Smadi, O. A., Rashid, R. A., Saad, H., Zrekat, Y. H., Kamal, S. S. L. A., & Uktamovich, G. I. (2024) · Journal of Language Teaching and Research · doi:10.17507/jltr.1506.09 · Cited by 22 · see citing works
Educational Collaboration : Teachers and Artificial Intelligence
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Yao, M., Wei, Y., & Liu, H. (2026) · Ethics & Behavior · doi:10.1080/10508422.2025.2549310 · Cited by 6 · see citing works
Ayanda, A. O. (2026) · International Journal of Applied Resilience and Sustainability · doi:10.70593/deepsci.0203003
“Reinforcement learning, unlike supervised learning, learns by the successive engagement with an environment, and by successfully performing and instead, is rewarded [26-28].”
- Authors
- Schiff, D.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Schiff, Daniel
- ORCID
- Schiff, Daniel
- Venue
- AI & SOCIETY
- Published
- 2020-08-09
- Volume
- 36
- Issue
- 1
- Pages
- 331-348
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- PMC
- PMC7415331
- OpenAlex
- W3048335295
- Cited by
- 334
- References
- 124
- Citations by year
- 2026: 48 · 2025: 91 · 2024: 95 · 2023: 55 · 2022: 32 · 2021: 11 · 2020: 2
- FWCI
- 23.51
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- AI & Society (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- SocArXiv (OSF Preprints) (submittedVersion, cc-by-nc-nd)
- SocArXiv (OSF Preprints) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence in education, intelligent tutoring systems, student differentiation, risks of AI in education, democratization of education, industrialization of education
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