Human-in-the-loop in artificial intelligence in education: A review and entity-relationship (ER) analysis
Open accessAbstract
Human-in-the-loop research predominantly examines the interaction types and effects. A more structural and pragmatic exploration of humans and Artificial Intelligence or AI is lacking in the artificial intelligence in educational literature. In this systematic review we follow the Entity-Relationship (ER) framework to identify trends in the entities, relationships, and attributes of human-in-the-loop AI in education. An overview of N = 28 reviewed studies followed by their ER characteristics are summarized and analyzed. The dominant number of two or three-entity studies, one-sided relationships, little attributes, and many to many cardinalities may signal a lack of deliberation on beings that come to interact and influence human-in-the-loop and AI in education. The contribution of this work is identifying the implications of human-in-the-loop and AI from a more formal ER perspective and acknowledging the many possibilities for placement of humans in the loop with the AI, system, and environment of interest.
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AI Agents are typically designed as single-entity systems that perform goal-directed tasks by utilizing external tools, applying sequential reasoning, and integrating real-time information to complete well-defined functions.
“AI Agents are typically designed as single-entity systems that perform goal-directed tasks by utilizing external tools, applying sequential reasoning, and integrating real-time information to complete well-defined functions [25,43].”
From AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges (2026) · cites this paper as [43]
AI Agents are typically single-entity systems that perform goal-directed tasks using external tools, sequential reasoning, and real-time information.
“AI Agents are typically designed as single-entity systems that perform goal-directed tasks by utilizing external tools, applying sequential reasoning, and integrating real-time information to complete well-defined functions [24], [45].”
From AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges (2025) · cites this paper as [45]
Cites
A survey of human-in-the-loop for machine learning
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.014 · Cited by 800 · see citing works
Bhutoria, A. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100068 · Cited by 677 · see citing works
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AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103599 · Cited by 244 · see citing works
“AI Agents are typically designed as single-entity systems that perform goal-directed tasks by utilizing external tools, applying sequential reasoning, and integrating real-time information to complete well-defined functions [25,43].”
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025) · SuperIntelligence - Robotics - Safety & Alignment · doi:10.70777/si.v2i3.15161 · Cited by 186 · see citing works
- Authors
- Memarian, B., & Doleck, T.
- Affiliations
- Simon Fraser University (CA)
- Corresponding author
- Memarian, Bahar
- Venue
- Computers in Human Behavior: Artificial Humans
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 2
- Issue
- 1
- Pages
- 100053
- ISSN
- 2949-8821 (ISSN-L)
- OpenAlex
- W4391723017
- Cited by
- 73
- References
- 2
- Citations by year
- 2026: 55 · 2025: 16 · 2024: 2
- FWCI
- 27.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers in Human Behavior Artificial Humans (publishedVersion, cc-by-nc-nd)
- Funders
- Simon Fraser University
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- human-in-the-loop, artificial intelligence in education, human-AI interaction, entity-relationship model, human-AI systems, interaction types, interaction effects
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