Cited by 284Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

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Human-centred learning analytics and AI in education: A systematic literature review

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Alfredo, Riordan, Echeverria, Vanessa, Jin, Yueqiao, Yan, Lixiang, Swiecki, Zachari, Gašević, Dragan, Martinez-Maldonado, Roberto·2024·Computers and Education: Artificial Intelligence

DOI 10.1016/j.caeai.2024.100215

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Abstract

The rapid expansion of Learning Analytics (LA) and Artificial Intelligence in Education (AIED) offers new scalable, data-intensive systems but raises concerns about data privacy and agency. Excluding stakeholders—like students and teachers—from the design process can potentially lead to mistrust and inadequately aligned tools. Despite a shift towards human-centred design in recent LA and AIED research, there remain gaps in our understanding of the importance of human control, safety, reliability, and trustworthiness in the design and implementation of these systems. We conducted a systematic literature review to explore these concerns and gaps. We analysed 108 papers to provide insights about i) the current state of human-centred LA/AIED research; ii) the extent to which educational stakeholders have contributed to the design process of human-centred LA/AIED systems; iii) the current balance between human control and computer automation of such systems; and iv) the extent to which safety, reliability and trustworthiness have been considered in the literature. Results indicate some consideration of human control in LA/AIED system design, but limited end-user involvement in actual design. Based on these findings, we recommend: 1) carefully balancing stakeholders' involvement in designing and deploying LA/AIED systems throughout all design phases 2) actively involving target end-users, especially students, to delineate the balance between human control and automation, and 3) exploring safety, reliability, and trustworthiness as principles in future human-centred LA/AIED systems.

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Authors: Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R.
Affiliations: Monash University (AU)
Corresponding author: Alfredo, Riordan
ORCID: Alfredo, Riordan
Jin, Yueqiao
Yan, Lixiang
Swiecki, Zachari
Gašević, Dragan
Venue: Computers and Education: Artificial Intelligence
Publisher: Elsevier BV
Published: 2024-06
Volume: 6
Pages: 100215
ISSN: 2666-920X (ISSN-L)
DOI: 10.1016/j.caeai.2024.100215
OpenAlex: W4392769397
Cited by: 281
References: 211
Citations by year: 2026: 165 · 2025: 93 · 2024: 22
FWCI: 21.16
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc/4.0/ (vor)
Locations: Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc)
arXiv (Cornell University) (submittedVersion, cc-by)
Monash University Research Portal (Monash University) (submittedVersion, cc-by-nc)
HAL (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
arXiv (Cornell University) (cc-by)
Funders: Australian Research Council
Jacobs Foundation
Topics: Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Social Sciences › Education › Artificial Intelligence in Education
Keywords: learning analytics, artificial intelligence in education, human-centered design, stakeholder engagement, human control, automation, data privacy, agency, trustworthiness, safety and reliability, end-user involvement

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