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Human-centred learning analytics and AI in education: A systematic literature review
Open accessAbstract
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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“We note that scaffolding students with LLM-generated content that contains hallucination may break the safety, reliability, and trustworthiness requirements of AI-powered learning technologies (Alfredo et al., 2024).”
From Towards reliable generative AI-driven scaffolding: Reducing hallucinations and enhancing quality in self-regulated learning support (2026) · cites this paper as Alfredo et al., 2024
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Metadata
- Authors
- Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R.
- Affiliations
- Monash University (AU)
- Corresponding author
- Alfredo, Riordan
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 6
- Pages
- 100215
- ISSN
- 2666-920X (ISSN-L)
- 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
- 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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