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

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

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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  • Scaffolding students with LLM-generated content that contains hallucination may break the safety, reliability, and trustworthiness requirements of AI-powered learning technologies.

    “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

    Section 4.4.2 (p. 14) reports that inaccurate information from educational AI systems can introduce unreliability, and Section 5.4 (p. 20) describes safety, reliability, and trustworthiness as interconnected. The paper does not specifically address LLM-generated scaffolding or hallucinations, so it supports the general concern but not that particular application. (LLM, conf=0.86)

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Research works printed but not linked: 4

  1. Fernandez-Nieto, G.M., Martinez-Maldonado, R., Echeverria, V., Kitto, K., Gaševi´ c, D., Buckingham Shum, S., 2024. Data storytelling editor: A teacher-centred tool for customising learning analytics dashboard narratives, in: LAK24: 14th International Learning Analytics and Knowledge Conference.
  2. Li, S., Gu, X., 2023. A Risk Framework for Human-centered Artificial Intelligence in Education. Educational Technology & Society 26, 187-202. URL: https://www.jstor.org/stable/48707976 .
  3. Moya, B., Eaton, S.E., Pethrick, H., Hayden, K.A., Brennan, R., Wiens, J., McDermott, B., Lesage, J., 2023. Academic integrity and artificial intelligence in higher education contexts: A rapid scoping review protocol. Canadian Perspectives on Academic Integrity 5, 59-75.
  4. Shetty, S.V., Guruvyas, K.R., Patil, P.P., Acharya, J.J., 2022. Essay Scoring Systems Using AI and Feature Extraction: A Review, in: Proceedings of Third International Conference on Communication, Computing and Electronics Systems. Springer, Singapore, pp. 45-57. doi:doi:10.1007/978-981-16-8862-1\_4. · DOI

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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
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 · 2023: 1
FWCI
21.16
Type
journal article
Language
en
Open access
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
  • Physical Sciences › Computer Science › Information Systems › 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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