# Human-Centred Learning Analytics and AI in Education: a Systematic Literature Review

arXiv: https://arxiv.org/abs/2312.12751 · HTML: https://mlsanity.com/paper/2312.12751/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 200 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI](https://mlsanity.com/paper/2105.05424/) (2021)
- [StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement](https://mlsanity.com/paper/2202.06205/) (2022)
- [Meaningful human control: actionable properties for AI system development](https://mlsanity.com/paper/2112.01298/) (2021)
- [RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities](https://mlsanity.com/paper/1910.05522/) (2019)
- [Towards Value-Sensitive Learning Analytics Design](https://mlsanity.com/paper/1812.08335/) (2018)
- [Steering Recommendations and Visualising Its Impact: Effects on Adolescents' Trust in E-Learning Platforms](https://mlsanity.com/paper/2303.00098/) (2023)

## Built on by (most cited first, 9 of 9)

- [Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators](https://mlsanity.com/paper/2502.09799/) (2025)
- [Large Language Models Meet User Interfaces: The Case of Provisioning Feedback](https://mlsanity.com/paper/2404.11072/) (2024)
- [WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation](https://mlsanity.com/paper/2412.01626/) (2024)
- [Assessing the Auditability of AI-integrating Systems: A Framework and Learning Analytics Case Study](https://mlsanity.com/paper/2411.08906/) (2024)
- [Human Experts' Evaluation of Generative AI for Contextualizing STEAM Education in the Global South](https://mlsanity.com/paper/2511.19482/) (2025)
- [Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study](https://mlsanity.com/paper/2408.03472/) (2024)
- [Data Science Students Perspectives on Learning Analytics: An Application of Human-Led and LLM Content Analysis](https://mlsanity.com/paper/2502.10409/) (2025)
- [A Mixed User-Centered Approach to Enable Augmented Intelligence in Intelligent Tutoring Systems: The Case of MathAIde app](https://mlsanity.com/paper/2508.00103/) (2025)
- [Culturally-Aware AI for Cross-Boundary Community Learning: Undergraduate Innovation at the Intersection of Computation and Design](https://mlsanity.com/paper/2606.09041/) (2026)

## Abstract (arXiv)

The rapid expansion of Learning Analytics (LA) and Artificial Intelligence in Education (AIED) offers new scalable, data-intensive systems but also 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.