Jie Xiang Fan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jie Xiang Fan has a total of 27 co-authors. Here are the top co-authors by number of shared publications:
- Riordan Alfredo
Monash University
8 shared publications - Vanessa Echeverria
RMIT University
8 shared publications - Dragan Gašević
Monash University
7 shared publications - Lixiang Yan
School of Education Tsinghua University Beijing China
7 shared publications - Mikaela Elizabeth Milesi
Monash University
7 shared publications - Roberto Martinez-Maldonado
Monash University
7 shared publications
Latest Publications
- 7 October 2026
- Published by Springer Nature
- p. 451-466
Decoding the Dashboard: Data Comics to Support Students’ Understanding of Learning Analytics Visualisations
- 7 October 2026
- Published by Springer Nature
- p. 451-466
Decoding the Dashboard: Data Comics to Support Students’ Understanding of Learning Analytics Visualisations
- 6 October 2026
- Published by Springer Nature
- p. 149-154
Designing Support for Written Reflections Using GenAI Summaries and Chatbots
- 6 October 2026
- Published by Springer Nature
- p. 149-154
Designing Support for Written Reflections Using GenAI Summaries and Chatbots
- Open Access
- 24 September 2026
- Published by Taylor & Francis in International Journal of Human–Computer Interaction
- p. 1-38
Comparing GenAI Summaries Versus Chatbots for Supporting Reflection in Healthcare Simulation
1. Reflection is a crucial process for learners to make sense of experiences, consolidate their learning, and understand how they can improve their practice (Pammer et al., 2017; Rabu & Badlishah, ...
- 24 September 2026
- Published by Taylor & Francis in International Journal of Human–Computer Interaction
- p. 1-38
Open AccessComparing GenAI Summaries Versus Chatbots for Supporting Reflection in Healthcare Simulation
1. Reflection is a crucial process for learners to make sense of experiences, consolidate their learning, and understand how they can improve their practice (Pammer et al., 2017; Rabu & Badlishah, ...
- Open Access
- 23 December 2025
- Published by Springer Nature in International Journal of Educational Technology in Higher Education
- Vol. 22 (1) , 78
Co-designing AI-powered learning analytics: bringing students and teachers together
There is a growing interest in involving students and teachers in the design of human-centered Learning Analytics (LA) systems to align them with authentic learning needs. Yet, limited prior research has explored the implications of integrating both students’ and teachers’ perspectives within a structured co-design process. To address this shortcoming in the literature, we report on a study that examined how undergraduate nursing students and teachers co-designed an AI-powered LA system to support post-debriefing reflection on teamwork and communication in the context of healthcare simulation. This qualitative study, using a co-design approach, examined the design process of an LA system from conceptualization to post-use evaluation. The study addressed two key questions: i ) What tensions emerge from the contrasting perspectives of students and teachers in the co-design an AI-powered LA system? and ii ) How do students and teachers perceive their joint participation in the co-design process? Three key design tension themes emerged from the contrasting perspectives of students and teachers: teaching–learning goals tension , privacy–utility tension , and human-AI guidance preferences tension . The collaborative design process revealed mutual benefits: students valued teachers’ guidance in refining ideas and aligning system goals with learning objectives, while teachers, initially cautious about student involvement, came to see co-design as an opportunity to empower students and deepen their own understanding of responsible data use in practice. These findings contribute to the broader understanding of co-design dynamics in educational technology, underscoring the importance of balanced stakeholder involvement in developing practical, context-aware LA systems.
- 23 December 2025
- Published by Springer Nature in International Journal of Educational Technology in Higher Education
- Vol. 22 (1) , 78
Open AccessCo-designing AI-powered learning analytics: bringing students and teachers together
There is a growing interest in involving students and teachers in the design of human-centered Learning Analytics (LA) systems to align them with authentic learning needs. Yet, limited prior research has explored the implications of integrating both students’ and teachers’ perspectives within a structured co-design process. To address this shortcoming in the literature, we report on a study that examined how undergraduate nursing students and teachers co-designed an AI-powered LA system to support post-debriefing reflection on teamwork and communication in the context of healthcare simulation. This qualitative study, using a co-design approach, examined the design process of an LA system from conceptualization to post-use evaluation. The study addressed two key questions: i ) What tensions emerge from the contrasting perspectives of students and teachers in the co-design an AI-powered LA system? and ii ) How do students and teachers perceive their joint participation in the co-design process? Three key design tension themes emerged from the contrasting perspectives of students and teachers: teaching–learning goals tension , privacy–utility tension , and human-AI guidance preferences tension . The collaborative design process revealed mutual benefits: students valued teachers’ guidance in refining ideas and aligning system goals with learning objectives, while teachers, initially cautious about student involvement, came to see co-design as an opportunity to empower students and deepen their own understanding of responsible data use in practice. These findings contribute to the broader understanding of co-design dynamics in educational technology, underscoring the importance of balanced stakeholder involvement in developing practical, context-aware LA systems.
- Top Cited PapersOpen Access
- 26 April 2025
- Published by Elsevier in Computers & Education
- Vol. 234
The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics
- 26 April 2025
- Published by Elsevier in Computers & Education
- Vol. 234
Top Cited PapersOpen AccessThe effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics
- Open Access
- 25 April 2025
- Published by Association for Computing Machinery (ACM)
- p. 1-22
TeamVision: An AI-powered Learning Analytics System for Supporting Reflection in Team-based Healthcare Simulation
- 25 April 2025
- Published by Association for Computing Machinery (ACM)
- p. 1-22
Open AccessTeamVision: An AI-powered Learning Analytics System for Supporting Reflection in Team-based Healthcare Simulation
- Top Cited PapersOpen Access
- 3 March 2025
- Published by Association for Computing Machinery (ACM)
- p. 579-590
Chatting with a Learning Analytics Dashboard: The Role of Generative AI Literacy on Learner Interaction with Conventional and Scaffolding Chatbots
- 3 March 2025
- Published by Association for Computing Machinery (ACM)
- p. 579-590
Top Cited PapersOpen AccessChatting with a Learning Analytics Dashboard: The Role of Generative AI Literacy on Learner Interaction with Conventional and Scaffolding Chatbots
- Open Access
- 21 October 2024
- Published by Society for Learning Analytics Research in Journal of Learning Analytics
- p. 1-20
Designing a Human-centred Learning Analytics Dashboard In-use
Despite growing interest in applying human-centred design methods to create learning analytics (LA) systems, most efforts have concentrated on initial design phases, with limited exploration of how LA tools and practices can co-evolve during the actual learning and teaching activities. This paper examines how a human-centred LA dashboard can be further refined and adapted by teachers while actively using it in a real-world scenario (i.e., design-in-use), beyond its intended design (i.e., design-for-use). We use instrumental genesis as a theoretical lens to analyze the temporary and permanent instrumentalization of design features and individual and collective instrumentation of the LA dashboard. The analysis of semi-structured individual interviews with five nursing teachers who used an LA dashboard to guide team reflections with 224 students (56 teams) revealed technical and pedagogical changes that occurred in both the system’s features (instrumentalization) and teaching practices (instrumentation). We found that teachers adopted the LA dashboard beyond initially intended ways by (i) providing emotional support with the analytics, (ii) reducing details in AI-powered data visualizations for easier comprehension, (iii) creating data narratives to address data limitations, and (iv) collectively developing new practices to use the LA dashboard for co-teaching. Therefore, teachers’ design-in-use of the LA dashboard highlights the ongoing need for design improvements to address challenges posed by dynamic data and complex algorithms underlying AI and analytics interfaces.
- 21 October 2024
- Published by Society for Learning Analytics Research in Journal of Learning Analytics
- p. 1-20
Open AccessDesigning a Human-centred Learning Analytics Dashboard In-use
Despite growing interest in applying human-centred design methods to create learning analytics (LA) systems, most efforts have concentrated on initial design phases, with limited exploration of how LA tools and practices can co-evolve during the actual learning and teaching activities. This paper examines how a human-centred LA dashboard can be further refined and adapted by teachers while actively using it in a real-world scenario (i.e., design-in-use), beyond its intended design (i.e., design-for-use). We use instrumental genesis as a theoretical lens to analyze the temporary and permanent instrumentalization of design features and individual and collective instrumentation of the LA dashboard. The analysis of semi-structured individual interviews with five nursing teachers who used an LA dashboard to guide team reflections with 224 students (56 teams) revealed technical and pedagogical changes that occurred in both the system’s features (instrumentalization) and teaching practices (instrumentation). We found that teachers adopted the LA dashboard beyond initially intended ways by (i) providing emotional support with the analytics, (ii) reducing details in AI-powered data visualizations for easier comprehension, (iii) creating data narratives to address data limitations, and (iv) collectively developing new practices to use the LA dashboard for co-teaching. Therefore, teachers’ design-in-use of the LA dashboard highlights the ongoing need for design improvements to address challenges posed by dynamic data and complex algorithms underlying AI and analytics interfaces.