HI Data Forum @ LeaF: Understanding Human–AI interaction through Heterogeneous Interaction Network Analysis (HINA)

HI Data Forum @ LeaF: Heterogeneous Interaction Network Analysis (HINA) framework on Thu 28.5.2026 brings together researchers to explore advanced methods for analysing complex learning interactions.

The event focuses on the Heterogeneous Interaction Network Analysis (HINA) framework, a novel approach for modelling dynamic relationships among learners, AI agents, and digital artefacts in digital learning environments. The session is led by Shihui Feng, Assistant Professor at the University of Hong Kong, whose research focuses on complex networks and learning analytics. Through research presentation and a hands‑on workshop, participants will gain practical experience in applying HINA to uncover interaction patterns, support hypothesis testing, and enrich process‑oriented learning analytics.

The event is part of the Hybrid Intelligence: Human-AI Co-evolution and Learning in Multi-realities (HI) research program, which examines the role of data in advancing research and innovation in human-centred AI.

The hybrid event is free and open to all!
Secure your spot and register:
onsite participation closes May 26 and online registration by May 27 at 12:00 (noon).

 Program

13:00
Opening

13:00
Heterogeneous Interaction Network Analysis (HINA): A New Learning Analytics Framework for Modelling Complex Interactions in Learning Processes, Assistant Professor Shihui Feng, Faculty of Education, University of Hong Kong

14.00
Hands-on Introduction to HINA framework

This hands-on workshop introduces participants to the Heterogeneous Interaction Network Analysis (HINA) framework an innovative approach for modelling and analysing complex interactions in contemporary learning environments. No prior experience with network analysis is required, as participants will be guided step by step through the analytical workflow. Participants who wish to try HINA during the talk will need to bring their own laptop and can register an account beforehand (hina-network.com).

14:30
Closing

Abstract of the presentation

Existing learning analytics approaches, which often model learning processes as sequences of learner actions or homogeneous relationships, are limited in capturing the distributed, multi-faceted nature of interactions in contemporary learning environments. To address this gap, this talk introduces the Heterogeneous Interaction Network Analysis (HINA) framework—a novel methodological approach for modelling complex learning interactions between diverse entities such as learners, AI agents, and artefacts in technology-enhanced learning environments.

The talk will cover HINA's methodological foundations for individual, dyadic, and meso-level analysis. It will showcase empirical studies in which HINA was applied to uncover students’ human–AI interaction patterns and to quantify individual participation in support of process-oriented hypothesis testing.

About the speaker

Shihui Feng is an Assistant Professor in the Faculty of Education at the University of Hong Kong. Her research expertise lies in complex networks and learning analytics, with a particular focus on developing new analytical methods and theoretical frameworks to investigate collaborative learning and social interactions in digital learning environments. Her recent work involves the development of the Heterogeneous Interaction Network Analysis (HINA), a novel learning analytics framework and open-source tool designed to analyze complex interactions in learning processes.

Warmly welcome!

Register here.