Dr HUANG, Lingyun
黃凌云 博士
Assistant Professor
Department of Curriculum and Instruction
Contact
ORCiD
0000-0002-7336-7079
Phone
(852) 2948 7525
Fax
(852) 2948 7563
Email
lingyunhuang@eduhk.hk
Address
10 Lo Ping Road, Tai Po, New Territories, Hong Kong
Website
https://pappl.eduhk.hk/rich/web/person.xhtml?pid=300256&name=HUANG, Lingyun
Scopus ID
57195130967
Research Interests
Designs of technology-rich environments
Technology-enabled teacher professional development
Self- and Socially shared regulated learning
Learning emotions
Learning sciences and analytics 
Teachers' digital literacy
Teaching Interests

Technology in teaching and learning

Data literacy and assessment

Research methods

Learning sciences and analytics

Other Activities

Editorial Services

Executive Editor for Knowledge Management and E-Learning: An International Journal (2022-present)


Review Services

Computers and Education

Journal of Intelligence

Education and Information Technologies

BMC Medical Education

 

Conference Program Chair Services

International Society of Learning Science Conference 2023

International Conference on Artificial Intelligence in Education 2023

ICCE Sub-Conference on Computer-supported Collaborative Learning (CSCL) and Learning Sciences 2023

GCCCE C2: Mobile, Ubiquitous & Contextual Learning 2023

The 22nd IEEE International Conference on Advanced Learning Technologies 202


Student Supervision

I am available to supervise EdD (English) students in 24/25 and would welcome inquiries for supervision.

Expected background are educational psychology and educational technology

Personal Profile

Lingyun Huang (Franco) is an Assistant Professor in the Department of Curriculum and Instruction of the Faculty of Education and Human Development at the Education University of Hong Kong. He received his Ph.D. from McGill University (Learning Sciences), M.Ed. from Technical University Munich (Education), and B.A from Jiangxi Normal University (English Education). Before joining the Education University of Hong Kong in 2023, he was a postdoctoral fellow at the University of Hong Kong, holding the RGC Postdoctoral Fellowship Award.


Dr. Huang’s research explores the potential of technology in education, including but not limited to designing intelligent technology-rich learning environments and assessing the effectiveness of such environments on teaching and learning. As a learning scientist, Dr. Huang is interested in adopting artificial intelligence computational techniques to examine learners’ cognition, metacognition, behavioral, and affective patterns hidden in learning trajectories. His research makes implications for improving the designs of environments, making them more intelligent, adaptive, and ergonomic for teachers and learners across disciplines to increase their productivity and performance.


Research Interests
Designs of technology-rich environments
Technology-enabled teacher professional development
Self- and Socially shared regulated learning
Learning emotions
Learning sciences and analytics 
Teachers' digital literacy
Teaching Interests

Technology in teaching and learning

Data literacy and assessment

Research methods

Learning sciences and analytics

Other Activities

Editorial Services

Executive Editor for Knowledge Management and E-Learning: An International Journal (2022-present)


Review Services

Computers and Education

Journal of Intelligence

Education and Information Technologies

BMC Medical Education

 

Conference Program Chair Services

International Society of Learning Science Conference 2023

International Conference on Artificial Intelligence in Education 2023

ICCE Sub-Conference on Computer-supported Collaborative Learning (CSCL) and Learning Sciences 2023

GCCCE C2: Mobile, Ubiquitous & Contextual Learning 2023

The 22nd IEEE International Conference on Advanced Learning Technologies 202


Student Supervision

I am available to supervise EdD (English) students in 24/25 and would welcome inquiries for supervision.

Expected background are educational psychology and educational technology

Research Outputs
Journal Publications
Publication in refereed journal
HUANG Lingyun; LIANG Min; XIONG Yuhan, WU Xiaomeng, LIM Cher Ping (2024). A Systematic Review of Technology-Enabled Teacher Professional Development during COVID -19 Pandemic. Computers and Education, 223, 1-18. https://doi.org/10.1016/j.compedu.2024.105168
Huang, L., Zheng, J., Lajoie, S. P., Chen, Y., Hmelo-Silver, C., & Wang, M. (2024). Examining university teachers’ self-regulation in using a learning analytics dashboard for online collaboration. Education and Information Technologies, 29, 8523-8547. https://doi.org/10.1007/s10639-023-12131-7
HUANG Lingyun; DOLECK Tenzin; CHEN Boyin; HUANG Xiaoshan; TAN Chengyi; LAJOIE Susanne P; WANG Minhong (2023). Multimodal learning analytics for assessing teachers’ self-regulated learning in planning technology-integrated lessons in a computer-based environment. Education and Information Technologies, 28, 15823-15843. https://doi.org/10.1007/s10639-023-11804-7
Liu, Y., Huang, Li., Doleck, T. (2023). How teachers’ self-regulation, emotions, perceptions, and experiences predict their capacities for learning analytics dashboard: A Bayesian approach. Education and Information Technologies, 1- 36. https://doi.org/10.1007/s10639-023-12163-z
Yue, X., Liu, F., Yang, Y., Zheng, X., Huang, L. (2023). Examining the antecedent factors and their influences on English language anxiety via the lens of control-value theory and data mining approaches. Current Psychology, 43, 11050-11061. https://doi-org.ezproxy.eduhk.hk/10.1007/s12144-023-05219-3

Conference Papers
Refereed conference paper
HUANG Lingyun, XU Kuangze (2025, March). Cognition, Metacognition, and Emotions in Self-Regulated Lesson Design: Preliminary Results from EDA Signals and Log Files. The International Conference of Learning Analytics and Knowledge, Dublin, Ireland.
Huang, L., Liang, M., Xiong, Y., & Lim, C. (2024, April). A Systematic Review of Technology-Enabled Teacher Professional Development during COVID -19 Pandemic. American Educational Research Association Annual Meeting, Philadelphia, USA.
Huang, L. (2024, March). Student Teachers’ Self-Regulation in Learning Complex Professional Knowledge: A Sequential and Clustering Analysis with Think-Aloud Protocols. 14th International Conference on Learning Analytics & Knowledge (LAK24), Kyoto, Japan.

Projects
A Comparative Study on Policies and Practices for Promoting School Teachers’ AI Competencies in the Greater Bay Area (GBA): Implications for Developing a Digital Repository for AI-Enabled Teaching
A key objective of the study is to explore the extent to which existing policies in the GBA align with global trends and frameworks for teachers’ AI competencies through professional education and development.
1. To compare the policies implemented by regional governments, education departments, and schools within the GBA design to enhance teachers’ AI-related knowledge and skills.
2. To explore practices implemented by GBA governments, educational departments, and schools to enhance teachers’ skills for AI-integrated teaching.
3. Implications for how educational systems in the GBA can take immediate actions to enhance teachers’ skills and strategies for AI-integrated teaching.

Project Start Year: 2025, Principal Investigator(s): HUANG, Lingyun
SDGs Information: 4 - Quality Education
 

Design and Implementation of AI-supported collaborative learning instructions to increase students’ learning engagement in higher education
• To design innovative instruction to facilitate teachers implementing asynchronous collaborative learning
• To examine the effectiveness of the instruction in terms of teachers’ experience.
• To examine the effectiveness of the instruction in terms of students’ collaborative learning experience and learning engagement.

Project Start Year: 2025, Principal Investigator(s): HUANG, Lingyun
SDGs Information: 4 - Quality Education
 

The Role of ChatGPT in Enhancing Academic Performance: Behavioral Patterns and Effective Implementation Strategies
The proposal involves delving into how students interact with AI-powered chatbots, understanding how they perceive and respond to the generated content, and examining the impact on their learning outcomes. By analyzing these behavioral patterns, our research aims to identify the strengths and limitations of ChatGPT as an educational tool and propose effective strategies to maximize its benefits while mitigating potential risks. This includes considerations for ethical use, addressing biases, promoting critical thinking, and fostering meaningful human-AI interactions.

Project Start Year: 2024, Principal Investigator(s): HUANG, Lingyun

 

Enhancing formative assessment and feedback in collaborative inquiries with an AI-powered analytical module
Collaborative inquiry is one of the most prevalent learning approaches in higher education. It involves students working together through sustained communication to solve problems and construct knowledge. Formative assessment and feedback are vital for developing students’ higher-order thinking skills, especially problem-solving and critical thinking, within collaborative inquiries. However, university instructors struggle to assess the status of collaborative inquiries efficiently and provide feedback to students effectively due to increasing class sizes and the vast amounts of discourse data generated. This struggle further diminishes the quality of student learning. In this context, this project introduces an innovative solution in the form of an easy-to-use and user-friendly analytical module powered by artificial intelligence (AI). Guided by the community of inquiry (CoI) theoretical model, this analytical module automatically processes discourse data, models students’ inquiry patterns, and generates visualizations and explanations to inform instructors’ decision-making. This analytical module holds great potential in assisting and enhancing instructors’ formative assessment and feedback practices. During the project period, the analytical module will directly impact 200 to 300 undergraduate and graduate students in several inquiry-based courses (e.g., HPI 2001 Honours Project, CPI 2001 Capstone Project, and PRJ6003 Practitioner-based Research Project). Quasi-experiments will be conducted to explore the effects of AI-assisted formative assessment and feedback on students’ inquiry patterns and perceived feedback effectiveness. In addition, 30 to 50 staff members will benefit from attending workshops and seminars on this project’s design and findings. In the long term, this project aims to extend the reach of the proposed analytical module to all inquiry-based courses in EdUHK and beyond, optimizing the way collaborative inquiries are assessed and feedback is provided. The outcomes and findings of this project will not only provide a sustainable and generalizable path for incorporating AI into teaching and learning processes but also significantly enhance students' achievement of intended learning outcomes, especially problem-solving and critical thinking skills.

Project Start Year: 2024, Principal Investigator(s): BA, Shen (HUANG, Lingyun as Co-Investigator)
SDGs Information: 4 - Quality Education, 9 - Industry, Innovation and Infrastructure, 10 - Reduced Inequality
 

SPROTI, an Online Learning Environments Informed by Regulation of Learning Theory for Teacher Technology Education
The project proposes to develop an online environment to support preservice and in-service teachers' technology integration development.

Project Start Year: 2024, Principal Investigator(s): HUANG, Lingyun

 

Technology-Enabled Futures of Education: Harnessing Emerging Technologies to Enhance Education Equity, Quality and Efficiency
..

Project Start Year: 2022, Principal Investigator(s): LIM, Cher Ping, CHAN, Che Hin, Chetwyn (HUANG, Lingyun as Co-Investigator)