60th Anniversary e-Newsletter Aug 2025
CUHK Faculty of Education Achieves Global Recognition
The CUHK Faculty of Education has achieved an impressive global ranking, placing 2nd in the world in the 2025-2026 Best Global Universities Rankings by U.S. News & World Report in the field of Education & Educational Research. This ranking is part of a comprehensive evaluation of 2,250 top institutions from 105 countries, based on 13 key indicators, including academic reputation, research output, international collaboration and more.
CUHK's Faculty of Education has consistently demonstrated leadership in educational research, making significant contributions to the advancement of teaching and learning practices worldwide. This prestigious recognition highlights our unwavering commitment to academic excellence and innovative research in education.
Reimagining Education with Artificial Intelligence
Artificial Intelligence (AI) is no longer a distant concept—it has become a driving force in the evolution of modern education. From personalised learning to data-driven decision-making, AI is unlocking new possibilities for educators and learners alike.
AI is reshaping the education landscape, and our faculty members are at the forefront of this transformation through pioneering research and impactful projects:
- Prof. Hau Kit Tai demonstrates how ChatGPT revolutionises educational data analysis by drastically reducing the time and effort required to interpret large-scale assessment data.
- Prof. Chen Si’s multimodal AI system maps child curiosity and teacher scaffolding across diverse Chinese contexts, offering scalable insights into early childhood learning.
- Under the leadership of Prof. Morris Jong, the Centre for Learning Sciences and Technologies empowers thousands of teachers through AI-integrated professional development, fostering future-ready classrooms.
- Prof. Yang Yijian applies machine learning to help practitioners customising exercise programmes for older adults, enhancing care delivery and engagement.
These interdisciplinary initiatives highlight AI’s potential to personalise learning, enhance teaching practices, and support evidence-based education policy. Collectively, they contribute to a more ethical, inclusive, and transformative future for education.
ChatGPT as a Powerful Research Assistant for Educational Data Analysis in Our Educational Data Research Projects
Prof. Hau Kit Tai
ChatGPT has become an invaluable tool for streamlining our research workflow. Previously, analysing Hong Kong students’ performance in large-scale international assessments, such as PISA and TIMSS, required considerable manpower—typically two full-time research assistants working for up to two months. Their tasks involved generating SPSS syntax, performing comparative analyses across multiple dimensions (e.g., benchmarking Hong Kong students against boys and girls from other high-performing economies), and summarising results from over 100 constructs containing tens of thousands of statistical statements and interpretations.
With ChatGPT, this entire process has been dramatically accelerated and simplified. We can now generate SPSS syntax tailored to specific analytical goals with just brief instructions. ChatGPT also automates the compilation and interpretation of statistical outputs, transforming them into structured, policy-relevant summaries suitable for traditional government report formats.
What previously required two months of manual effort can now be accomplished in just half a day, based on a simple half-page instruction. This remarkable efficiency has significantly enhanced our research productivity, improved consistency, and boosted our responsiveness to policy needs.
The following reports are generated by ChatGPT.
Harnessing AI to Advance Early Childhood Education: A Multimodal Approach
Prof. Chen Si
AI is transforming early childhood educational research by offering innovative tools to deepen our understanding of children's learning processes and empower teachers to deliver more effective developmental support.
Our study, "Digital Eyes on Growing Minds: Mapping Child Curiosity and Teacher Scaffolding Across Rural-Urban China," highlights how AI can uncover the dynamic relationships interplay between children's curiosity, teacher scaffolding, and the Zone of Proximal Development (ZPD).
In addition to advancing early childhood education across diverse cultural contexts, this project establishes a scalable, cost-effective, and culturally responsive model for AI-driven methodologies.
From Traditional Observation to AI-Powered Multimodal Analysis
Traditional methods of studying children's learning behaviours often rely on subjective observations and self-reports, which are inherently limited in scope, consistency, and objectivity. To address the limitations, our research introduces an AI-driven multimodal measurement system that integrates computer vision, natural language processing (NLP), and machine learning to capture and analyse curiosity-driven learning behaviors in real time.
This innovative approach provides objective, dynamic, and scalable tools for quantifying children's curiosity, mapping their ZPD, and evaluating the effectiveness of teacher scaffolding strategies.
The system synthesises data from multiple sources simultaneously. Video recordings capture facial expressions and behavioral patterns, serving as emotional and exploratory markers of curiosity through computer vision analysis. Audio interactions are processed using NLP to identify curiosity-driven questions and elaborative responses. Additionally, physiological signals provide complementary insights into engagement and attention.
These diverse data streams are synchronised and analysed to reveal the temporal dynamics of how curiosity emerges, how children transition through their ZPD, and how teachers adapt their scaffolding strategies to support learning effectively.
Building Interdisciplinary Foundations Through Strategic Collaboration
The sophistication of our AI-powered approach has been achieved through strategic collaborations with leading experts from diverse disciplines. Our partnership with Professor XIAO Xiao from Fudan University's Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI) brings invaluable expertise in neuroscience and brain-inspired AI algorithms. Professor JP de Ruiter from Tufts University contributes profound insights into human communication and interaction analysis, while Professor GAO Yuan from the Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) provides specialized knowledge in human-robot interaction and AI system design.
These collaborations have built a robust interdisciplinary foundation that goes beyond technical development. Together, our teams have established the computational infrastructure required for advanced multimodal data capture and analysis, secured access to high-performance computing resources, and laid the groundwork for developing AI-powered feedback systems that can operate in real-world educational settings.
The academic impact of these partnerships is evident in our high-impact publications, presentations at international conferences, successful funding applications, and the creation of lasting research networks that continue to amplify the project's influence and research.
Uncovering Educational Patterns Across Rural and Urban China
A distinctive feature of our research is its comparative analysis of how curiosity manifests and how scaffolding strategies differ across rural and urban educational contexts in China. This comparison highlights the influence of environmental and cultural factors on learning experiences. Rural settings often offer rich experiential learning opportunities tied to natural environments and agricultural practices, encouraging curiosity through hands-on interaction with the physical world. In contrast, urban environments typically prioritise structured learning enhanced by technological resources, creating different pathways for curiosity development.
Our study involves hundreds of teacher-child pairs, equally distributed between rural and urban settings across diverse regions, including Sichuan, Anhui, Shenzhen, and Shanghai. By applying machine learning to this extensive dataset, we are uncovering patterns that illustrate not only the varied manifestations of curiosity across these contexts but also how teachers, parents, and even grandparents tailor their scaffolding strategies to align with their unique environments and available resources.
This understanding of contextual differences has allowed us to design culturally responsive intervention programmes that leverage AI to enhance teaching effectiveness. Our next step involves deploying an AI-powered feedback system that analyses teacher-child interactions in real time, offering personalised scaffolding suggestions tailored to specific moments in the learning process.
By integrating AI with human wisdom, we are not merely observing how children learn—we are shaping a future where every child's natural curiosity serves as the foundation for lifelong learning. This version is supported by teachers equipped with both cultural understanding and technological insight.
Advancing Artificial Intelligence in Education through Teacher Capacity Building
Prof. Morris Jong
Artificial Intelligence (AI) is redefining the future of education, transforming the way teachers teach and students learn. The Centre for Learning Sciences and Technologies (CLST) led by Prof. Morris Jong, at the Department of Curriculum and Instruction, plays a pivotal role in advancing the Faculty of Education’s commitment to educational innovation.
Through a variety of professional development initiatives, CLST supports the integration of AI into teaching practices, empowering educators to explore new possibilities in their classrooms. By providing comprehensive training, subject-specific innovation, and sustained school-based support, our programmes not only equip teachers to leverage AI technologies in meaningful ways but also foster a culture of student-centred, future-ready teaching across Hong Kong’s schools.
Professional Development Programmes on Learning and Teaching with AI, Funded by the Education Bureau (EDB)
To address the evolving needs of the teaching profession, CLST has introduced four AI-focused professional development programmes. These initiatives aim to enhance teachers’ competencies while promoting ethical, effective integration of AI technologies in the classroom.
- Effective Use of AI Technologies to Facilitate Learning and Teaching (2024–2026)
This foundational programme introduces teachers to essential AI tools and concepts that enhance personalised teaching, content creation, and lesson planning. Participants explore practical applications of AI across various subject areas, including STEAM, language education, and the humanities. Key topics cover adaptive learning systems, Natural Language Processing (NLP), virtual and augmented reality (VR/AR), chatbots, and AI-driven analytics. Key ethical issues—data privacy, bias, and equity—are also examined. To date, over 1,300 teachers have participated in and benefited from this programme. - Subject-specific AI Programmes (2025–2027)
CLST has developed three professional development programmes specifically tailored for language and mathematics teachers. These programmes aim to equip primary and secondary educators with the knowledge and skills needed to meaningfully integrate AI tools into their respective disciplines.
In Chinese and English language education, teachers explore the application of AI technologies such as natural language processing, speech recognition, and machine learning to enhance instruction in reading, writing, and speaking. The programmes also emphasise promoting learner-centred and autonomous learning.
For mathematics, the programme introduces AI concepts such as modelling of real-world phenomena and abstracting concepts beyond specific instances. These concepts help teachers understand how AI can support mathematical thinking and deepen students’ learning experiences.
All three courses place a strong emphasis on ethical awareness and responsible use of AI in the classroom. In total, these programmes are expected to benefit 4,200 teachers across Hong Kong.
Harnessing AI to Promote Inquiry-Based Learning in Junior Secondary Science Education—“AI for Science Education” Programme Supported by the Quality Education Fund (QEF)
The “AI for Science Education” Programme is designed to enhance science teachers’ capacity in AI-assisted teaching, encourage schools to support teacher participation in professional development programmes and facilitate trial lessons to foster pedagogical innovations. The ultimate goal is to improve students’ learning outcomes. This programme, led by CLST, emphasises advancing inquiry-based teaching in junior secondary science by equipping teachers to integrate AI technologies to create interactive, student-centered learning experiences through the following key components:
- Teacher Training Course
This course introduces the fundamentals of AI, including generative AI and large language models (LLMs). Through practical, hands-on activities, teachers learn how to integrate AI tools into science lesson design, promoting inquiry, critical thinking, and exploration. - School-based Support
After completing the training course, CLST offers in-school support, where expert teams collaborate directly with teachers to implement AI strategies in real classroom settings. This personalised guidance helps educators refine lesson designs, address challenges, and sustain the effective use of AI in their teaching practices.
Through these initiatives, CLST supports the Faculty of Education’s ongoing efforts to lead the integration of AI in education. By providing comprehensive training, subject-specific innovations, and sustained school-based support, our programmes empower educators to effectively utilise AI technologies. Additionally, they foster a culture of student-centred, future-ready teaching across schools in Hong Kong.
Applying AI Technology to the Design of Customised Exercise Programmes for Older Adults
Prof. Yang Yijian
Hong Kong’s population is aging rapidly, with the proportion of older adults (aged 65 years and above) projected to reach 30% by 2036. As life expectancy increases, the demand for care facilities continues to grow to meet the needs of elderly care. Many older adults in these settings face complex health challenges, including physical impairments, with over 50% unable to independently perform at least one daily activity.
Research shows that exercises can help mitigate age-related physical decline and maintain functional capacity in older adults. Despite these benefits, participation rates in exercise programmes remain low. This may be because many programmes are too generic and fail to engage older adults with diverse physical impairments. Additionally, most care facilities lack exercise professionals to design and implement tailored exercise programs, further limiting access to effective interventions.
To address these challenges, we incorporated AI, specifically machine learning, into the design of an exercise recommendation model. This model aims to help care practitioners deliver customised exercise programmes tailored to older adults with different functional capacities. The foundation of this model is our previously developed multicomponent exercise programme, Mobility-Fit (MBF), which focuses on improving balance, strength, coordination, and agility (Figure 1).
Figure 1. Schematic of the Mobility-Fit (MBF) program (adapted from Yang et al, 2024).
In a prior study, we assessed the physical capacities of 160 older adults, following which physiotherapists delivered the MBF programme to 80 participants. This dataset was then leveraged to train a machine learning model that could learn the relationship between the physical capacities of older adults and the appropriate MBF programme. The goal was to replicate physiotherapists’ recommendations regarding exercise type, intensity, frequency, and duration.
As illustrated in Figure 2, the AI-powered exercise recommendation model comprises two sub-models: Model A and Model B. First, physical assessment data from the 80 participants who had undergone the MBF intervention were manually labeled by experts (e.g., physiotherapists) to assign suitable customised exercise programmes. Using the labeled dataset, we trained a supervised learning model (Model A) to predict appropriate exercises.
Figure 2. Architecture of the AI-customized exercise recommendation model.
Next, Model A was applied to the remaining unlabeled data (80 participants who had not practised MBF) to generate pseudo-labels (i.e., predicted MBF exercises). After fine-tuning and further training, both the labeled and pseudo-labeled datasets were used to train a more robust supervised learning model, Model B, which learned the relationship between physical capacities and corresponding exercise programmes.
Once trained, this model can automatically generate MBF exercise recommendations for new participants without requiring constant expert input, making it a cost-efficient solution for care facilities. By integrating AI into the exercise recommendation process, the model achieves performance comparable to physiotherapists, enhancing its practicality and scalability in read-world applications.
The integration of AI not only enhances efficiency in care services but also improves the effectiveness of exercise delivery. The AI-assisted customised exercise recommendation model is capable of tracking the progress of older adults, automatically adjusting their exercise routines to optimise health outcomes over time.
Additionally, Explainable AI technology, specifiially SHapley Additive exPlanations (SHAP), is incorporated into the model to interpret and clarify the AI-generated exercise recommendations. This feature helps users understand the rationale behind their personalised exercise programmes, fostering greater trust in the model (Figure 3).
Figure 3. Overview of the data pipeline in the AI-customized exercise recommendation model.
For instance, if the model suggests a low-intensity muscle-strengthening exercise, SHAP might reveal that this recommendation is based on the participant’s low scores on the sit-to-stand performance test, indicating weaker lower limb and core muscle strength. By providing such insights, the model empowers older adults to better understand their tailored exercise plans, which can significantly improve adherence and, consequently maximise their health benefits. Furthermore, Explainable AI technology offers care practitioners valuable insights into how the recommended exercise programmes align with the physical capacities of older adults. This enables practitioners to refine their care strategies and provide more personalised and effective support, further enhancing the overall quality of care.
Looking Ahead: A Future Shaped by Intelligent Learning
From fostering curiosity in younger learners to empowering educators and enhancing elderly wellness, our Faculty is redefining the possibility of AI. By integrating advanced technologies into pedagogy, professional development, and policy research, we ensure that Hong Kong remains a leader in educational innovation.
As AI continues to advance, our commitment to interdisciplinary approach and cultural responsiveness will shape the future of learning. We aim to guide the next generation of learners and educators toward a smarter, more inclusive, and equitable future.
Funded Research Projects
| General Research Fund (2025-2026) | | |
|---|---|---|
| Principal Investigator |
Project Title | |
| Prof. Chiu Kin Fung Thomas | Does Generative AI Support or Thwart Needs Satisfaction in STEM Self-Regulated Learning? | + |
Generative AI (GenAI) tools have the potential to support independent learning, but concerns have been raised about their impact on human interaction and critical thinking, leading to the risk of students becoming overly reliant on these tools. Research indicates that GenAI can both motivate and demotivate students, depending on how it meets or undermines their psychological needs during the learning process. This project builds on two previous studies, combining insights on STEM interest and self-regulated learning (SRL) to examine how GenAI influences students’ learning experiences in STEM education. The project aims to: To achieve these goals, experts and teachers will collaborate to design the activity list, while the project team will implement and test the activities in a 15-week study. Data will be collected through surveys, interviews, and lesson feedback to track progress and outcomes. The findings will provide valuable insights for policy makers, teachers and parents, helping to shape GenAI education policies and enhance STEM learning experiences with AI in schools. |
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| Prof. Leung Kit Ying Suzannie | Investigating In-Service Kindergarten Teachers’ Professional Learning Community for Early Visual Arts Education through a Digital Commons | + |
Arts-related pedagogical practices in Asian kindergarten settings have traditionally been product-oriented, with a focus on outcomes rather than processes. Inadequate teacher training further limits the knowledge and competencies of early visual arts educators, a challenge particularly evident in Hong Kong. Establishing a professional learning community (PLC) offers a promising approach for fostering teachers’ professional development.
This study aims to explore how in-service kindergarten teachers can create an online PLC to share and enhance their artistic knowledge and skills within a digital commons. Using a mixed-method approach, the research investigates:
1. Teachers’ PLC behaviours on an online platform. 2. The knowledge and skills teachers acquire in early visual arts education through using a digital commons. 3. The development and effectiveness of a digital commons as a tool for professional growth.
The study’s theoretical contribution lies in advancing our understanding of how kindergarten teachers can establish and sustain PLCs using an online platform, thereby supporting their professional development in early visual arts education. |
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| Prof. Ng Oi Lam | Investigating the Secondary–Tertiary Mathematics Transition in Hong Kong: Toward Local and Global Significance | + |
This project examines the challenges students face when transitioning from secondary to tertiary mathematics education, where persistent gaps have been identified both locally and internationally.
Despite ongoing curricular reforms, many students struggle with university-level mathematics, particularly in pre-calculus and calculus, raising concerns about their readiness for STEM careers. This study adopts a mixed-methods approach, engaging three key stakeholder groups—teachers, students, and university academics—to assess mathematical competence and explore their teaching and learning experiences.
Through quantitative assessments and task-based interviews, the research aims to uncover the cognitive and sociocultural factors that influence this critical transition. The findings will contribute to refining theoretical insights into advanced mathematics learning and provide evidence-based recommendations for improving curriculum and instruction.
Ultimately, the project seeks to enhance the coherence of mathematics education across educational levels, fostering sustainable STEM development on both local and global scales. Its broader impact includes informing policy reforms to better align public education systems with the evolving demands of the digital age. |
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| Prof. Poon Tsz Chun Eric | Building Resilience Against Rising Temperature - The Effect of High-Intensity Interval Exercise (HIIE) on Heat Tolerance, Thermoregulatory Adaptation, and Physical Fitness: A Comparative Randomized Controlled Trial | + |
This study investigates whether high-intensity interval exercise (HIIE), a time-efficient workout, can enhance heat resilience, thermoregulation and physical fitness compared to traditional exercise. One hundred physically inactive adults will be recruited and assigned to one of four groups: HIIE, moderate continuous exercise, light walking, or no exercise.
Over a 12-week period, participants will engage in their respective exercise programmes three times per week. Tests conducted in a heat-simulated environment will measure key metrics such as heat tolerance, body temperature, sweat rate, and fitness levels. Assessments will take place before, during, and after the intervention to monitor changes over time.
The findings of this research aim to inform policymakers and health professionals in developing effective exercise strategies to mitigate heat-related risks. By promoting adaptive fitness programmes, this study seeks to support healthier, more resilient populations in Hong Kong and beyond amidst the growing challenges of climate change. |
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| Prof. Qian Haiyan | Leading Cross-City School Networks in the GBA: Configurations, Practices and Impact | + |
This study adopts a multilevel distributed perspective to examine the leadership distribution and practices in four selected cross-city school networks in the GBA, using a case study design.
The research focuses on three key areas: 1. Network Configuration: How each network is structured and how leadership is distributed across the different levels of leaders. 2. Leadership Practices: How leadership is enacted within each network, including how leaders at different levels mobilise and coordinate resources, as well as nurture relationships to initiate and implement network-wide improvement initiatives. 3. Impact on school leaders: How school-level and teacher leaders from member schools perceive the benefits of participating in a school network.
This study aims to provide insights into the dynamics of leadership within cross-city school networks and their influence on educational improvement initiatives. |
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| Prof. Yin Hongbiao | Beyond the Cold Technology: Teachers’ Cognitive Appraisal, Emotions and Coping in Generative AI Usage and its Antecedents and Consequences | + |
This project aims to investigate primary school teachers’ cognitive appraisals, emotional responses, and coping strategies in using generative artificial intelligence (GenAI) within educational settings in the Guangdong-Hong Kong-Macao Greater Bay Area. It also seeks to explore the interactions between these elements and their relationships with the antecedents and consequences of GenAI use.
GenAI has emerged as a transformative tool in education, reshaping teaching and learning processes. However, there is limited understanding of the mechanisms that influence teachers’ acceptance and effective utilisation of GenAI. While existing technology acceptance models identify factors that drive technology adoption, they often overlook the emotional dimensions of teachers' experiences. Furthermore, scholars have called for a more comprehensive framework that incorporates GenAI-specific characteristics while accounting for individual, contextual, and cultural factors to better understand its adoption and impact on teacher outcomes.
To address these gaps in the literature, this project adopts a convergent parallel mixed-methods design, aiming to provide deeper insights into the dynamics of GenAI usage by educators and its implications for educational practice. |
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| Prof. Zhou Sihan | Orchestrating listening in EMI university lectures: A multimodal perspective of students’ strategic lecture comprehension | + |
The global expansion of English-medium instruction (EMI) in higher education foregrounds the need to understand how students process academic content in a second language.
This project bridges research on second language listening and multimodality to investigate how university students perceive and use multimodal information—such as teacher speech, visuals, and gestures—in strategically comprehending EMI academic lectures. Adopting a two-stage mixed-methods design, the study will first capture students' real-time strategy use through idiodynamic stimulated recall interviews. Subsequently, it will develop and validate an EMI Multimodal Listening Strategy Questionnaire via a large-scale survey.
The findings aim to advance theoretical frameworks for multimodal listening comprehension, provide a validated research tool, and inform strategy training programs to enhance students’ multimodal literacy and strategic listening competence in EMI higher education. |
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| Early Career Scheme (2025-2026) | | |
|---|---|---|
| Principal Investigator |
Project Title | |
| Prof. Chen Si | Sustainable Early Literacy Development in Rural China: A Collaborative Shared Book Reading Intervention Across Family, School and Community | + |
Rural children in China face significant educational inequities, with literacy skills falling far behind those of their urban peers. Professor Chen’s Early Career Scheme (ECS) project seeks to address these disparities by implementing a comprehensive, multi-faceted support system that integrates families, schools, and communities.
The intervention provides rural kindergartens with high-quality picture books and equips teachers with training in evidence-based interactive reading techniques. Parents receive structured guidance and resources through WeChat, China's widely used messaging platform, to support shared reading practices at home. Additionally, community libraries play a vital role by offering free book-lending services and organising weekend reading activities, establishing local hubs for literacy development.
This study will involve 1,000 children across 80 classrooms in China's southwestern provinces. The effectiveness of this three-pronged approach—focusing on families, schools, and community resources—will be evaluated against single-component strategies. By leveraging existing technologies and community infrastructure, the project aims to establish a scalable, sustainable model for improving early literacy outcomes in rural China.
Ultimately, this initiative has the potential to transform early education for millions of disadvantaged children, significantly narrowing the urban-rural education gap. |
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| Prof. Dai Kun | Early Career Academics in Hong Kong: Talent Attraction and Development | + |
Early career academics (ECAs) play a critical role in advancing and sustaining higher education and society. However, universities worldwide are increasingly challenged in attracting and retaining global ECA talents.
This study focuses on Hong Kong (HK), an international higher education hub, to explore how global ECAs navigate their careers amidst recent ‘brain-drain’ challenges caused by geopolitical tensions and the pandemic. This research primarily involves interviews with a diverse group of ECAs—both local and non-local, and from various disciplines—to examine their motivations, challenges, coping strategies, access to institutional support, and perceptions of their career journeys in HK.
Additionally, the study includes a document analysis of talent attraction policies across the eight UGC-funded universities in Hong Kong. Interviews with faculty leaders are also conducted to assess current support strategies for ECAs.
By examining Hong Kong’s higher education system, this research offers significant empirical contributions to international studies on ECA mobility and development. |
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| Research Impact Fund | | |
|---|---|---|
| Project Coordinator |
Project Title | |
| Prof. Sit Hui Ping Cindy | Battling Sedentarism in Children with Special Educational Needs through Inclusive Physical Activity | + |
The Research Impact Fund project, “Battling Sedentarism in Children with Special Educational Needs (SEN) through Inclusive Physical Activity,” aims to foster an inclusive environment that encourages children with SEN to engage in 60 minutes of moderate to vigorous physical activity daily while reducing sedentary behaviour.
The project applies the STEP principle (Space, Time, Equipment, Person) and the socio-ecological framework to address sedentarism at multiple levels: intrapersonal, interpersonal, organisational, community, and societal. By providing quality physical education, inclusive physical activity sessions, and self-monitoring tools, the initiative directly benefits children with SEN.
To amplify its impact, the project employs a train-the-trainer model, empowering teachers, parents, and coaches to support and sustain these efforts. Through the collaboration of a multidisciplinary research team, educators, families, and community partners, this initiative promotes both physical and mental health for children with SEN. Ultimately, it aims to make a meaningful social impact by fostering an inclusive and supportive environment for physical activity in Hong Kong. |
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Exploring Excellence: Research Centres at the Faculty
In this series, we highlight the Faculty’s research centres—dynamic hubs of innovation, collaboration, and academic excellence. These centres play a vital role in advancing educational theory and practice on both local and global scales. We begin with the Centre for University and School Partnership, a trailblazing initiative dedicated to fostering impactful collaboration between the university and schools.
Fostering School Partnerships to Promote Quality Education in Hong Kong
Founded in 1998, the Centre for University and School Partnership (CUSP) is committed to enhancing quality and holistic education through close collaboration with schools across Hong Kong. Serving as an academic support unit, CUSP bridges research and real-world practices, equipping schools with evidence-based strategies to address evolving educational needs. Over the years, CUSP has partnered with more than 2,000 schools, providing a diverse range of professional services to support their sustainable growth and development.
The inauguration of CUSP in 1998.
Driving Education Excellence: CUSP’s Mission and Impact
CUSP’s work aligns closely with the values and priorities of the Hong Kong SAR Government’s education policies. Grounded in research and policy expertise, the Centre empowers schools to enhance teaching and learning, foster whole-person development, and strengthen professional capacity. Through tailored support and long-term relationships, CUSP helps schools to establish systems for continuous self-improvement, ensuring quality education to every student.
In recent years, CUSP has spearheaded several major projects commissioned by the Quality Education Fund (QEF), the Education Bureau (EDB), the Investor and Financial Education Council (IFEC), and other key stakeholders.
Building a Culture of Well-Being: The Positive Education Initiative
One of CUSP’s most impactful projects was the QEF-funded initiative, “Promoting Positive Education for Whole-Person Development” (2017–2022). This school network engaged over 200 kindergartens and nursery schools in implementing a school-wide approach to positive education. Inspired by the principles of positive psychology and the practices of Geelong Grammar School in Melbourne, the project focused on six key areas: purpose, engagement, emotion, relationships, accomplishment, and health.
The initiative empowered schools to cultivate a culture of well-being and adopt effective teaching strategies that support students’ holistic development.
Visit to Australian school as part of the “Promoting Positive Education for Whole-Person Development” initiative
Innovative Programmes Shaping Early Childhood and Financial Education
Another CUSP’s notable long-term project is the Play-based Learning Programme, funded by the QEF’s Designated Theme since 2015. This initiative has engaged over 300 kindergartens and nursery schools, introducing models inspired by Australia’s Early Years Learning Framework and the “Free Play” practices of Anji in Mainland China. The programme emphasises learning through play, provides training for school leaders, and fosters collaboration between schools and families to enhance the quality of early childhood education.
From 2018 to 2024, CUSP also led the “Wealth by Virtue – The Financially Literate Schools Programme”, commissioned by the IFEC. Working with 40 local primary schools, this initiative promoted financial literacy through a whole-school approach, guided by the Hong Kong Financial Competency Framework. It identified essential knowledge, skills, and attitudes for primary-level students while promoting five core values—prudence, self-discipline, integrity, diligence, and responsibility—to nurture responsible and value-oriented citizens.
Launch of “Wealth by Virtue – The Financially Literate Schools Programme”
Advancing Education Excellence: CUSP’s Legacy and Future
Since 2009, CUSP has collaborated with the Education Bureau to implement the Support Programme on Fostering Communities of Practice to Enhance Small Class Teaching. Drawing on the research of Professor Maurice Galton from the University of Cambridge, this programme supports teachers of Chinese, English, Mathematics, and General Studies. It also equips middle managers to foster professional collaboration, enhancing teaching and learning in small-class settings.
Beyond these major initiatives, CUSP actively conducts research on gifted education and organises enrichment programmes for Gifted and Talented students. The Centre also plays a pivotal role in early childhood leadership by offering certificate courses for kindergarten principals, equipping them to lead with vision and professionalism.
In 2023, CUSP celebrated its 25th anniversary, marking a significant milestone in its contribution to education in Hong Kong. Looking to the future, the Centre remains steadfast in its mission to integrate research with school development. By building strong partnerships with schools and delivering practical, research-informed support, CUSP continues to champion quality education and drive the advancement of Hong Kong’s education system.
Celebratory toast at the 25th Anniversary Banquet
Upcoming Celebratory Events
20-21/11/2025
The International Conference of AI X STEM Education
05-06/12/2025
CUHK China Education Forum - Education Innovation and Transformation in the AI Era cum MSA Conference 2025
08-12/12/2025
The ISSP 16th World Congress
Event Archives
QSIP School Leader Seminar 2025 — From Enhancing Self-Assessment to Promoting School Improvement
“Innovation with Heart” – Hong Kong Principal Forum 2025
Positive Education Symposium: Promoting Learning, Well-being, and Flourishing
International Conference on Mindfulness-Asia Pacific (ICM-AP) 2025
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