Introduction
Events
Introduction
China and France issued a joint declaration on AI governance in May 2024. This declaration strengthens the bond between these two nations and sets a precedent for future AI collaborations between China and other European countries. Against this background, the knowledge and insights should be disseminated to promote AI literacy to the general public.
The proposed initiative aims to promote AI literacy and cultural exchange between the public in France and Hong Kong. French educational systems are renowned for their emphasis on philosophical inquiry, which is crucial in cultivating thoughtful individuals who can maintain their unique human qualities in an AI-infused society. The discussions on ‘human-centered’ AI-Human Partnership are fundamental to creating a world where AI may surpass human abilities without diluting the distinctive traits that define our humanity.
This travel for knowledge exchange and dissemination will be the extension of the ongoing project ‘Uncovering the “Black Box” of Machine Learning: Promoting Artificial Intelligence Literacy with AlphAI robots in Senior Primary/Junior Secondary Schools across Hong Kong and France’, which is supported by the Central Reserve Allocation Committee of The Education University of Hong Kong. This ongoing project will facilitate teacher professional development using AlphAI learning robots developed by French collaborators.
Research Objective(s):
1. To strengthen the academic collaboration between France and Hong Kong.
2. To exchange best practices in AI literacy education in K-12 settings and explore innovative approaches to teaching and curriculum development that enhance AI literacy among students.
3. To disseminate the knowledge and insights gained in the ongoing collaboration to promote AI literacy to the general public.
4. To foster dialogues on AI-human partnership across educational communities in France and Hong Kong.
5. To establish a framework for cross-cultural knowledge exchange and capacity-building in AI education and research.
AEER primary and secondary school teachers interview
AEER 5 Students for AIphAI course
PROCORE 2026
教師專業進修課程證書（小學教育的人工智能）——運用 AlphAI 機器人學習機器學習後的教師訪談
Events
Seminar on Unleashing Human Potential: an Artificial Intelligence Competency Framework for School Education
25 March 2026
This seminar talks about a three-component framework—Understanding, Using, and Unleashing—to guide AI integration into teaching and learning. The ‘Understanding’ component cultivates a conceptual understanding of AI, while ‘Using’ emphasizes practical and cognitive skills of using AI to enhance learning. ‘Unleashing’ highlights AI’s potential to empower personal growth, and fostering a spiritual self capable of making value-based decisions and do good. This framework aims to prepare learners to contribute meaningfully to future society. Schools must teach students to use AI for empowerment and good, avoiding mere reliance or shortcuts. Through practical activities, education should guide learners to unleash their potential and harness technology for spiritual self-development.
Seminar on Learning to Learn Through Artificial Intelligence Techniques: Metacognitive Impacts of Digital Teaching and Machine Learning in Elementary School
17 December 2025
Artificial Intelligence (AI) and Machine Learning education is entering the classroom, and yet the link between its introduction and the development of metacognition in students – i.e. the ability to understand and control their own learning processes – remains little explored. Therefore, our project investigates how teaching digital technology and machine learning can promote the metacognitive development of students aged 8 to 11. Two studies using AlphAI, a learning robot with a graphical interface, explored students’ understanding of artificial intelligence (AI) and the comparison between human and machine learning, as well as the transfer of these concepts to problem solving. Combined, the results demonstrate an improvement in students’ knowledge of AI, their metacognitive beliefs and behaviors, and their problem-solving strategies and performance. There has also been a shift in teaching practices, with increased integration of digital technologies and trial-and-error learning.
Seminar on Teachers’ Perspectives on Teaching Machine Learning to Senior Primary/Junior Secondary Students
12 November 2025
We present in this talk a machine learning course developed for Hong Kong senior primary/ junior secondary schools, designed to introduce foundational concepts—including supervised and reinforcement learning methods and algorithms such as artificial neural networks and K-nearest neighbours—through hands-on activities with AlphAI robots. This study reported that sixteen teachers from five schools provided insights through focus group interviews after participating in a teacher development workshop and implementing the 6–8-hour course with students aged 11-13 years. An analysis of the interview data revealed that the course provided a clear and effective pedagogical framework, guided by the Attention-Engagement-Error-Feedback-Reflection (AEER) model, which teachers found particularly useful for lesson planning and instruction.
Seminar on Building a Curriculum that Uses Robots to Teach AI Algorithms in a Friendly and Intuitive Way
27 September 2025
We have developed the AlphAI software and robots to teach AI algorithms in a tangible way from Elementary school, by letting kids train their own Machine Learning model that drives the robots, and showing algorithmic details in our graphic interface. In addition to this great tool, we needed to build a Curriculum with a well-chosen sequence of lessons to let kids understand step-by-step “how AI works”. In this seminar I will present the activities co-developed with the Education University of Hong-Kong, which are an evolution of a first version that was tested during a year of experimentations in both Hong-Kong and France. We chose to focus on a specific configuration of the robots, where the AI processes the robots’ camera image reduced to only two super-pixels: this configuration is simple enough to follow all the details of AI calculations, and rich enough to enable playful activities such as “autonomous robot races”. This allows studying in great details a succession of AI algorithms: K nearest neighbors, artificial neural networks, first without, then with hidden neural layers, and finally reinforcement learning.