人工智能与教育科技理学硕士

This course aims to equip students with the foundational and advanced knowledge of artificial intelligence coupled with an emphasis on its principles and practices in the educational setting. It also provides opportunities for students to analyse the impacts of artificial intelligence on education, and to examine its ethical and social issues. This course discusses both contemporary and emerging technologies of artificial intelligence, including but not limited to intelligent agents, problem solving, knowledge and reasoning, computer vision, robotics, natural language processing, chatbots, voice assistance, and affection detection. Frameworks for integrating education and artificial intelligence, emerging applications of artificial intelligence in education will be introduced. Ethical and social issues in artificial intelligence applications and development will be discussed.

This course provides an overview of the instructional design frameworks, principles and pedagogical models supported by current and emerging technologies. It also explores and evaluates innovative pedagogical designs with technologies consistent with social constructivist principles and frameworks to optimise learning. Students are provided with the opportunity for hands-on practice in designing and evaluating innovative learning environments leveraged by Artificial Intelligence (AI) and educational technology.

This course aims to develop students’ understanding of the principles of research design and methods in the field of Artificial Intelligence (AI) and educational technology. It is designed to enable students to develop their own research proposal to investigate empirical issues in AI and educational technology with the key components of literature review, statement of problems, and research design with appropriate methods.

Deep Learning is one of the latest trends in Machine Learning and Artificial Intelligence to model how the human brain works. Deep Learning methods have brought revolutionary advances in Machine Learning. This course provides students with the foundations of artificial neural networks, followed by the theories, principles and practices for building Deep Artificial Neural Networks (i.e. Deep Learning) to solve real-world problems based on empirical data. During the course, students will understand the applications of Deep Learning in various fields, particularly in education. The design and implementation of one kind of deep neural network called Convolutional Neural Network (CNN) will also be covered.

This course begins with a review of the knowledge and skills of computational thinking and its role in developing advanced and future technology. The important role of coding and computational thinking as an integral part of STEM (Science, Technology, Engineering and Mathematics) education and the rationale behind will be critically examined. It then discusses strategies for learning coding from various perspectives to develop students’ computational thinking within the context of STEM education. The course will provide hands-on practices of using coding and computational thinking to address authentic problems and real-life scenarios in relation to STEM. Participants will be introduced to a variety of teaching and learning approaches to use coding to develop computational thinking, with the effectiveness of these approaches critically examined. They will also be led to further explore issues related to the design and practice of coding pedagogies, and how coding and computational thinking could be linked with other STEM disciplines to design integrated STEM learning activities in school curricular contexts.

This course provides an overview of data mining and the fundamental concepts of STEM education. Data mining is increasingly being used to improve teaching and learning process and educational pedagogy. Teachers can use the discovered knowledge from data mining models to solve educational problems. This course covers data preprocessing, data visualization, probability and statistics for establishing the algorithms for association, classification and clustering. It also covers the concepts of STEM education for students to design STEM learning activities and discuss the social and moral issues related to STEM education. Some examples of data analytics in STEM applications are presented.

The Internet of Things (IoT) is a system of connected smart devices providing rich data over a network. It provides advanced data collection, connectivity, and analysis of information collected by smart devices with the concepts of machine-to-machine communication. This course aims to provide students with a solid foundation in the IoT, including the components, tools, and analysis by teaching the concepts behind the IoT and a glimpse of real-world applications. Students will learn the IoT technologies in designing and implementing solutions for real-world problems. A hands-on approach to prototyping IoT products and applications will be adopted.

This course provides students with opportunities to apply and extend their knowledge and skills developed in the programme to their own chosen area of specialism related to artificial intelligence (AI) and educational technology with two options: (1) planning, conducting and reporting a small-scaled study with appropriate research methods related to AI and educational technology; or (2) planning and producing a workable instructional solution leveraged by AI and educational technology with a report.

This course critically reviews research on creative multimedia and design within design and technology domains. Contents like multimedia production, 2D and 3D animation design, computer graphics, and visualization techniques for data applications will be covered in the course. The course will explore innovative multimedia design including graphical design, virtual and augmented reality, and artistic or scientific visualization with applications in design and educational trainings. The course provides students with overall understanding of innovative and practical knowledge for expressive design and creative media sciences. Research methodologies on the key concepts of multimedia design and visual sciences will also be discussed.

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This course begins with a review of the major components in the infrastructure of the metaverse, namely: internet infrastructure (e.g., 5G/6G networking, real-time graphics), spatial computing (e.g., 3D mapping, VR/AR), creator economy (e.g., blockchain, non-fungible tokens) and immersive experiences (e.g., socializing, games, education). The course will then explore application scenarios of the metaverse in education and society, with a focus on understanding the roles that core technologies play in each of those scenarios. Towards the end, there will be a discussion on how the metaverse might enhance learning and communication, increase accessibility, gamify human experiences and bring up huge economic benefits.

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Mobile digital devices have become popular nowadays. It can provide an excellent educational platform for information and communication technology. This course introduces students to important concepts and aspects in mobile application development. It aims to equip students with the knowledge and skills to develop mobile applications. It also offers opportunities for students to examine the use of information and communication technology to promote IT education and teaching of sciences, technology, engineering, and mathematics education. Through hands-on practical activities, students will apply mobile platform programming skills in their own mobile application development to promote playful teaching and learning environments.

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* (will not be offered in 2023/24 academic year)

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This course aims at introducing students to the basics of statistics, including standard probability distributions, sampling distributions, parameter estimations, inference and statistical decision based on hypothesis testing. This course provides an introductory overview of probability and statistics. The basics of random variables are introduced. With these basics in place, concepts of sampling distributions and techniques of data analysis and hypothesis testing are then introduced and discussed.