AIED Courses - Master's and Leadership Programmes

Course Code	Course Title	Course Synopsis
MSL906 (under Master of Science in Science of Learning)	Education at the Intersection of Artificial Intelligence and Neuroscience	The human brain is the best example of intelligence known, with unsurpassed ability for complex, real-time interaction with a dynamic world. At the same time, developments in AI are yielding benefits for neuroscientific research. Patterns identified from neural networks can illuminate computations enacted by the biological brain, functioning both as a model for developing and testing ideas about how the brain performs computations. Conversely, brain-activity recordings can be fed to an artificial neural network and tasked with learning how to reproduce the data, functioning as a tool for processing complex data sets that the Science of Learning research field is generating. This course will explore cycles of mutual reinforcement between neuroscientific data and artificial neural networks to obtain further insights into how computation works in the brain, and how machines that can take on more human-like intelligence to advance understanding for how a learner develops. Specifically, the course will focus on unexplored spaces at the intersections of neural AI, symbolic AI, brain science and cognitive science. Takeaways include implications for education and how cutting edge teaching and learning methodologies harnessed from AI and SoL fields may be developed.
MSL903 (under Master of Science in Science of Learning)	Learning Analytics for Science of Learning	Learning analytics is an emerging field of study that has been gathering broad interests in educational research and practices; recent research has harnessed the power of learning analytics to enhance understanding of learning processes. Learning analytics can be a game-changer that creates more effective learning environments by providing useful insights that help us to understand, visualize and predict learners' performance, provide learners with personalized learning, and increase retention and success rates.
MLT916 (under Master of Education in Learning Sciences and Technologies)	Learning Analytics for Educational Practitioners	This course is designed for educational practitioners, particularly MEd (LST) students, who are interested in the theoretical foundations and applications of learning analytics across educational areas (e.g., primary to tertiary students, adult-learning). Activities will include in-class discussions, designs, and presentations, all of which are intended to help students build up a strong foundation in understanding the theoretical and educational use of learning analytics for teaching and learning. Participants will be able to engage with theories presented in the readings as well as to connect learned content to their own teaching practices; guided with learning from case studies and reviews. The course will help to enhance learners educational data literacy as well as their assessment literacy.
MLT917 (under Master of Education in Learning Sciences and Technologies)	Artificial Intelligence for Education: A Pedagogical Spectrum	Modern artificially intelligent systems for education (AIED) embody various pedagogical models to scaffold participants’ learning, each of which holds different implications for how the technology is designed and what kind of data is generated. This seminar-based course will showcase concrete use-cases of AI systems for education that are aligned with pedagogies such as mastery, inquiry learning, collaborative learning, socio-emotional learning, embodied learning, etc, illustrate how they work and what their limitations are. By showcasing how these different learning possibilities can be created with AI-enabled technology, the course will enculturate participants into the practice of teaching with technology for active learning to create more participatory, connected and reflective classrooms. Taken together, in strong alignment with MOE’s most recent EdTech masterplan 2030, this course will strengthen participants’ proficiency in e-pedagogies and their know-how of cutting-edge practices for creating and critiquing technology-enabled learning experiences drawing on artificial intelligence for education.
MLS 3107 (under Management and Leadership in Schools In-service program)	Leadership in Artificial Intelligence for Education	This course aims to develop middle leaders’ leadership in choosing and using AI and educational technology tools, contributing to appropriate instructional tool selection, guidance in implementation, and respecting student needs, privacy, and other concerns. This is accomplished through meeting several objectives. First, basic understandings of AI are established. Second, the use of AI for education over the past fifty years will be reviewed. Third, we provide a discussion of the ethical issues of the use of AI for education, Fourth, course attendees link the affordances of AI with their pedagogical knowledge and experiences to apply it to design an action plan for their schools, which may include components of fostering AI literacies, and selecting AI systems for education that are beneficial for students. Thus, this course provides an overview of the background and application of AI for education. Different AI perspectives and approaches in reshaping education will be introduced, discussed and reflected. Course attendees will develop a basic understanding of AI technologies and its application to enhancing learning environments and experiences. Course assessment is structured to provide maximum learning to course attendees.