*The programme will be launched during the academic year 2026/2027 and become operational upon its publication in the Official Gazette of the Macao SAR.

1. Programme Overview

The Master of Science (MSc) in Artificial Intelligence and Education Programme aims to provide students with foundational knowledge and skills in using artificial intelligence (AI) to improve teaching and learning effectiveness with ethical awareness, to integrate AI into school curriculum and practices, and to apply AI in school administration and management and inform policy-making in education.

Upon completion of this programme, students are expected to be able to:

PILO-1: Articulate knowledge and understanding of concepts and tools in artificial intelligence (AI).
PILO-2: Apply AI knowledge and skills to enhance student engagement and learning outcomes.
PILO-3: Integrate AI technology into school curriculum design and assessment practices.
PILO-4: Use appropriate methods of AI technology in school leadership and management.
PILO-5: Identify the ethical and social implications of AI and use critical skills in ethically analyzing AI in educational settings.

To be announced

Following the general admission rules and requirements of the University.
Applicants with an academic background in education are welcome to apply. Those with a background of computing science, information and communication technology, mathematics, engineering or data science who desire to join the education industry are also welcome to apply to receive further training in this programme.

The duration for the MSc in Artificial Intelligence and Education Programme is 2 years. During this period, the student needs to take 5 compulsory courses (including project report) and 4 required elective courses. Then he/she will be awarded the degree of Master of Science in Artificial Intelligence and Education after completing the programme of study.

 

Course Code Course Type Total Lecture Hours Credits
New Introduction to Artificial Intelligence Compulsory 45 3
New Artificial Intelligence and School Curriculum Compulsory 45 3
New Artificial Intelligence for Educational Measurement and Assessment Compulsory 45 3
New Artificial Intelligence for School Leadership and Management Compulsory 45 3
New Project Report Compulsory — 6
Students are required to take 4 required elective courses from the following courses to obtain 12 credits:
New Applications of Machine Learning in Education Required Elective 45 3
New Artificial Intelligence and Data Analytics Required Elective 45 3
New Current Issues in Artificial Intelligence and Education Required Elective 45 3
New Generative AI in Teaching and Learning Required Elective 45 3
EDUC7851 Design of Digital Learning Environments Required Elective 45 3
EDUC7854 Emerging Technologies for Learning and Assessment Required Elective 45 3
Total number of credits required 30

Introduction to Artificial Intelligence

This foundational course covers key concepts, and theoretical underpinnings of artificial intelligence (AI). Students will be introduced to the definition of AI and the basics of generative AI by exploring the impact, applications, and use cases of AI and how it is transforming our lives. Additionally, students will learn about the core concepts of AI, covering machine learning, neural networks, and deep learning and explore generative AI models, including large language models (LLMs), and their capabilities. As part of the course, students will gain hands-on experience using AI tools and platforms to implement AI applications, enabling them to apply theoretical knowledge to practical, real-world scenarios.

Artificial Intelligence and School Curriculum

Artificial intelligence (AI) has the potential to transform education through a dynamic teacher-AI-student relationship. This shift requires changes in school curricula and practices. This course aims to engage students with the knowledge and skills for integrating AI in curriculum planning and design with the eventual goal of optimizing student learning experiences. The complexity of incorporating AI in school curriculum will be examined through common curriculum planning approaches, including purpose, content, process, product and ethical considerations. In addition, participants will study theoretical frameworks and delve into arguments and challenges associated with existing AI integration approaches.

Artificial Intelligence for Educational Measurement and Assessment

This course introduces students to various educational assessment strategies by integrating Artificial Intelligence (AI) into assessment practices. This course is tailored for educators across primary, secondary, and vocational schools, as well as adult education professionals. Students will delve into both summative and formative assessment techniques, leverage AI to enhance learning outcomes and streamline evaluation processes, and master theoretical knowledge and practical skills to implement innovative assessment methods that align with modern educational standards.

Artificial Intelligence for School Leadership and Management

This course is designed for school leaders, administrators, and educators who aim to harness the potential of Artificial Intelligence (AI) to drive meaningful transformation in education. Throughout the course, participants will explore practical methods for incorporating AI into their leadership roles, fostering learning environments that are not only digitally advanced but also inclusive and sustainable. They will also learn how AI can be used to enhance decision-making and improve problem-solving. Additionally, the course will show how AI-driven strategies can support teaching quality and overall organizational efficiency.

Project Report

Students are provided with an opportunity to build on interests developed in this program through undertaking an individual project normally of 8,000 words by independent study. Examples of possible projects are a critical literature review of an issue or topic of significance in artificial intelligence and education, a small-scale research project on applying AI tools and applications in K-12 schools, a relevant professional development activity for K-12 schoolteachers who are likely to be users of AI in their classrooms, a reflection on AI-based or AI-enhanced curriculum and material development in a particular instructional context, a case study of how AI enhances school management or any other project in this area. Each student will be assigned an academic advisor that supports their independent project.

Applications of Machine Learning in Education

This course covers the foundations of machine learning, artificial neural networks, deep learning models and natural language processing, with a strong focus on their applications within educational contexts. Students will develop a comprehensive understanding of various architectures and algorithms, with hands-on experience in implementing machine learning to address real-world educational issues, such as personalized learning in different learning contexts (e.g., math, writing and etc.), student performance prediction, and multimodal learning analytics. In addition, students will develop skills to critically assess these approaches and examine their ethical and societal implications.

Artificial Intelligence and Data Analytics

Artificial intelligence and data analytics provide the power to analyze and learn about large amounts of data from multiple sources and detect patterns to make future trend predictions. The education industry benefits from predictive analytics to make classroom teaching and learning decisions, school management and policy making. Hence, this course aims to introduce students to the foundational concepts of data analytics and their use in AI, to enable students to gain the background knowledge necessary to understand and develop different algorithms in AI and data analytics, and the concepts of data-driven decision-making and their use in AI.

Current Issues in Artificial Intelligence and Education

The course takes the form of a seminar wherein students read, think and analyze the growing importance of artificial intelligence (AI) in education and its impact on students from a cognitive, psychological, sociological perspective. From a psychological perspective, relevant topics include how AI affects students’ motivation, self-control, and learning strategies. The sociological perspective focuses on the transformation of social interactions between students and educators and on the limits of accessibility to education through digitalization. The ethical and social challenges associated with AI 21 education are also discussed.

Generative AI in Teaching and Learning

This course explores how Generative AI can enhance teaching and learning by personalizing instruction, optimizing engagement, and automating routine tasks. Students will examine generative AI tools, such as Copilot, ChatGPT, and etc. that help create adaptive learning experiences, promote classroom interactions, and address ethical considerations. Through hands-on projects and case studies, participants will gain practical skills in AI integration, design effective AI-based activities, and foster a deeper understanding of how AI applications transform teaching and learning.

EDUC7851 Design of Digital Learning Environments

This course immerses students in theories, research, and practical methodologies pivotal for crafting innovative multimedia, online, and blended learning environments. Emphasizing a hands-on approach, it delves into the dynamic intersection of educational technology, multimedia elements, and instructional design theories. Students engage in practical exercises to apply these concepts, honing skills for designing and implementing impactful digital learning experiences.

EDUC7854 Emerging Technologies for Learning and Assessment

This course empowers students to lead the charge in educational innovation by delving into the transformative capabilities of emerging technologies for learning and assessment. Students actively engage with emerging technologies for learning and assessment, evaluating their efficacy and determining their suitability for diverse learning environments. They develop implementation strategies specific to these technologies, gaining a nuanced understanding through immersive, project-based learning experiences. Students pilot their innovative ideas within practice or research-based educational contexts, fostering both critical evaluation and practical application of these technologies for learning and assessment.

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