Master of Arts in Artificial Intelligence in Education

This course introduces learners to essential concepts and terminology in scientific research, emphasizing both quantitative and qualitative research paradigms. Learners will develop the ability to describe the process of conducting a research study, including its main steps. The course covers ethical considerations in educational research, measurement, and evaluation, including factors related to the psychometric properties of research tools. Additionally, it includes instruction on designing a research proposal using diverse quantitative and qualitative methods. Ultimately, learners will be equipped to evaluate educational research within the context of AI in Education by the course's conclusion.

This course provides an overview of learning theories and their application in instructional design. Participants will explore principles and strategies for effective teaching and learning, applying these concepts through assignments, reflective discussions, and group projects. The course emphasizes the integration of technology in instructional design and learning processes.

This course strives to empower students with a thorough understanding of curriculum development, emphasizing practical application in crafting subject-specific curricula. Through scientific methods, students will critically analyze and evaluate curricula, exploring foundational concepts in cognitive, psychological, social, philosophical, and technological dimensions. The curriculum encompasses key aspects, including the design and organization of curricula, planning, development, instructional evaluation, and the integration of Artificial Intelligence in shaping the future of education. Furthermore, the course delves into curriculum issues, including teaching methodologies, the integration of technology, and considerations for sustainable development.

This course will provide a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.). The course will demonstrate how these models can solve complex problems in various industries, including medical diagnostics, image recognition, and text prediction. In addition, various case studies and exercises will be used to explain the implementation aspects, besides teaching legal and ethical issues.

Introduction to data mining, its terminology, an overview of various types of data and their properties, an overview of different methods to explore and visualize large amounts of data, an introduction to classification methods, introduction to clustering methods, introduction to association analysis, and handling of personal integrity in the area of data mining. The course also includes data mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process with examples. The subjects are treated both theoretically and practically through laboratory sessions where selected methods are implemented and tested on typical amounts of data.

Prerequisite: Completion of 15 CH

The Thesis course is a core requirement of the Master of Arts in Artificial Intelligence in Education program. It aims to enable students to conduct an original, in-depth scholarly research study in a specialized area related to the application of artificial intelligence in educational contexts. Under the supervision of a qualified faculty member, students independently plan, implement, and document a rigorous research project that integrates educational theory with artificial intelligence technologies. The course emphasizes the development of advanced research skills, including problem identification, formulation of research questions, literature review, selection of appropriate research methodologies, data collection and analysis, and interpretation of findings. Students are also required to adhere to ethical standards and professional research practices relevant to education and AI-driven studies. The thesis culminates in a written dissertation that demonstrates the student’s ability to synthesize theoretical knowledge and empirical evidence, contribute meaningfully to the field of artificial intelligence in education, and communicate research outcomes effectively. Successful completion of the course requires the submission of the thesis and its formal defense before an academic committee.

Prerequisite: Completion of 15 CH

The course explores current literature and practices in elementary (K-8) classrooms. It aims to equip students with the knowledge and skills for effective teaching through differentiated instruction. The course covers essential elements of differentiated classrooms, the role of differentiated instruction, and strategies to meet diverse learning needs. Students will develop instructional tools, design inclusive assessments, and create reports on differentiated instruction plans, including objectives, evaluation strategies, and outcomes.

This course provides a thorough examination of instructional structures, the philosophy of active learning, and practical strategies to create an engaging classroom. Students explore "Designing for Active Learning: A Problem-Centered Approach" and acquire skills for fundamental teaching tasks. Emphasizing the efficient use of active learning strategies for class sessions, the curriculum covers evaluation methods and encourages learner autonomy through digital portfolios. The course culminates in students producing a concise research paper or report on an active learning strategy

In this course, learners explore diverse approaches to educational assessments, incorporating authentic and best practices from international contexts. The focus is on understanding principles and tools for quality assessments in teaching and learning. Participants develop practical skills for conducting assessments of learning and assessments for learning, empowering them to make informed, evidence-based decisions for transformative teaching and learning, all within the framework of ethical and inclusive practices.

This course explores the practical applications of advanced AI such as deep learning techniques in education. Students will learn state-of-the-art techniques for solving complex problems using deep neural networks. Topics covered include convolutional neural networks (CNNs) for computer vision tasks, recurrent neural networks (RNNs) for natural language processing, generative models for image and text generation, reinforcement learning for decision-making, transfer learning for leveraging pre-trained models, and ethical considerations in deep learning. By the end of the course, students will be equipped to apply deep learning algorithms effectively in real-world scenarios and contribute to advancements in AI.

This course will provide an in-depth exploration of the diverse applications of Artificial Intelligence (AI) tools in the realm of education. In this course, students will understand how AI technologies are reshaping and enhancing various aspects of modern education. Furthermore, the course will integrate diverse case studies and hands-on exercises. These real-world examples will illuminate the practical implementation aspects of AI tools in education. Students will gain theoretical insights and engage in exercises that simulate real scenarios, fostering a deeper understanding of how AI can be effectively applied in educational contexts.

This course provides an introduction to gamification and game-based learning concepts supported by artificial intelligence (AI) in educational contexts. It explores fundamental principles of AI, gaming, and gamification, and examines how these technologies can be integrated to enhance teaching, learning, motivation, and student engagement. Students will analyze real-world applications, design AI-supported gamified learning activities, and evaluate the effectiveness of game-based approaches in education. The course emphasizes practical skills, critical thinking, and ethical considerations related to the use of AI and games in learning environments.

This course explores the transformative potential of Virtual Reality (VR) and Augmented Reality (AR) technologies in educational settings. Students are exposed to the fundamental concepts, design principles, and practical applications of VR and AR in learning environments. Through a combination of pedagogical, social, and hands-on experience, students will learn to plan the development and evaluation of immersive educational content and tools that enhance teaching and learning outcomes.

This course aims to equip students with a foundational understanding of several social, ethical, and legal issues related to Artificial Intelligence (AI) as well as necessary knowledge to address the ethical and societal challenges arising from the use of AI. The course will establish the philosophical grounds for different ethical theories in conjunction with their implications on AI, including ethical and societal challenges of AI, its relationship to other disciplines and technologies, the capabilities of current AI applications with their potential ethical and societal challenges and evaluating key ethical and societal problems created by the use of AI using the ethical theories. Students will apply ethical theories to case studies in which ethical and societal ussies raised by AI.

This course provides an introduction on machine learning with application to education. It gives an overview of many concepts, techniques and algorithms in machine learning such as supervised, unsupervised, and reinforcement learning. It then shows their applications to education field. The course contents include introduction to AI and Machine Learning: definition, types, applications, etc. Regression: linear, multiple linear, polynomial, etc. Classification: logistic regression, KNN, Naïve-Bayes, and decision tree algorithm. Unsupervised Learning: K-means and DBSCAN algorithms. Reinforcement Learning: Q-Learning algorithm. Noting that in each chapter, a specific education use-case is addressed such as student performance and profiles, students’ study plans, career path, advisor selection for students, etc.