AI for Course Design (Coursera)

AI for Course Design (Coursera)

AI for Course Design is designed for instructional designers and educators and focuses on practical skills in working with generative AI for course development. Throughout the course, you'll learn to define and differentiate between AI concepts, navigate various AI models, and utilize AI tools for course creation. The course also covers ethics and limitations of AI in education, enabling you to effectively incorporate AI into your teaching methods.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

What you'll learn

  • Articulate clear definitions and differentiate between Large Language Models (LLMs), Artificial Intelligence (AI), and Generative AI
  • Identify strategies for employing artificial intelligence to design courses and create course content
  • Describe the benefits, challenges, and ethics implications associated with the integration of Generative AI in education

Syllabus

Embarking on the AI Journey: AI Expedition Kickoff
Welcome to Week 1: "Embarking on the AI Journey: AI Expedition Kickoff!" We're thrilled to have you on board. In this first week, we'll dive into the exciting world where education meets cutting-edge technology. You'll get to know our instructors, the design of the course, and set the stage for a collaborative and supportive learning experience. Then, we'll explore the fascinating landscape of Artificial Intelligence in education. Finally, we'll work to understand how AI works and the different types of AI, with particular focus on Generative AI. Throughout the course, we hope you engage in conversations with your fellow learners, share your thoughts, and challenge some of the ideas in this course.

Charting Creative Frontiers with AI in Course Design
Welcome to Week 2: "Charting Creative Frontiers with AI in Course Design." This week we'll be exploring the synergy between creativity and technology. As we delve into the diverse realm of AI tools, you'll gain the expertise to critically assess and strategically select the most fitting ones for your distinct course design objectives. At the end of the week, we'll practice and reflect on developing course materials using two different Generative AI tools.

Navigating the Realities of Using AI for Teaching and Learning
Welcome to Week 3: "Navigating the Realities of Using AI for Teaching and Learning." This week, we'll explore the benefits, challenges, and ethics implications for the integration of generative AI in education. We begin by reflecting on Week 2 and the benefits and creative possibilities of AI's role in course material creation, accessibility enhancement, and student engagement. Then, we turn towards the complexities, challenges, and ethical considerations, ensuring a responsible and strategic approach to AI integration. We'll end the week with valuable insights into the learner's perspective, reflecting on how students leverage AI and discussing effective strategies.

Future Horizons: Embracing the Next Wave of AI in Course Design
Welcome to the final week of the course, Week 4: "Future Horizons: Embracing the Next Wave of AI in Course Design." As we embark on this closing chapter, let's celebrate the incredible journey we've shared, exploring the dynamic intersection of education, course design, and AI. In these concluding lessons, we reflect on the enduring collaboration between educators and AI, recognizing the evolving landscape of teaching and learning.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Technologies and platforms for Artificial Intelligence (Coursera) Coursera
Politecnico di Milano

Technologies and platforms for Artificial Intelligence (Coursera)

This course will address the hardware technologies for machine and deep learning (from the units of an Internet-of-Things system to a large-scale data centers) and will explore the families of machine and deep learning platforms (libraries and frameworks) for the design and development of smart applications and systems.

Jun 22nd 2026
4 Weeks
Human Factors in AI (Coursera) Coursera
Duke University

Human Factors in AI (Coursera)

This third and final course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the critical human factors in developing AI-based products. The course begins with an introduction to human-centered design and the unique elements of user experience design for AI products.

Jun 22nd 2026
4 Weeks
AI Capstone Project with Deep Learning (Coursera) Coursera
IBM

AI Capstone Project with Deep Learning (Coursera)

In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.

Jun 22nd 2026
4 Weeks
AI Workflow: Business Priorities and Data Ingestion (Coursera) Coursera
IBM

AI Workflow: Business Priorities and Data Ingestion (Coursera)

This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites.

Jun 22nd 2026
2 Weeks
Scalable Machine Learning on Big Data using Apache Spark (Coursera) Coursera
IBM

Scalable Machine Learning on Big Data using Apache Spark (Coursera)

This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer.

Jun 22nd 2026
4 Weeks
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (Coursera) Coursera
DeepLearning.AI

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (Coursera)

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

Jun 22nd 2026
4 Weeks
AI Workflow: Machine Learning, Visual Recognition and NLP (Coursera) Coursera
IBM

AI Workflow: Machine Learning, Visual Recognition and NLP (Coursera)

This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company.

Jun 22nd 2026
2 Weeks
AI Fundamentals for Non-Data Scientists (Coursera) Coursera
University of Pennsylvania

AI Fundamentals for Non-Data Scientists (Coursera)

In this course, you will go in-depth to discover how Machine Learning is used to handle and interpret Big Data. You will get a detailed look at the various ways and methods to create algorithms to incorporate into your business with such tools as Teachable Machine and TensorFlow. You will also learn different ML methods, Deep Learning, as well as the limitations but also how to drive accuracy and use the best training data for your algorithms.

Jun 22nd 2026
4 Weeks
Machine Learning Introduction for Everyone (Coursera) Coursera
IBM

Machine Learning Introduction for Everyone (Coursera)

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more.

Jun 22nd 2026
3 Weeks
Evaluations of AI Applications in Healthcare (Coursera) Coursera
Stanford University

Evaluations of AI Applications in Healthcare (Coursera)

With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

Jun 22nd 2026
5-12 Weeks
Artificial Intelligence: An Overview (Coursera) Coursera
Politecnico di Milano

Artificial Intelligence: An Overview (Coursera)

The course will provide a non-technical overview of the artificial intelligence field. Initially, a discussion on the birth of AI is provided, remarking the seminal ideas and preliminary goals. Furthermore, the crucial weaknesses are presented and how these weaknesses have been circumvented. Then, the current state of AI is presented, in terms of goals, importance at national level, and strategies. Moreover, the taxonomy of the AI topics is presented.

Jun 22nd 2026
5-12 Weeks
Deep learning in Electronic Health Records - CDSS 2 (Coursera) Coursera
University of Glasgow

Deep learning in Electronic Health Records - CDSS 2 (Coursera)

Overview of the main principles of Deep Learning along with common architectures. Formulate the problem for time-series classification and apply it to vital signals such as ECG. Applying this methods in Electronic Health Records is challenging due to the missing values and the heterogeneity in EHR, which include both continuous, ordinal and categorical variables. Subsequently, explore imputation techniques and different encoding strategies to address these issues. Apply these approaches to formulate clinical prediction benchmarks derived from information available in MIMIC-III database.

Jun 22nd 2026
4 Weeks