Designing Effective Learning Experiences in the Age of Generative Artificial Intelligence

The rise of generative artificial intelligence (AI) presents new opportunities and challenges for shaping learning experiences. This course examines how generative AI, when applied appropriately in the teaching and learning processes, may enhance instructional delivery, learner engagement and outcomes. Students explore generative AI's affordances for learning and education—its ability to generate personalized explanations, provide structured feedback, reason through complex topics to facilitate research and problem-solving, and create multimodal content. Building on these capabilities, students experiment with various generative AI-powered tools and platforms to design and redesign learning experiences, such as creating AI-powered chatbots, reimagining assignments and learning reflections, enhancing visual media for education, and gamifying learning to enhance comprehension and engagement. At the same time, this course considers how learning and education may evolve as generative AI becomes more widely available, while ensuring that effective pedagogical theories are reinforced. Students examine foundational learning science principles that become even more critical in an AI-enhanced learning environment, such as project-based learning, experiential learning, dual-coding theory, deep processing, and formative feedback. Students apply these evidence-based strategies to create effective learning designs that foster deep learning and critical thinking. Students also consider the broader implications of AI in education, including ethical considerations, implementation challenges, and the important and evolving role of educators to ensure any technology is used in a responsible way to support and enhance meaningful learning experiences. This course is designed for educators, instructional designers, and learning professionals with little to no experience in generative AI for learning design. It is intended for those building foundational knowledge rather than advanced practitioners. By the end, students have built AI-enhanced learning prototypes, analyzed real-world applications, and thoughtfully considered AI's role in teaching and learning.