Over the past several posts, I have explored how AI can support learners at different moments of need. The series began with a simple premise: AI should not be used simply because it is available. It should be used when it helps learners do meaningful learning work. That distinction matters. It is easy to start […]
Challenged by Change When Learning In the previous post about helping students work through problems during their learning experience, I explored how AI can support learners when they encounter problems, uncertainty, and unexpected challenges. Those moments often arise when something does not work as expected, and students must diagnose, interpret, and work through difficulty. But […]
When Understanding Is Not Enough In the previous post, I focused on helping students apply what they know. That is an important step in learning because understanding alone is not enough. Learners eventually need to use ideas, methods, and tools in real situations. But application does not always go smoothly. Sometimes students begin using what […]
For decades, educators have primarily evaluated the quality of the final product. We assess essays, projects, exams, presentations, portfolios, and discussions by asking, “How well did the student perform?” Performance remains important and should remain important. Academic quality, accuracy, sophistication, and achievement of learning objectives are central responsibilities of teaching and assessment. However, contemporary learning […]
When Understanding Is Not Enough In the previous post, I focused on helping students build understanding. That is an essential moment in learning, but it is not the end of the learning process. Students may understand an idea well enough to explain it and still struggle when asked to use it in a real situation. […]
In the previous post on using GenAI to help students who are just getting started on their learning journey, I focused on helping students get started at the very beginning, often trying to figure out what to do (to begin learning), and how to do it.. That moment matters because students cannot engage deeply with […]
In every course I’ve taught, whether as a high school teacher, an undergraduate instructor, or working with graduate students, there is a predictable pattern in the first few days. Some students get started right away. They understand what the course is about, what is expected of them, and how to begin. Others are less certain. […]
In the first post in this series, I introduced the idea of AI in the Moment of Learning Need (AIMON) framework as a way of thinking about AI not as a general-purpose tool, but as a form of support that should be aligned to specific points in the learning process. That shift from tool to timing […]
This post is the first in an eight-part blog series exploring AI in the Moment of Learning Need (AIMON – I treat the “L” as invisible). Over the next several weeks in the HyFlex Learning Community blog, I’ll introduce the core framework, share the design principles behind it, and illustrate how it can support teaching and learning through […]
Designing a HyFlex course is only half the story. Implementation is where plans meet the complex realities of institutional systems, technology, schedules, and people. Even the most thoughtful HyFlex design can fall short without coordinated planning across multiple layers of support, from faculty readiness and student communication to infrastructure and policy alignment. The HyFlex Planning […]