Designing engagement is both the most challenging and the most rewarding part of creating a HyFlex course. Learner choice, equivalency, reusability, and accessibility all matter deeply here, because students’ experiences are shaped not only by what content they encounter, but also by how they interact with each other, their instructor, and the broader learning environment.
The HyFlex Planning for Engagement GPT was developed to help instructors and designers work through this critical stage of course design. Like the earlier GPTs in this series, it provides a structured conversation that surfaces challenges, helps you weigh options, and generates practical strategies for creating engagement opportunities that are meaningful, equitable, and sustainable across all modes.
Engagement Across Multiple Dimensions
When thinking about engagement in HyFlex, it helps to break it down into a few key dimensions:
1. Student-to-Student
Peer interaction is often cited as one of the most valuable parts of learning, yet it is difficult to design consistently across modes. In-person students may thrive in breakout groups, while asynchronous students may feel isolated if their “discussion” space is too quiet. The Engagement GPT can help you map out equivalent activities across modes, for example, planning a synchronous breakout activity that is mirrored in a structured online discussion or small-group project space, ensuring all students have chances to connect and learn from one another.
2. Student-to-Instructor
Feedback and presence are vital in HyFlex. Instructors often struggle to maintain equitable visibility across modes, especially when most students show up in one format. The GPT can help you identify practical approaches, such as scheduling rotating office hours across formats, designing a system of timely feedback that reaches all students, or weaving short video or text announcements into asynchronous spaces to maintain instructor presence.
3. Student-to-Content
Engagement with content must go beyond passive consumption. The GPT helps you plan active strategies, such as case studies, problem-based tasks, or low-stakes quizzes, that are accessible whether students are participating live or asynchronously. For example, a live “think-pair-share” discussion can become an asynchronous reflection post, both serving the same pedagogical goal of prompting students to process and apply ideas actively. Using custom GPTs can also greatly increase the amount of student-content interaction available for students in all modes of the course.
4. Student-to-Community
Authentic learning often connects students to communities beyond the classroom, but in HyFlex, this raises questions of access and equity. The GPT encourages you to think about designing projects that allow multiple entry points: some students might attend a live community event or service project, while others engage through online interviews or digital storytelling projects. The goal is to create authentic engagement options that are flexible without losing meaning.
Addressing Common Challenges
Designing for engagement across modes also brings real challenges. These commonly include:
- Balancing synchronous and asynchronous participation. The GPT helps you identify where equivalent (but not necessarily identical) activities can serve the same learning goals.
- Ensuring equity across modes. It prompts you to check whether one mode consistently offers “richer” experiences and to brainstorm ways to strengthen other modes.
- Choosing tools and technologies wisely. The GPT can suggest common platforms or tools that work well in both synchronous and asynchronous environments, reducing friction for learners.
- Managing faculty workload. It helps you streamline engagement design by reusing activities or artifacts across modes and by setting realistic expectations for your role as instructor.
A Vignette: Jordan Plans for Engagement
Jordan, a faculty member developing their first HyFlex course, has reached the engagement stage of planning. In their traditional classes, Jordan relies heavily on in-class discussion and group work. They worry that asynchronous students will miss out on this key aspect of learning.
Using the HyFlex Planning for Engagement GPT, Jordan explores options for creating consistent interaction across modes. The GPT suggests structuring weekly discussion prompts that can be used both for in-class small groups and in an online forum. It also guides Jordan to think about rotating “peer facilitator” roles, so that both in-person and asynchronous students take turns leading. Finally, it recommends embedding instructor “touch points” (short, regular announcements and feedback loops) that keep students connected across time and space.
By the end of the planning session, Jordan feels more confident that their HyFlex course will provide every student, no matter how they attend, meaningful opportunities to engage, connect, and learn together.
The Story Continues…. A Colleague’s Lab Challenge
Jordan’s colleague, Riley, teaches in the sciences and is responsible for both a large lecture section and a smaller, hands-on lab section. Riley has been intrigued by the flexibility of HyFlex but is uncertain how it could work in a course where direct, physical participation in lab activities is so central. “How can students engage in a lab if they’re not in the room?” Riley wonders.
Turning to the HyFlex Planning for Engagement GPT, Riley begins exploring possibilities. The GPT prompts them to consider which aspects of lab engagement are essential to preserve (such as handling equipment, practicing experimental techniques, and working with lab partners) and which aspects could be adapted or mirrored in other formats (such as analyzing data, writing lab reports, or watching demonstrations).
Through the guided planning conversation, Riley discovers several feasible options:
- Synchronous Remote Participation: Students could join lab sessions live via video feed, observing demonstrations and collaborating in real time with on-site lab partners.
- Asynchronous Engagement: Students unable to attend in person could work with recorded demonstrations and shared datasets, applying analysis skills and contributing to group reports.
- Hybrid Lab Teams: Lab groups could be structured to include both in-person and remote students, with responsibilities divided so all contribute meaningfully.
The GPT also prompts Riley to think about feasibility: Does the local lab space have the right technology to support video streaming? Will safety protocols allow remote students to substitute data analysis for physical lab activities? By asking these questions early, Riley is better prepared to assess whether a HyFlex design for the lab course is realistic at their institution.
For Riley, the Engagement GPT doesn’t provide a one-size-fits-all solution, but it does help them discover what’s possible, weigh options, and begin planning toward a design that supports meaningful engagement in both the lecture and lab components of the course.
Moving Forward
Engagement is at the heart of effective HyFlex teaching. The HyFlex Planning for Engagement GPT is designed to support instructors in thinking through the complexities, identifying practical strategies, and creating an intentional plan that balances opportunities across modes.
Like the other HyFlex GPTs in this series, it doesn’t replace the judgment of an experienced teacher. Rather, it enhances it, sparking ideas and providing frameworks to ensure that all learners have equitable chances to engage.