The tides are turning; online education is becoming the new norm. As demand and popularity for blended and fully remote classes grow, how will we respond? Join us as we explore an in-progress pilot project that leverages generative AI to support engagement in asynchronous classes with hands-on demonstrations.

After the pandemic, fully online classes are more popular than ever, with 58% of students in higher education preferring them over hybrid or in-person courses. Assessment of this group of students reveals that 64% prefer classes that are not only fully online, but entirely asynchronous as well. This sentiment towards asynchronous education represents over one-third (37%) of the total student body (Bentrim et al., 2022). Enrollment in online programs has grown rapidly to reflect this preference, with over half of students enrolled in higher education being in hybrid or fully online programs (Garrett & Simunich, 2023; Hill, 2024). With this rise in non-traditional teaching, there is a growing need for educators and instructional designers to adjust to meet the demands of their stakeholders. One such demand is providing an engaging and interactive experience despite the lack of direct interaction among class members. I face this issue directly in my graduate internship, where I have been working with my school’s digital pedagogy coach to translate project-based learning scenarios from our in-person classes into an online format. To balance the quality and immersion with the cost needed for these projects, we have leveraged AI-generated videos to simulate characters and scenarios for students to interact with. Our first set of classes using this strategy will launch during the spring 2025 semester.

This session will consist of a deep-dive presentation into the design process behind these projects. First, the presentation will provide context for the learning goals and standard scenario structure. For our class, learning goals revolve around collaborative, group-oriented skills, including facilitating discussions, managing teams, reaching consensus, and resolving conflict. Excerpts from the student syllabus and project outlines will be provided. The session will then shift to discuss the specific design decisions throughout the process. This discussion will cover our reasoning for pursuing generative AI as a medium; our AI tool of choice, HeyGen; and the challenges posed by HeyGen throughout the process, particularly regarding script content. Throughout the discussion, attendees will have the opportunity to see scripts, screenshots, and videos of the characters in the scenarios. Finally, attendees will have the chance to step into the shoes of students and gain hands-on experience with our finished products.

Following the presentation will be a brief review of AI tools and the future of AI in education. This will offer a comparison between our selected tool HeyGen and its competitors, as well as opportunities to engage with free AI tools such as Chat GPT, veed.io, and Galileo.ai. Through this session, attendees will gain a greater understanding of the potential of generative AI in education. Attendees will also be provided with a list of the AI tools covered in the presentation and a QR code to access the materials from the presentation. Join us as we take the next step into the future of online education!

References
Bentrim, E. M., Grygier, J., Ralicki, J., Schiller, J., & Widenhorn, M. (2022). Nationwide student survey: Opportunities to grow student success and career preparation. Anthology. https://www.anthology.com/paper/nationwide-student-survey-opportunities-to-grow-student-success-and-career-preparation

Garrett, R., & Simunich, B. (2023, August 15). CHLOE 8: Student Demand Moves Higher Ed Toward a Multi-Modal Future. Quality Matters. https://qualitymatters.org/qa-resources/resource-center/articles-resources/CHLOE-8-report-2023

Hill, P. (2024, January 21). Fall 2022 IPEDS DATA: US Higher Ed Online Profile. On EdTech Newsletter. https://onedtech.philhillaa.com/p/fall-2022-profile-us-higher-ed-online-education