2026 ASEE Annual Conference & Exposition

Motivation
Asynchronous learning provides flexibility and self-pacing but often risks becoming passive, with limited interaction or scaffolding. Research shows mixed results: some studies report lower performance compared to in-person or synchronous formats, while others highlight positive outcomes, particularly when interactive elements are included. Students value flexibility but cite challenges such as reduced feedback and accountability. If not carefully designed, asynchronous courses risk lowering engagement, reducing peer interaction, and weakening the development of higher-order cognitive skills.

A common obstacle to offering asynchronous learning more widely is the start-up effort required of faculty, which requires learning a new set of teaching practices. The integration of Generative AI/LLM tools offers a promising way to provide scaffolding, interactivity, and personalization while lowering the demand on faculty. This study uses two courses offered in an asynchronous format to investigate the potential of leveraging Generative AI/LLM tools.

Course Design and Activities
The two courses explored in this work-in-progress paper are a Cryptography and Data Security course with 14 students offered in Fall 2025 and a Machine Learning in Cybersecurity course with 9 students offered in Spring 2025. These asynchronous versions of existing in-person courses were redesigned around three structured phases—Priming, Discussions and Reflections, and Summary—with Generative AI/LLM tools integrated throughout each phase.
Step 1: Priming, students engage with course materials while AI supports comprehension and connections.
Step 2: Discussions and Reflections, students use AI to brainstorm creative projects, address challenges, and participate in peer-led forums.
Step 3: Summary, students evaluate project strengths and weaknesses, prepare reports, and reflect on their process with AI scaffolding.
The redesign was grounded in Bloom’s Taxonomy, moving from foundational knowledge to higher-order thinking, and guided by Universal Design for Learning (UDL) principles to ensure inclusivity and accessibility.

Methods
This work-in-progress paper presents a multiple case study of asynchronous courses with AI/LLM tool integration to explore the possibilities and obstacles involved in leveraging new technology to improve how engagement in asynchronous courses can be managed. Assessment involved multiple methods. Students’ learning processes were monitored using AI interaction logs, peer engagement measures, and self-reflections. Project rubrics and course grades captured student learning. The instructor engaged in peer debriefing with a member of the university’s teaching and learning center during analysis to validate interpretations.

Findings
Students used AI tools to meet a variety of objectives (e.g., to clarify complex concepts, to brainstorm a broad set of project ideas). Projects reflected higher-order thinking, originality, and strong alignment with real-world cybersecurity challenges. Surveys and engagement analytics revealed increased motivation and self-efficacy, while external reviewers validated the authenticity and workforce relevance of student work.

The full paper will detail the course design and structure, provide examples of project activities, outline the comparative evaluation methodology, and present key results how they were used to refine the course for a second offering, and posit future directions. The study contributes to the scholarship of teaching and learning by offering insights into how Generative AI/LLM tools can enhance engagement and learning outcomes in asynchronous education across disciplines.