Health professions learners often struggle with abstract theory. This session shares an AI-supported scaffolding strategy, reflective guides, podcasts, and reading summaries, that reduced cognitive load and improved learner engagement in a graduate course. Explore how to integrate AI for deeper learning in asynchronous or blended environments
 

Topic and Relevance
In graduate-level professional education, courses on theoretical foundations—such as learning theories—are essential for building instructional competence. However, many learners, especially those with clinical or applied backgrounds, report difficulty engaging with such abstract content. Learners often cite theoretical readings as too dense or disconnected from their prior knowledge or practice-based experiences. Compounding the challenge, most are working professionals with significant demands on their time, limiting their ability to deeply engage with unfamiliar frameworks.
This session shares the design and implementation of an AI-augmented scaffolding model developed to address this pedagogical challenge. The approach, titled “Before You Dive In” (BYDI), is grounded in Vygotsky’s concept of the zone of proximal development and schema theory. It was created to help learners meaningfully approach complex theoretical texts through layered support that activates prior knowledge, reduces cognitive load, and facilitates the construction of conceptual frameworks before reading.

The BYDI model incorporates three generative AI-supported elements:

• Reflective study guides with prompts that activate prior knowledge and provide cognitive structure;
• Podcasts that contextualize theory within practical settings, narrated in a conversational tone to lower entry barriers;
• Concise reading summaries that highlight essential concepts, terminology, and structure.
• This session is particularly relevant for instructional designers, faculty developers, and educators in asynchronous or hybrid settings. While the model was initially developed in a graduate course for educators, the principles and tools are applicable across disciplines and institutional contexts, including continuing professional development and online adult learning.

Interactivity Plan
A core goal of this session is to allow attendees to not only learn about the intervention, but also to engage with its components and reflect on how similar strategies might apply to their own work.
The session will use a structured 5-part engagement format:

• Framing Prompt (5 min): Participants reflect individually on a course or topic where learners struggle with abstract content. Responses will be shared on a live poll or post-it wall.
• Case Study Walkthrough (10 min): Presenters outline the instructional challenge, theoretical basis for BYDI (drawing from scaffolding, schema theory, and cognitive load theory), and summarize learner feedback.
• Hands-On Analysis (15 min): In rotating groups, participants will interact with anonymized BYDI examples (sample study guide, podcast transcript, AI-generated summary). They’ll evaluate strengths, consider learner accessibility, and discuss adaptation ideas.
• Group Synthesis (10 min): Small groups share one high-impact takeaway and one unanswered question with the whole room. We’ll co-create a digital list of design strategies and open questions.
• Commitment & Closure (5 min): Participants write down one adaptation idea to apply in their context and receive access to a resource folder, including templates and planning guides.
• The session emphasizes applied takeaways and community sharing. No prior experience with generative AI or instructional design theory is needed; examples and tools will be accessible to all.

Takeaways
Attendees will leave the session with:

• A deeper understanding of scaffolding abstract content: Participants will understand the cognitive and motivational barriers that abstract readings pose and how scaffolding supports schema-building for adult learners.
• A replicable AI-supported model: Attendees will receive a walkthrough of the BYDI model and resources to replicate or adapt this approach using freely available tools.
• Insight into AI’s instructional value: The session will showcase how generative AI (e.g., ChatGPT, NotebookLM) can be thoughtfully integrated into course design—not to replace teaching, but to enhance learners’ cognitive readiness and reduce overload.
• Design tools and reflection prompts: Participants will receive handouts and links to templates for reflective study guides, podcast outlines, and reading summaries, along with sample student feedback quotes (anonymized).
• A collaborative design space: Participants will contribute to a shared bank of scaffolding ideas for abstract or complex content, which will remain accessible after the conference to support continued experimentation and dialogue.
• Research-informed practice: The model is supported by literature on Vygotsky’s scaffolding, Sweller’s cognitive load theory, and schema theory. Practical implementation examples are grounded in learner feedback and evaluation.

Anonymity Statement
This abstract does not contain any institutional or personal identifiers. All content, examples, and resources will be anonymized in accordance with the OLC submission guidelines.

EJ Jung

Assistant Professor at Uniformed Services University of Health Sciences

Paula Weissman

Assistant Professor at Uniformed Services University of Health Sciences

Assistant Professor in the Distance Learning Lab (DLL) at the Uniformed Services University of the Health Sciences (USUHS)

Anita Samuel

Associate Professor at Uniformed Services University of Health Sciences

Jerusalem Merkebu

Dr at Uniformed Services University of Health Sciences