Addressing educational overload with generative AI through dual coding and cognitive load theories
Bibliographic record
Abstract
WHAT WAS THE EDUCATION CHALLENGE?: Health professions education faces a critical challenge: the volume and complexity of medical knowledge has outpaced the cognitive limits of learners. Cognitive load theory indicates that traditional text-heavy instruction overloads the working memory. Current educational materials fail to leverage the dual coding theory which proposes that content should be presented through both verbal and visual channels. WHAT WAS THE SOLUTION AND HOW WAS IT IMPLEMENTED?: Generative AI tools enable the creation of multimodal educational content. Platforms such as ChatGPT, Gemini, NotebookLM, and HeyGen enable the creation of multimodal educational content - audio summaries, interactive mind maps, infographics, narrated videos, and voice-based interactions - designed to improve information processing in the working memory. Multimodal AI-generated content potentially enhances comprehension, retention and learning efficiency, and provides scalable, easily updated resources. These tools align with Generation Z learning preferences and can be integrated into existing curricula to transform text heavy content into multimodal engaging content. These tools can be used by faculty and learners,with minimal technical expertise or time investment. WHAT LESSONS WERE LEARNED AND WHAT ARE THE NEXT STEPS?: To ensure success, institutions must provide faculty and learners training, with consistent access, and evaluate outcomes. Pilot programs with iterative feedback can guide thoughtful implementation. By aligning educational strategies with cognitive science principles, generative AI can play a transformative role in addressing educational overload and creating more effective, engaging learning environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".