Using AI-Enabled Images and Comic Narrative to Enhance Entrepreneurship Education
Bibliographic record
Abstract
This learning innovation is an engaging technique to help students learn abstract concepts such as entrepreneurship theory, mindsets, and methods in a playful and exciting way. We explore how current AI tools can be used by entrepreneurship educators to enhance their course materials by creating memorable images and story-based, highly visual comic book content (“graphic novels”). We implemented these enhancements in an interactive learning platform based on Articulate Storyline and tested them in an introductory entrepreneurship course with over 2000 students per year on building an entrepreneurial mindset. In this article, we include sample copies of the images and graphic novel comics and highlight key methods, lessons, results, and design artifacts created during the course of our learning journey. Educators may add these free Open Educational Resource (OER) assets to their own courses by linking to the online version. We believe this approach to entrepreneurship education enhances students’ engagement, memory retention, and the ability to identify and name underlying concepts and entrepreneurship theories. We hope to spark the interest of other entrepreneurship educators to use similar approaches at their universities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".