Leveraging Generative AI in Academic Libraries: A Case Study of the AI Story Idea Generator at the University of Lethbridge
Bibliographic record
Abstract
This paper explores the design, implementation, and initial outcomes of the AI Story Idea Generator, an interdisciplinary project developed collaboratively by the Dhillon School of Business and the University of Lethbridge Library. Created to enhance digital literacy and promote creative storytelling through artificial intelligence, this open-access initiative generates dynamic, randomized story prompts by combining predefined narrative elements stored locally in structured JSON format, enabling over 126 trillion unique story combinations. Employing a sustainable, open-source architecture free from ongoing external costs, the project exemplifies key principles of open scholarship, affordability, and ease of maintenance. This case study highlights the strategic technical decisions made to balance complexity and user accessibility, details the collaborative process involving expertise from business analytics, library science, and AI, and reports preliminary user engagement observations indicating successful promotion of AI literacy and creative exploration. The AI Story Idea Generator serves as a replicable framework for academic libraries seeking scalable and cost-effective methods of integrating generative AI technologies into educational services.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".