Curiosity to Confidence with the AI Hub
Bibliographic record
Abstract
In response to increasing curiosity, confusion, and concern about generative artificial intelligence (AI), the authors launched the AI Hub at the University of Guelph-Humber during the 2024–2025 academic year. Designed as a physical booth in a high-traffic area of campus, the AI Hub served as a welcoming space where students, instructors, and staff could explore the practical and ethical dimensions of AI through informal, hands-on interactions. Weekly activities ranged from live demonstrations to guided discussions and resource sharing, aiming to make AI approachable and meaningful for academic, personal, and professional use. These encounters encouraged dialogue and reflection, fostering a deeper understanding of AI’s capabilities and limitations. This paper describes the development and implementation of the AI Hub, offering insight into both the logistics and outcomes of this initiative. Over the course of the year, the AI Hub engaged more than 500 members on campus and over 18,000 views on videos on social media, suggesting strong interest and growing demand for accessible AI education. Reflections from the student research assistants who operated the booth revealed four key themes: shifting from fear to empowerment, creating safe spaces for open conversation, bridging understanding through practical tools, and reshaping their own perspectives on AI’s role in their future careers. This article offers a replicable, low-barrier model for engaging campus communities in ethical AI exploration and concludes with recommendations for institutions seeking to build confidence, curiosity, and critical awareness around AI technologies.
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 | 0.039 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".