Bridging the AI Gap: Evaluating the Impact of an AI Education Program for Caregivers on Parental Leave
Bibliographic record
Abstract
Artificial Intelligence (AI) literacy is increasingly important across many fields, yet caregivers remain underrepresented in AI-related fields due to a combination of systemic and individual barriers. To address this, the Caregivers and Machine Learning (C&ML) program developed and delivered an accessible AI education program to caregivers on parental leave. Two cohorts participated in this 6-week interprofessional program, featuring fundamental machine learning concepts, hands-on programming assignments, and a capstone project. This study examines the program's impact on participants, focusing on their motivations and barriers before, during, and after the program as outcomes after completion. Post-program surveys and semi-structured interviews highlight that caregivers often face barriers such as the rapid pace of AI, discrimination, and balancing caregiving responsibilities with learning new skills. The C&ML program's flexible structure and personalized support network were critical in enabling participants to fully engage in the program, leading to significant improvements in their knowledge of ML and increased confidence in applying these skills. After completing the program, 20\% of participants transitioned into AI-related roles or pursued further education. This research highlights the value of targeted, inclusive educational programs for underrepresented groups and provides practical recommendations for refining future AI training programs for caregivers.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".