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Bridging the AI Gap: Evaluating the Impact of an AI Education Program for Caregivers on Parental Leave
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Evaluates a 6-week AI/ML education program for caregivers on parental leave (two cohorts, 2022 and 2023) using a post-program survey (n=33) and semi-structured interviews analyzed with reflexive thematic analysis.
Problem
Caregivers, especially those on parental leave, are underrepresented in AI fields due to systemic and individual barriers, and little is known about how targeted AI education affects their motivations, barriers, confidence, and career outcomes.
Approach
A qualitative and survey-based evaluation of a bespoke caregiver-focused ML education program (small cohorts, asynchronous delivery, extended TA access, childcare bursary), examining motivations, barriers, pre/post confidence and post-program career outcomes.
Key results
- Of 33 survey respondents, about 20% made a major career shift (graduate studies in AI n=3, new ML roles n=3) after completing the program.
- Overall self-reported confidence increased across all measured skill categories, most notably discussing AI professionally and learning something new, with a ceiling effect on fundamental math skills.
- 94% (n=31) said they would recommend the program; 86% completion rate among 93 participants.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Complete the C&ML program | Caregivers on parental leave · C&ML program | ML knowledge | Helped |
| Complete the C&ML program | Caregivers on parental leave · C&ML program | confidence applying ML-relevant skills | Helped |
| Provide outside-of-working-hours TA access, asynchronous delivery, and a small cohort | C&ML program · C&ML program | support for participant needs | Helped |
| Use a condensed six-week format | C&ML program · C&ML program | participant experience | Mixed |
| Complete the C&ML program | Caregivers on parental leave · Post-program outcomes | transition to AI-related roles or further education: 20% | Helped |
Surprising findings
- Major career shifts were judged not an appropriate sole metric of success, since many completers felt unprepared to pursue ML jobs and cited hiring barriers.
Limitations
- Self-selected sample introduces selection bias and may not represent the broader caregiver population.
- Evaluation team members were involved in program development and delivery, risking bias.
- Program content and delivery varied across the two cohorts.
Experimental setup
Datasets: post-program survey responses (n=33) semi-structured interview transcripts
Metrics: self-reported confidence change (pre/post) survey response frequencies career transition rate
Builds on: Knowledge-to-Action framework Braun and Clarke reflexive thematic analysis
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