Enhancing Parenting Using AI: Exploratory Hackathon
Bibliographic record
Abstract
BACKGROUND: Parenting skills programs are the primary intervention for conduct disorders in children. The Pause app enhances these programs by providing digital microinterventions that reinforce learning between sessions and after program completion. The potential of artificial intelligence (AI) in this context remains untapped. Hackathons have proven effective for health care innovation and can facilitate collaborative development in this space. OBJECTIVE: We aimed to rapidly build AI-powered features in the Pause app to enhance parenting skills. METHODS: We undertook a 1-day hackathon that included an ideation phase drawing on the Design Council's double diamond framework and a development phase using microsprints based on agile and scrum approaches. The interdisciplinary participants included medical professionals, developers, and product managers. RESULTS: Participants identified 3 core problems: generating age-appropriate distractions, receiving feedback on parenting efforts, and effectively using the journal function. During the solution phase, a wide range of options were explored, resulting in 3 key solutions: AI-assisted idea generation, a tool for summarizing parenting interactions, and a weekly journal roundup. During the development phase, participants completed 4 microsprints. Teams focused on 3 workstreams: building a "weekly roundup" module, creating an AI-based distraction generator, and developing a summarizer for active play sessions. These prototypes were integrated into the preproduction environment, with each workstream producing a functional component. Participant feedback (n=4) was unanimously positive, with all participants rating the event as "excellent" and highlighting the value of in-person collaboration. CONCLUSIONS: This 1-day hackathon used the double diamond approach to develop AI-powered features for parenting programs. Three solutions were explored across workstreams, resulting in 2 fully functioning and 1 near-functioning app component. The rapid problem-solving approach mirrors other health technology hackathons and highlights the untapped potential of AI in digital parenting support, surpassing traditional e-learning or video-based methods. This work suggests broader applications of AI-driven coaching in fields like social care. Despite a small team, the hackathon was focused and productive, generating relevant solutions based on prior engagement with parents and practitioners. Future research will assess the impact of the app's AI-powered features on parenting outcomes.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".