Exploring A Design Space for Digital Interventions Facilitating Early Adolescents’ Tech Disengagement: A Parent-Child Perspective
Bibliographic record
Abstract
Children's excessive technology use remains a significant challenge for parents, especially with early adolescents, given their growing independence and resistance towards parent-set device restrictions. Despite numerous parental control tools, there is limited research on tailored solutions for this age group. This paper advances this design problem by introducing and studying a child-centric design space for digital interventions targeting early adolescents’ technology overuse. Synthesizing literature on mediation strategies, early adolescents’ perspectives, and self-regulation, we first identify four pertinent design dimensions (early adolescents’ agency, supportive parental engagement, mentorship style, and motivation). Using these dimensions, we then create three contrastive design concepts as video prototypes. Utilizing the prototypes in an online study with 13 early adolescents (ages 11-14) and their parents, we provide insights into how both groups conceptualize effective digital interventions. Our findings highlight areas of consensus (e.g., granting early adolescents’ agency) as well as considerable variability (e.g., differing preferred mentorship approaches).
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".