Mobile devices enable just-in-time adaptive interventions that flexibly adapt support to students' changing internal and contextual states¶
Claim · All claims
Evidence · 1 study · theoreticalr3·q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Mobile devices can deliver different types or amounts of prompts in real time based on collected data, providing support precisely when and where it is most beneficial, especially when human support is unavailable. → Breitwieser 2024
Evidence¶
Breitwieser 2024¶
Breitwieser, J., Neubauer, A. B., Schmiedek, F. & Brod, G. (2024). Realizing the potential of mobile interventions for education. npj Science of Learning, 9, 76. https://doi.org/10.1038/s41539-024-00289-9
q2 · i? · theoretical · r3
Theoretical argument in this Perspective, drawing on mHealth examples such as the HeartSteps program. The authors argue algorithms can send "different types and/or amounts of prompts based on decisions made in real time," e.g. study reminders based on location or breathing prompts based on electrodermal activity.
"Mobile devices are ideally suited toimplement and adapt interventions just in time, especially when teachers or other human support is not readily available."
Discussion¶
Related Claims¶
- Micro-randomized trials with mobile data allow explicit testing of time-varying contextual moderators of intervention effectiveness — related
- AI chatbots enable flexible personalized learning by adapting teaching strategies to individual student needs — related
- AI-supported adaptive systems are reported to enhance adult learner engagement, motivation, and outcomes when aligned with learner goals and prior knowledge — a broader claim this one bears on
- Integrating cognitive scores with non-cognitive learner attributes enables the system to dynamically adapt AI-generated explanations in tone, depth and instructional strategy to individual learner performance — related
- AI is implemented in SRL research primarily as an intervention, most often through adaptive systems and personalization — related
- The effect of mobile game-based learning on learners' engagement and achievement is still to be documented in research — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
jitai-just-in-time-mobile-support |
title |
Mobile devices enable just-in-time adaptive interventions that flexibly adapt support to students' changing internal and contextual states |
description |
Mobile devices enable just-in-time adaptive interventions that flexibly adapt support to students' changing internal and contextual states |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2024 |
generated |
by process:wiki-ingest on 2026-10-26 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — breitwieser-2024 |
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