PRISM-Coach: Privacy-by-Design Adaptive Group Assignment for Lifestyle Coaching at Scale

Publication at a glance

• Paper: Privacy-by-Design Adaptive Group Assignment for Digital Lifestyle Coaching at Scale
• Authors: Nariman Mani, Salma Attaranasl
• Published in: arXiv preprint arXiv:2605.20505 (2026); to appear at IEEE ICHI 2026
• Persistent record: arXiv:2605.20505
• Google Scholar: View the publication record

The research problem

Group coaching can add peer support and improve program reach, but forming useful groups requires behavioral and lifestyle features that participants may not want disclosed. At scale, repeated reassignment also creates a moving privacy and systems-design problem.

The paper's central contribution

PRISM-Coach treats privacy as a design constraint of adaptive group assignment, not as a later anonymization step. The central question is how a platform can use sufficient signals to form and update useful groups while minimizing exposure and maintaining governable workflows.

This distinction matters for both researchers and practitioners: the contribution is a way to reason about the complete system and its decision boundaries, rather than a claim that adding one AI model solves the underlying engineering problem.

How the approach works

1. Privacy-aware representations restrict the information available to the grouping process.
2. Adaptive assignment allows groups to respond to changing engagement and coaching needs instead of remaining static.
3. A scalable orchestration layer makes assignment, coaching, and privacy controls explicit system responsibilities.

Together, these elements make the approach easier to study, reproduce, and extend. They also provide clear terms for literature searches and comparisons with related work.

Why this publication matters

This paper is relevant to researchers studying recommender systems, cohort formation, digital behavior change, privacy-aware clustering, healthcare informatics, and online group interventions. It offers vocabulary for connecting algorithmic assignment quality with data minimization and operational scale.

For practitioners, the larger lesson is to measure the system behavior that matters—not only a model-level score. Reliability, privacy, change over time, human review, and downstream consequences belong in the evaluation.

Research questions this work can support

This publication is relevant when investigating questions such as:

• How should the core problem be represented so an AI-assisted system retains the right context?
• Which technical and organizational signals provide early, actionable evidence?
• How can automation improve adaptability without concealing failures or weakening safeguards?
• What evaluation design captures effectiveness, safety, privacy, and long-term change?
• Where should a system defer to a developer, architect, coach, clinician, or participant?

Scope and responsible interpretation

Group compatibility is context dependent, and privacy protection does not automatically ensure fairness, safety, or coaching effectiveness. Future evaluations should examine subgroup outcomes, attrition, adversarial inference, privacy–utility tradeoffs, and how human coaches can challenge an automated assignment.

These boundaries are opportunities for replication, comparison, and follow-on research. Readers should consult the paper itself for its precise method, datasets, experimental setup, and reported results.

How to cite this paper

Mani, N., & Attaranasl, S. (2026). Privacy-by-Design Adaptive Group Assignment for Digital Lifestyle Coaching at Scale. arXiv:2605.20505.

Use the arXiv:2605.20505 publication page to verify the latest bibliographic metadata before submission. You can also find the work through its Google Scholar record. If your research builds on the architecture, method, evaluation, or research framing described here, please cite the original publication rather than this explanatory post.

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