Exploring Customizable Interactive Tools for Therapeutic Homework Support in Mental Health Counseling

Human-LLM CollaborationAI-Assisted Decision-Making & AutomationMental Health Apps & Online Support CommunitiesPsychiatrists & PsychotherapistsPhysicians, Nurses & CliniciansAI/ML Researchers & Engineers

Paper Title

Exploring Customizable Interactive Tools for Therapeutic Homework Support in Mental Health Counseling

Publication Info

  • Topic area: Therapist-centered AI tools for mental health counseling.
  • Keywords: Therapeutic homework, mental health counseling, generative AI, therapist-facing tools, cognitive offloading, customization, clinical workflows, AI trust, human oversight, ethical AI.

Background and Problem

  • Problem / challenge: Therapists face cognitive burdens in synthesizing fragmented client homework data, which is often scattered across formats like paper worksheets, verbal reports, and app-based submissions. Existing tools fail to integrate and summarize this data effectively.
  • Significance: Efficiently tracking and interpreting therapeutic homework is critical for improving client outcomes, enabling therapists to focus on clinical insights rather than administrative tasks.
  • Motivation and related work: Prior research has explored client-facing mHealth tools and therapist-facing practice management systems, but few have addressed therapist needs for synthesizing multi-format homework data. Generative AI has shown promise in adjacent tasks, such as progress note generation, but its application to therapist-facing homework review remains underexplored.

Solution

  • Proposed approach: TheraTrack, a customizable therapist-facing tool that integrates multi-dimensional data and leverages large language models (LLMs) to generate traceable summaries and support natural-language queries.
  • Novelty:
    1. Centralized integration of fragmented homework inputs into a single interactive dashboard.
    2. Customizable widgets tailored to therapists’ clinical goals and therapeutic modalities.
    3. GenAI-powered summaries and chat assistant for efficient synthesis and query-driven exploration of client data.
  • Procedure and key techniques:
    • Defining Needs: Therapists complete an onboarding survey to specify preferences for tracking homework and clinical assessments.
    • Choosing Widgets: Therapists select relevant widgets, such as homework progress charts, GenAI summaries, and assessment trackers.
    • Displaying Customized Dashboard: The dashboard integrates homework trends, assessment results, and optional biometric data, with GenAI features enabling traceable summaries and interactive queries.

Results

  • Concrete findings:
    • 79% of therapists found GenAI summaries reduced their workload.
    • 64% rated the graphs and metrics as visually clear.
    • 50% expressed strong enthusiasm for long-term integration.
  • Advantage over baselines: TheraTrack reduced cognitive load, enabled verification of AI-generated insights, and provided a centralized view of client data, addressing fragmentation and inefficiencies in current workflows.
  • Experiments / evaluation:
    • Pilot study with 14 therapists using simulated client data.
    • Methods included think-aloud exploration, semi-structured interviews, and Likert-scale surveys.
    • Evaluated usability, perceived usefulness, trust, and customization.
  • Limitations and future work:
    • Small sample size may limit generalizability.
    • Focused on therapist perspectives; client perspectives remain unexplored.
    • Real-world adoption and long-term use may reveal additional challenges.
    • Current design tailored to specific homework tracking scenarios; broader therapeutic contexts require adaptation.

Summary

TheraTrack is a therapist-facing AI tool designed to streamline the review and interpretation of therapeutic homework by integrating fragmented data into a centralized, customizable dashboard. Leveraging GenAI for summaries and queries, it reduces cognitive burden, enhances session preparation, and supports clinical sense-making. Pilot study results indicate high usability, perceived usefulness, and trust among therapists, with customization enabling flexible integration into diverse workflows. Future research should explore long-term adoption, client perspectives, and adaptation to broader therapeutic practices.

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https://hci.top/en/papers/chi/222031/2026

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DOI: https://doi.org/10.1145/3772318.3790569
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Source
CHI
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Year
2026
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5 authors
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Subtopics
Human-LLM Collaboration, AI-Assisted Decision-Making & Automation, Mental Health Apps & Online Support Communities
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Psychiatrists & Psychotherapists, Physicians, Nurses & Clinicians, AI/ML Researchers & Engineers
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