Mind Journey
– A Multimodal LLM–Powered Reflective Journaling Tool
1. Project Overview
MindJourney is an AI-supported journaling tool that analyzes users’ daily photos and notes using a multimodal LLM to generate personalized prompts for deeper reflection. The goal is to make journaling more engaging, emotionally relevant, and easier to begin—especially for young adults who struggle with traditional, generic journaling tasks.
To evaluate the system, our team conducted a four-day within-subjects study (N = 42), comparing MindJourney with a traditional guided journaling tool. Participants completed journaling sessions, responded to post-session questionnaires, and were later interviewed about their experiences.
To give an immediate sense of the system, Figure on the right shows the full user flow—from photo upload to AI-generated reflective prompts—illustrating the interface that participants interacted with during the study.
2. My Role & Responsibilities
3. Research Questions
RQ1: How can photos, notes, and multimodal LLMs be combined to support reflective journaling?
RQ2: Do personalized prompts enhance young adults’ motivation, engagement, and introspection compared to traditional journaling?
RQ3: How do users experience an AI-supported reflective journaling tool?
4. Research Process
4.1 Study Design
We adopted a four-day within-subjects design in which each participant used both systems (baseline → MindJourney or reversed order). The study procedure diagram clearly shows the sequence:
Day 0: Intake questionnaire and onboarding
Day 1–2: Traditional guided journaling
Day 3–4: MindJourney sessions
Post-study: Surveys and interviews
4.2 Prototype Testing
Before running the study, I conducted hands-on testing with early prototypes to examine:
clarity of the journaling flow
coherence of AI-generated prompts
user experience when uploading images or notes
pacing and cognitive load during sessions
4.3 Data Collection
Across all participants, I collected:
504 journaling entries (3 prompts × 4 days × 42 participants)
IMI and UES questionnaires after each system
System-logged metadata: entry length, typing duration
Semi-structured interview transcripts
4.4 Analysis Workflow
Quantitative analysis included paired t-tests, effect sizes, and visual result interpretation using the manuscript’s figures.
Qualitative analysis followed a thematic approach: initial coding → codebook development → iterative refinement → four final themes aligned with participants’ emotional and reflective experiences.
Additionally, semantic variation in journaling entries was explored using SBERT embeddings and PCA visualization (Fig. 8), which I helped interpret.
5. Key Findings
5. Key Findings
5.1 MindJourney Increased Motivation and Engagement
5.2 Richer Expression Without Increased cognitive load
5.3 Deepened Emotional Reflection (Qualitative Themes)
Quantitative results showed significantly higher scores for:
Interest and enjoyment
Value and usefulness
Felt involvement and perceived reward
These effects are clearly visualized in Fig.(IMI) and Fig.(UES), which highlight MindJourney’s ability to make journaling more intrinsically motivating.
MindJourney led to longer journaling entries, indicating richer reflection, without increasing session duration.
Users wrote more, but did not feel burdened—an important insight for wellbeing tools.
Four major themes emerged (supported by interview data):
Personalization fostered emotional connection
Photos and notes inspired new perspectives
Concrete, tailored prompts made writing easier to begin
Users desired more conversational, iterative interactions with AI
6. Design Implications
Based on users’ reactions and study outcomes, we identified several design opportunities:
Introduce “surprising” or perspective-shifting prompts to deepen reflection.
Enable multi-turn conversations, letting AI ask follow-up questions.
Make journaling part of daily mindfulness, incorporating photo capture into everyday routines.
Allow users to control tone, emotional depth, or difficulty of prompts.
Use multimodal input to reduce entry barriers, especially when users struggle to start writing.
These implications highlight how future reflective technologies could create more emotionally supportive and personalized experiences.
This work, MindJourney: Personalized Reflective Journaling for Young Adults Through Photos, Notes, and Multimodal LLMs, is currently under review for the ACM IMWUT Conference.