Olanah is a digital journaling app designed to help young adults better understand and regulate their emotions. Existing journaling apps offered users a lot of structured guidance but lacked control and privacy. Our answer was a low-friction "brain dump" journaling experience paired with optional AI-driven insights, giving users pattern recognition without judgment or forced structure.
Research Methods
Secondary Research (Competitive & Market Analysis)
The team analyzed existing apps (Stoic, Reflective, Apple Journal, Rosebud, Mindsera) and broader market trends. Finding: existing apps' AI features were both their biggest strength (good at prompting deeper reflection) and biggest weakness (introduces bias, feels paywalled, or over-guided). Market research also showed rising post-pandemic demand for accessible, privacy-first emotional support tools outside clinical settings.
Impact: Directly shaped the decision to make AI a supplementary, non-intrusive layer rather than a conversational/therapeutic guide. This lead to the tagging/insight approach over a chatbot-style design.
Expert Interview (PhD student in decision-making)
Findings: personality-test-style framing ("personal blueprint") is engaging and could differentiate Olanah; AI-detected cognitive bias recognition could add value without being clinical; the team needed a clear stance on how/whether to intervene when a user's entry contains alarming content.
Impact: Informed the cautious, "mirror not judge" framing of AI features and flagged the alarming-content escalation question as unresolved future work.
User Interviews
Synthesized via affinity mapping into themes: emotional regulation, journaling habits, outcome goals, AI acceptance, privacy/trust, cognitive load, and tools used. Key insights: people prefer processing emotions internally with selective sharing, prioritize productivity over emotional processing, need free/unstructured emotional unloading, are time-constrained, want simple low-cognitive-load UI, and care deeply about privacy boundaries.
Impact: This most directly shaped the "brain dump" concept, a low-friction, unstructured entry point, and reinforced the decision to keep AI optional.
Co-Design Workshop
Combined affinity mapping, a "journaling timeline" exercise, and a creative "build a reflection robot" activity. Findings: journaling is often used to compose thoughts when overwhelmed; many people don't revisit or even keep their entries; sensory elements aid recall; reflection is non-linear and mostly happens alone; small/mundane moments matter as much as big ones.
Impact: Reinforced stream-of-consciousness journaling as the right model (rather than structured/guided prompts) and validated that entries don't need heavy archiving emphasis for all users.
Usability Testing
Tested early low-fidelity prototypes for learnability and clarity. Findings: the red-green tone meter was confusing and inaccessible (unclear what colors meant, oversimplified nuance); the tagging/highlight relationship on entries wasn't intuitive until highlights were made tag-triggered rather than always-on.
Impact: Directly led to two major design pivots. This included both replacing the linear tone meter with the two-axis Emotional Grid, and revising the tagging UI so highlighted text only appears on tag selection.
App Features
Brain Dump: A stream-of-consciousness entry mode (text or voice dictation) designed to remove the pressure of structure or "getting it right," lowering the barrier to starting a journal entry.
Speech-to-Text / Voice Dictation: Real-time audio capture with playback, letting users talk through racing thoughts instead of writing them out.
Olanah Button (AI Analysis): A fully optional AI pass over an entry. Keeping this opt-in was a direct response to sponsor and classroom feedback about AI's risks in sensitive emotional contexts; users who opt out still retain access to archive and personal stats.
Tagging: AI-suggested tags based on topics/emotionally charged language in an entry, editable by the user.
Reflective Quotes: Lets users search their archive by tag/topic and surfaces related past entries and quotes.
Common Topics: Aggregate stats showing what percentage of entries relate to which topics (e.g., Work 50%, Family 25%).
Personal Statistics / Insights Page: Lifetime stats (words written, days journaled, brain dumps completed) plus a valence scale showing emotional trends over time.
Emotional Grid: A two-axis (pleasantness × energy) visualization mapping topics/tags into quadrants (e.g., "new job" → high energy/high pleasantness), replacing an earlier red-green tone meter that tested poorly for accessibility and clarity.
Archive: Calendar-based history view with search/filter by tag, showing entry content and tone per day.
Olanah is a digital journaling app designed to help young adults better understand and regulate their emotions. Existing journaling apps offered users a lot of structured guidance but lacked control and privacy. Our answer was a low-friction “brain dump” journaling experience paired with optional AI-driven insights, giving users pattern recognition without judgment or forced structure.
Research Methods
Secondary Research (Competitive & Market Analysis)
The team analyzed existing apps (Stoic, Reflective, Apple Journal, Rosebud, Mindsera) and broader market trends. Finding: existing apps' AI features were both their biggest strength (good at prompting deeper reflection) and biggest weakness (introduces bias, feels paywalled, or over-guided). Market research also showed rising post-pandemic demand for accessible, privacy-first emotional support tools outside clinical settings.
Impact: Directly shaped the decision to make AI a supplementary, non-intrusive layer rather than a conversational/therapeutic guide. This lead to the tagging/insight approach over a chatbot-style design.
Expert Interview (PhD student in decision-making)
Findings: personality-test-style framing ("personal blueprint") is engaging and could differentiate Olanah; AI-detected cognitive bias recognition could add value without being clinical; the team needed a clear stance on how/whether to intervene when a user's entry contains alarming content.
Impact: Informed the cautious, "mirror not judge" framing of AI features and flagged the alarming-content escalation question as unresolved future work.
User Interviews
Synthesized via affinity mapping into themes: emotional regulation, journaling habits, outcome goals, AI acceptance, privacy/trust, cognitive load, and tools used. Key insights: people prefer processing emotions internally with selective sharing, prioritize productivity over emotional processing, need free/unstructured emotional unloading, are time-constrained, want simple low-cognitive-load UI, and care deeply about privacy boundaries.
Impact: This most directly shaped the "brain dump" concept, a low-friction, unstructured entry point, and reinforced the decision to keep AI optional.
Co-Design Workshop
Combined affinity mapping, a "journaling timeline" exercise, and a creative "build a reflection robot" activity. Findings: journaling is often used to compose thoughts when overwhelmed; many people don't revisit or even keep their entries; sensory elements aid recall; reflection is non-linear and mostly happens alone; small/mundane moments matter as much as big ones.
Impact: Reinforced stream-of-consciousness journaling as the right model (rather than structured/guided prompts) and validated that entries don't need heavy archiving emphasis for all users.
Usability Testing
Tested early low-fidelity prototypes for learnability and clarity. Findings: the red-green tone meter was confusing and inaccessible (unclear what colors meant, oversimplified nuance); the tagging/highlight relationship on entries wasn't intuitive until highlights were made tag-triggered rather than always-on.
Impact: Directly led to two major design pivots. This included both replacing the linear tone meter with the two-axis Emotional Grid, and revising the tagging UI so highlighted text only appears on tag selection.
App Features
Brain Dump: A stream-of-consciousness entry mode (text or voice dictation) designed to remove the pressure of structure or “getting it right,” lowering the barrier to starting a journal entry.
Speech-to-Text / Voice Dictation: Real-time audio capture with playback, letting users talk through racing thoughts instead of writing them out.
Olanah Button (AI Analysis): A fully optional AI pass over an entry. Keeping this opt-in was a direct response to sponsor and classroom feedback about AI’s risks in sensitive emotional contexts; users who opt out still retain access to archive and personal stats.
Tagging: AI-suggested tags based on topics/emotionally charged language in an entry, editable by the user.
Reflective Quotes: Lets users search their archive by tag/topic and surfaces related past entries and quotes.
Common Topics: Aggregate stats showing what percentage of entries relate to which topics (e.g., Work 50%, Family 25%).
Personal Statistics / Insights Page: Lifetime stats (words written, days journaled, brain dumps completed) plus a valence scale showing emotional trends over time.
Emotional Grid: A two-axis (pleasantness × energy) visualization mapping topics/tags into quadrants (e.g., “new job” → high energy/high pleasantness), replacing an earlier red-green tone meter that tested poorly for accessibility and clarity.
Archive: Calendar-based history view with search/filter by tag, showing entry content and tone per day.