role
End-to-end UX/UI designer
User research & synthesis
Wireframing
Prototyping
Results
Developed a zero-to-one product across all stages of the design process
Applied data-driven insights and iterative user testing results to continually refine interaction design and enhance overall usability
Timeline
June 2025 – ongoing
Amidst a worsening global crisis of mental health, alongside a crisis in available psychological support services, a new crop of ‘digital wellness’ tools promises to be your therapist and your best friend, in your pocket.
As these tools become an inevitability (reaching for ChatGPT is a far more accessible and immediate solution than navigating the mental healthcare system, for example), there is now more than ever a pressing need for thoughtful digital tools that facilitate meaningful self-reflection. Moodring is a case study about how we might leverage artificial intelligence to enhance mental health in ways that help people think more – more deeply, more constructively, more intentionally – not less.
The antidote: Moodring
Moodring is a conceptual AI-assisted journaling app that encourages deeper, more informed self-reflection by acting as an interpretive intermediary, or a mirror, between users and their writing.
A 2025 US survey found nearly half (49%) of respondents had used a large language model (LLM) for psychological support within the past year. Similarly, a 2024 Australian survey found that 40% of mental health professionals incorporated AI into their practice – primarily to obtain "quick mental health advice". Together, these figures highlight a growing reliance on digital tools for mental wellbeing and a critical need for high-quality, intentionally designed solutions.
(why supercomputers can’t be therapists)
AI chatbots are designed to be agreeable.
The value of therapeutic approaches such as cognitive behavioural therapy (CBT) lies in having one’s own assumptions (or cognitive distortions) challenged, enabling their reframing. A digital tool that affirms its user’s thoughts and feelings by design is fundamentally ill-equipped to play the role of therapist in our post-digital age.
AI tools are contraindicated for thought.
AI tools are built for productivity, and thus operate on the implicit assumption that offloading or reducing work (cognitive load) for the user is a net benefit. Outsourcing thinking as such has many practical applications, though the deep, considered self-reflection core to therapy is not one.
I began Moodring’s formative market research with a competitive analysis of existing journalling and mental wellbeing products. I mapped these tools across two axes: degree of personalisation and depth of reflection supported, approximating value and experience respectively.
Products clustered away from the upper-right quadrant, indicating that even AI-driven journals remain shallow and generic when measured against the theoretical benchmark of therapeutic intervention. This gap defined Moodring’s positioning: an AI-assisted journal designed to deliver genuinely personalised insights and support sustained, deep self-reflection.
To better understand Moodring’s prospective users, I carried out a series of formative user research consisting of a survey, one-on-one interviews and a thematic analysis of the r/Journaling subreddit.
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8 overarching themes
With the problem clearly defined, I switched back to divergent thinking and began the develop phase of the design process. I formulated a foundational how might we question to guide my design direction: “How might we reframe negative emotions as opportunities?”.
(how supercomputers can help us think better)
I used a prioritisation matrix to evaluate and compare features, informing the scope of my minimum viable product (MVP). I prioritised quick wins from the upper-right quadrant, as well as some more difficult but high-value features that were essential to solving the core problem.
Prioritisation matrix
From my MVP, I developed low- to mid-fidelity wireframes, which I also used to visualise a range of user flows and the relationships between them:
Mid-fidelity wireframes and flows
Core to Moodring’s interface is the Insights feed, a dynamic, dashboard-style interface where users can explore bigger picture trends over time and reflect retrospectively.
The feed employs a widget-style insight delivery system, which frames content as accessible, modular, explorable and bite-size, fostering dynamic and sustained engagement with content that ultimately enables the goal of self-reflection.
Lens filters give users more control over the type of insights they see. The wide range of multidisciplinary angles gives users flexibility and enables a high degree of explorability in the feed.
Dynamic content creates a feedback loop with user reflection as an input, fostering repeat engagement via continual reflection.
Within the Insights feed, the breadth of content affords a highly explorable experience for casual users, while insight depth within expanded view also caters to users interested in deeply unpacking their insides.
Highlights Mode
Users can enable highlights mode to reveal personalised annotations within their entries for immediate, contextual reflection and access to targeted prompts in real time.
Journaling experience
User personalisation is at the core of Moodring's design system – customisable journal covers are one such example. I employed a vibrant take on neumorphism to hint at object physicality and enable some of the personal embellishment often lost in translation between traditional and digital journaling.
User testing: design iterations
I added a hide insights feature to help users manage anxiety or cognitive overload around negative trends.
Users wanted more control over AI-based content, so I introduced options to edit or delete proposed highlights.
Users varied in the time and effort they had to spare, so I added a simple mood slider as quick, low-effort entry-point.
Reflection
What I learned: This project reinforced that user wants do not translate cleanly into features on a one-to-one basis. During usability testing of Moodring’s Insights feature, I encountered an illustrative tension: while users wanted insights that felt accurate and meaningful, they were also uneasy about the emotional impact of being confronted with negative patterns. These two needs existed simultaneously and could not be satisfied by simply “improving” the insights themselves.
In response, I iterated on the original design to include a Hide Insights option, giving users more control over when and how they engaged with emotionally charged feedback. This shift helped me recognise that good UX often involves designing for psychological wellbeing, not just functional clarity.
Going forward, I intend to consider these tensions earlier in the design process rather than treating them as edge cases revealed through testing. While this conflict between explicit wants and implicit needs may never be fully resolved, being more attentive to it has already helped me in making more deliberate, user-centred design decisions.
© 2026 Joseph Cholakyan
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