Role:

Senior Product Designer

Company:

DeepL

Key responsibilities:

  • AI product design

  • Design strategy

Synopsis:

AI-generated translations may lack contextual accuracy. As the designer driving Clarify, I designed the feature to bridge this gap by allowing users and the model to collaborate to achieve greater precision, clarity, and contextual accuracy.

Clarify: Designing User<>AI Collaboration at DeepL

Design for content discovery for Europe's largest learning platform at StudySmarter

Product overview


DeepL is the world's most accurate translator. With over 10 million daily users, DeepL Translator is known for its exceptional accuracy and natural fluency. Fast, reliable, and intuitive, DeepL is a go-to solution for high-quality, context-aware translations.

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Problem space


Every translation makes assumptions and traditional machine translation lacks necessary user input required for greater precision, adaptability, and contextual understanding. The ambiguities such as those in gender, idioms, or specialised terms can result in confusing or misleading translations, especially for non-expert users.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

Opportunities

Users need full proficiency in target language to refine the translation

Without expert knowledge in the target language, users struggle to grasp the quality of the translation, resulting in the lack of confidence in utilising the translation in their multilingual communication

AI translation makes assumptions without asking users for clarification

Users rely on machine translation but often find that ambiguous phrases go unnoticed, leading to misinterpretations

Editing translation can be costly and cumbersome

Manually adjusting translations or rewriting phrases to avoid ambiguity adds extra steps, making the experience frustrating and inefficient

Users in specialised fields lack terminology support

Translating industry-specific terms such as those in the legal, medical, or technical industries require professionals to cross-check and correct translations, increasing the cost and time for business to achieve their goals

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

Impact


The Clarify feature led to an improved user satisfaction and activity. Usability tests showed that users significantly felt more in control of their translations. By introducing contextual prompts, we streamlined workflows, saving users and business cost and time.

*Product design is rarely a perfectly linear process. If you're interested in the key learnings and trade-offs made throughout this project, I'd be happy to discuss further.

*Product design is rarely a perfectly linear process. If you're interested in the key learnings and trade-offs made throughout this project, I'd be happy to discuss further.

  1. Signal scanning

Through scanning the pool of customer support tickets and the interview notes, we identified that no matter how accurate the machine produces, it will always lack contextual that only human users can provide. These insights helped establish the need for a system that could involve users in clarifying intent, rather than relying solely on AI guesses.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

  1. Conceptualise

While initial work on training the model began, I focused on analysing patterns in translation errors.


Some of the key categories are:

  • Gender

  • Idioms

  • Formatting

  • Culturally specific terms


Mapping these categories allowed us to define the types of clarifications that would provide the greatest value to users.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

  1. Prototyping & user testing

I developed early prototypes that prompted users for clarifying questions when ambiguities were detected. We conducted customer interviews to collect early feedback and refined the experience based on their feedback.

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StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

  1. Internal release

Before a public release, we conducted an internal launch to gather insights on:


  • Usability: Was the feature intuitive and non-disruptive?

  • Value: Did Clarify improve translation accuracy and was the effort required justifiable?

  • Scalability: Could the AI model efficiently handle a range of clarifications without overloading users?


Multiple iterations were tested internally and with key stakeholders, refining the experience based on feedback.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

5. Experiment design & release

We identified key success metrics and create an experimentation plan to measure the impact for the launch.

The experiment was designed to track both quantitative metrics, such as engagement rates, and qualitative feedback, assessing how intuitive and helpful users found the feature through the survey.

First release

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

Next steps

Monitor post-launch metrics & user feedback – Continuously track engagement, error rates, and qualitative user feedback to identify areas for improvement


Collaborate with ML scientists to optimise AI models – Refine the AI’s ability to detect ambiguity and improve contextual recommendations


Iterate on UI/UX for a more effective and time-saving userflow – Address frictions to make the interactions more intuitive and integrated into workflows.


Scale to a broader user base – Gradually expand availability to more users and more languages

StudySmarter aims to enhance its content discovery experience to provide users with a personalised and engaging learning journey. However, the current content discovery mechanisms lack efficiency and effective content organisation, resulting in low user retention and frustrating learning experiences.

I am more than a sum of pixels.

I love facilitating collaboration, understanding different points of view and taming chaos.


When I’m not designing, you’ll find me surrounded by plants, expanding my reading list, or experimenting with new recipes in the kitchen.


Recently, I've been exploring alternative design approaches. Need book recommendations or just fancy a chat? Hit me up anytime!