Lingumate

Problem Statement.

While many popular language learning apps have gained traction, user face a significant challenge in providing truly personalized and immersive learning experiences. Learners often find themselves following generic lesson plans and repetitive exercises, leading to a lack of individualized attention and limited engagement. This approach can hinder their progress and motivation to continue learning.

Prompt

How might we help users track their progress and set achievable goals for language learning?

How might we provide personalized recommendations for language learning resources based on the user's skill level?

How might we integrate language learning into users' daily routines and make it more convenient for them to practice regularly?

Research.

_{The language learning app market experienced a significant surge in revenue, reaching a staggering $2.92 billion in 2021, reflecting a remarkable year-on-year growth of 65%.}

_{The report highlights AI and Machine learning as key technologies fueling innovation in language learning apps, enabling personalized learning experiences, speech recognition, and language translation.}

_{During the isolation period, multiple language learning apps have an average retention rate of only 18%, whereas the retention rate for the millions of app downloads by users is above 46%.}

Existing Apps

Survey

Q : How important do you think personalization is for language learning apps?

22/27 people consider it highly significant. 81.48%

^{*From the scale of 1 (Not at all important) to 5 (Extremely important)}

Q : How do you measure your progress in learning a new language?

In-person Interview

Q : How important do you think personalization is for language learning apps?

^{*3/3 interviewees considered it highly significant.}

Q : What are your overall thoughts on the potential of AI-powered language learning apps to revolutionize language education?

Q : How important is instant feedback and correction in language learning apps?

Affinity mapping

Employed the method of affinity mapping to collect and structure user feedback. Our primary objective was to gain insights into how users perceive and engage with the current language learning applications.

Extract Insights

1. ‍Limited time
   Many language learners face the obstacle of having limited time available to study and practice, which can slow down progress and make it harder to achieve their goals.
2. ‍Goal to communicate
   For many language learners, the main goal is to be able to communicate effectively in the new language, whether it's for personal or professional reasons.
3. ‍Desire to track progress
   Learners often want to be able to track their progress over time, whether through formal assessments or informal measures such as conversations with native speakers or keeping a language learning journal.

Persona

Userflow

Created a user flow of how the whole structure would be when users start the process from profile, and what stages they have to get through to manage to their goal which is a successful learning joruney.

Iterating Design.

Wireframe modification based on user test.

Final Design.

Key Factors to consider (MVP)

MVP 01: Lesson/Study plan setup process in app

MVP 02 & 03: Provide real-time feedback during exercises & personalizes the learning journey by adpating the difficulty level of subsequent tasks based on the user's performance (AI)

Additional factors to consider (Post MVP)

Limitations & Concerns

Technical Feasibility : Developing an AI-powered language learning app comes with certain technical challenges. The implementation and integration of AI algorithms and models require substantial computational resources and expertise. Ensuring seamless performance and responsiveness of the app, especially during real-time interactions and feedback, may require efficient server infrastructure and optimized algorithms.

Personalization and Data Privacy : Personalization in an AI-powered language learning app relies on collecting and analyzing user data, raising concerns about data privacy and security. App developers must prioritize privacy measures, transparent data handling practices, and obtain user consent for data usage.

Continuous Improvement : AI should be continuously updated and improved based on user feedback and evolving language trends. Regular updates, bug fixes, and adding new features and content ensure that the app remains relevant and effective for learners.

Key Takeaways

Flexibility and Personalization : Allowing users to customize their learning experience, such as selecting specific topics or setting learning goals, adds a sense of ownership and adaptability to cater to individual learning preferences.

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Personalized Learning : Machine learning enables personalized learning experiences by adapting content and difficulty levels based on user performance, promoting effective and engaging learning.

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Design aspect : The need to strike a balance between aesthetics and functionality to create an engaging and visually appealing app