![\"\"](\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2021\/03\/Duolingo-interactive-app.png\")
<\/a><\/p>\nFigure 1: Duolingo\u2019s app with personalized lessons<\/strong><\/p>\n <\/p>\n Duolingo generates hundreds of millions of data points from its 42 million monthly active users every day.[3] The data is used to train and improve AI algorithms which humanize the learning experience and keep users engaged and returning to the app. It\u2019s like having a private language tutor in the form of Duolingo\u2019s mascot, a green owl named Duo.<\/p>\n Figure 2: Duolingo\u2019s mascot \u201cDuo\u201d<\/strong><\/p>\n <\/p>\n Since there is significant heterogeneity among individuals learning languages in terms of individual\u2019s different goals, knowledge, capability, interest, time and learning preferences, Duolingo\u2019s strategy of collecting and processing data with AI enables the app to differentiate itself from competitors.\u00a0Duolingo has detailed information on each user so that it can tailor lessons to the individual needs based on the user\u2019s previous lessons and mistakes. Duolingo therefore provides lessons that allow users to continuously progress. In fact, Duolingo has built its AI model off of spaced repetition which determines how many times a user has seen a word and how long it will take for the user to forget that word. The spaced repetition system which began in 2013 as Duolingo\u2019s first AI project predicts which words a user has forgotten and re-introduces those words into the user\u2019s lesson.[4] This helps users to master new languages faster and more efficiently, based on AI\u2019s seamless yet personalized lesson recommendations.<\/p>\n <\/p>\n Duolingo also uses a computer adaptive placement test which enables new users to spend five minutes taking a quick quiz when they sign up for a new course, effectively suggesting the most suitable part of the course for the user based on their proficiency.[5] By leveraging the data and AI models, Duolingo can quickly capture the interest of new users to ensure that they are sufficiently challenged and excited by the course that they have signed up for.<\/p>\n <\/p>\n Birdbrain, Duolingo\u2019s machine learning model complements the app\u2019s personalized learning system by predicting how hard specific lessons will be for a user. Based on that prediction, which is enhanced and trained by Duolingo\u2019s half a billion lessons completed each day, user\u2019s lessons are calibrated and either made harder or easier based on the user\u2019s success. The blame algorithm is used to try to understand why users are making mistakes. Smart tips, another feature based on machine learning, attempts to give immediate tips based on the algorithm\u2019s prediction for the root cause of a mistake.[6]<\/p>\n <\/p>\n Apart from the AI and ML that Duolingo leverages to improve the quality of lessons, one of Duolingo\u2019s best features is its app notifications which are backed by AI to prompt users to open the app to practice at times when they are most likely to respond to the notification. By leveraging data to optimize notification time, Duolingo has been able to increase user retention by 2% for new users in the period of one day to one week after download. [7]<\/p>\n <\/p>\n Figure 3: Duolingo\u2019s AI backed notifications<\/strong><\/p>\n Implementation of AI and ML has not been an easy path at Duolingo. In particular, the company struggled to find talent that could advance Duolingo\u2019s data analytics efforts, while still understanding the psychology and cognitive nature of language learning. However, Duolingo\u2019s investments in AI and ML have enabled the company to capture value by increasing retention rates through content and incentives that cater to each individual. This higher retention rate translates to increased revenue through conversions from free to paid users and more targeted in-app advertisements. The success of AI and ML at Duolingo is underscored by the company\u2019s 2020 revenues of $180 million, a 13x increase from 2017. [8] As Duolingo continues to establish itself as a valuable data-driven learning platform, talent acquisition should become less of a challenge with more data scientists excited by the opportunity to work for a $1.5 billion educational technology company.[9]<\/p>\n Figure 4: Duolingo\u2019s growth in revenue as a result of AI and ML models [10]<\/strong><\/p>\n <\/p>\n Moving forward, it will be crucial for Duolingo to continue to focus its efforts on recommending the most effective order for users to learn languages. Additionally, there are many opportunities for Duolingo to further leverage AI and ML to offer users the opportunity to have conversations and live learning experiences with bots. This use of AI and ML will better simulate an immersive language learning experience. With years of data available, Duolingo has created a competitive advantage and is positioned to continue to differentiate itself and to expand its AI and ML capabilities to provide a superior language-learning experience.<\/p>\n \u00a0<\/strong><\/p>\n Endnotes<\/strong><\/p>\n [1] https:\/\/www.businessofapps.com\/data\/duolingo-statistics\/<\/a><\/p>\n [2] https:\/\/www.forbes.com\/sites\/bernardmarr\/2020\/10\/16\/the-amazing-ways-duolingo-is-using-artificial-intelligence-to-deliver-free-language-learning\/?sh=6be1ca745511<\/a><\/p>\n [3] https:\/\/www.businessofapps.com\/data\/duolingo-statistics\/<\/a><\/p>\n [4] https:\/\/venturebeat.com\/2020\/08\/18\/how-duolingo-uses-ai-in-every-part-of-its-app\/<\/a><\/p>\n [5] https:\/\/www.wired.com\/brandlab\/2018\/12\/ai-helps-duolingo-personalize-language-learning\/<\/a><\/p>\n [6] https:\/\/venturebeat.com\/2020\/08\/18\/how-duolingo-uses-ai-in-every-part-of-its-app\/<\/a><\/p>\n [7] https:\/\/venturebeat.com\/2020\/08\/18\/how-duolingo-uses-ai-in-every-part-of-its-app\/<\/a><\/p>\n [8] https:\/\/www.businessofapps.com\/data\/duolingo-statistics\/<\/a><\/p>\n [9] https:\/\/www.businessofapps.com\/data\/duolingo-statistics\/<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n