The Untold Story of Byju’s AI Tutor

The English Learning App (ELA) was one of the most exciting projects I worked on as a Senior Product Manager at Byju’s. I led a passionate team (with guidance from our Director and AVP of Product) to build a solution that could bridge the gap between academic English education and practical communication skills. In many traditional classrooms, English learning centers on textbook grammar and rote memorization, with little focus on real speaking or listening. (In fact, studies have noted that heavy focus on reading/writing for tests while neglecting practical communication can make students less willing to engage in actual conversation.)

We envisioned ELA to change that. Leveraging cutting-edge AI, we set out to create an engaging, personalized learning experience that would help students truly use English in daily life. This is the story of ELA – its unique innovations, its impressive performance metrics, and the strategic hurdles it faced in scaling further.

What Made ELA Unique?

ELA was a self-paced, AI-driven English learning platform tailored to help students in grades K1-K8 improve their communication and language skills. From the outset, we knew ELA had to stand out in a crowded edtech space. To achieve this, we built ELA on four key pillars that defined its uniqueness:

AI Buddy - A Conversational Partner: The AI Buddy wasn’t just a basic chatbot. It actively engaged students in spoken conversations, analyzing live voice inputs to provide real-time feedback on grammar and pronunciation. Imagine a shy 7-year-old practicing English by chatting with a friendly cartoon character - that’s the kind of interactive experience ELA delivered. This focus on speaking and listening (rather than just reading or quizzes) emphasized practical skill-building over rote learning. It set ELA apart from other learning apps that were essentially digital textbooks for grammar practice. The real-time voice feedback was revolutionary for a kids’ app; it was like having a personal tutor available anytime. This approach isn’t just intuitive; our user-interactions anecdotally showed that AI-powered language tools with instant pronunciation feedback can significantly improve learners’ speaking fluency and confidence. In ELA, students could actually talk to the app and get gentle corrections, which made learning English feel like having a conversation rather than doing an assignment.
Personalized Learning Journeys: ELA used adaptive learning algorithms to craft a unique journey for every student based on their starting proficiency level. We aligned the levels with CEFR (the Common European Framework of Reference for Languages) - an international standard that rates language ability on a six-point scale from A1 (beginner) up to C2 (mastery). Each student’s goal was clearly defined: progress from their current CEFR level to the next one (for example, moving from A2 to B1, or B1 to B2). By giving every learner a customized roadmap, the app ensured that a child who started with basic vocabulary would get different content and pace than a child who was already intermediate. This level of personalization directly correlated with improved outcomes, because the difficulty always matched the student’s capability. It meant no one-size-fits-all lessons - every user’s experience was tuned to their needs. Learners could tangibly see their progress, which was highly motivating. For instance, if a student began at A2 level, the app would celebrate their milestones as they worked toward B1, making the advancement feel like a game level-up. This kind of adaptive approach is known to enhance engagement and performance and we saw it firsthand: by ensuring each task was neither too easy nor too hard, ELA kept students in the perfect zone of challenge and growth.
Engagement Through Gamification: We infused ELA with gamified features to make learning fun and sticky. Progress-based rewards, conversational mini-games, and even dynamic daily greetings from the AI Buddy turned the learning process into a playful experience. For example, a student might earn a badge or a cute animation after completing a lesson, or the AI Buddy might start the day by saying, “Good morning! Ready for a new challenge?” in a cheerful tone. These elements created an enjoyable environment that encouraged students to return daily. The gamification wasn’t just for show – it was a strategic choice to build habit-forming behaviors. Incorporating game elements (like points, rewards, and narrative challenges) significantly increased learner engagement and motivation with ELA: kids would often log in every day to check what new game or reward awaited them. By blending learning with play, we helped students associate English practice with fun and achievement, rather than treating it as a chore. This daily re-engagement loop was crucial for skill improvement.
Practical Skill Development: ELA prioritized real-world language skills - oratory, pronunciation, conversational grammar - rather than teaching to the test. The idea was to prepare students for everyday English conversations, not just school exams. In many education systems (including India’s), traditional English education has heavily emphasized writing and grammar rules, often neglecting the speaking and listening parts of communication. We took the opposite approach. Every module in ELA was designed to get students using English in realistic contexts: speaking full sentences, listening to dialogues, responding to questions – the kind of skills that would help them introduce themselves, describe their day, or ask for help in English. For example, one lesson might involve the AI Buddy asking “How was your day at school?” and guiding the child to respond in English. By focusing on these practical skills, ELA helped learners gain confidence for real interactions, not just score marks on a test. This pillar made ELA a true life-skills program. Parents told us that their children were not only acing their English exams at school, but also happily greeting relatives in English or narrating stories – a testament to how well they were applying what they learned.

Performance Metrics: A Testament to ELA’s Potential

We closely tracked ELA's performance. The numbers that came in were encouraging and underscored just how much promise the product held. These performance metrics showed not only that ELA was effective in teaching, but also that it could drive strong engagement and retention at scale. Below is a breakdown of key metrics that highlight ELA’s impact:

User Growth

Despite the annual ebb and flow typical of education platforms (many edtech apps see user activity dip during school exam season or long holidays), ELA bucked the trend. We observed steady month-on-month growth in both downloads and active users. In practical terms, this means that every month more new students were joining, and importantly, existing students were sticking around. This consistent growth indicated that word was spreading and the product was finding product-market fit. It’s worth noting that typically, educational apps struggle with seasonality - for example, during summer vacation, student activity might drop off. The fact that ELA continued to grow even through the usual slow periods shows the platform’s growing appeal and strong retention strategies. Essentially, students (and their parents) found enough value in ELA to keep using it year-round, making it resilient against the normal academic calendar cycles.

Engagement
Engagement metrics revealed how deeply students were involved with ELA on a daily basis. The results here were truly impressive:

Time Spent Soared: Over a 6-month period, the average daily time spent on the app increased by 48%, reaching about 45.89 minutes per user. Think about that – nearly 46 minutes of daily learning on ELA! That’s almost the length of a class period in school. It showed us that kids were willingly spending substantial time practicing English, far beyond a quick homework-like session. For an after-school app, this level of engagement per user is a big win; it suggested that learners were captivated by the content and features. Seeing such a jump in usage (almost half again as much time per day) meant our updates and engagement strategies were working to keep learners hooked.
Chat-Based Learning: A core design of ELA was the AI Buddy chat, and the data proved its importance. Fully 72% of user interactions were initiated through the AI Buddy’s chat interface – in other words, the majority of learning activities began with students chatting with the AI. This told us that kids weren’t just passively clicking multiple-choice questions; they were actively conversing as their main mode of learning. The chat format made learning interactive. Moreover, premium users (who had access to the full, unlocked version of ELA) spent 1.6× more time on these chat-based activities than free users did. This reflects how the richer, personalized content available in the premium tier really drew students in. Those who invested in the premium version apparently loved the conversational exercises and engaged with them even longer. It’s a strong validation of our interactive approach – given more to do and learn in the chat, the students eagerly took it. (It also makes a good case for the value of the premium subscription, since it drove much higher engagement.)
Daily Active Learning Habits: We tracked a metric for “critical activities” - essentially key learning actions like completing a lesson, taking a quiz, or engaging in a conversation. We found that 48% of users performed at least one critical learning activity every single day. Nearly half our user base was daily active in a meaningful way, not just logging in and bouncing off. This was a standout statistic: it indicated that ELA had become a part of the daily routine for a huge portion of users. In edtech, encouraging daily practice is like the holy grail, because language learning especially benefits from consistent practice. A 48% daily active rate showed that ELA was effective in building habits. It wasn’t a forgettable app that kids opened once a week; it was something they returned to regularly, which is where real skill improvement happens. This kind of stickiness – getting users to come back day after day – underscored ELA’s ability to sustain meaningful engagement over time.

Retention and Stickiness

Retention measures how well an app keeps its users over longer periods, and stickiness here refers to frequent usage. In these areas, ELA again showed strong performance:

Higher Retention on Tablets: We discovered that tablet users demonstrated a 65% retention rate, significantly outperforming the retention rate of mobile phone users. This means that 65% of the students using ELA on tablets were still actively using it after a given period (we often measure retention at 4 weeks or 8 weeks). Such a high retention number is outstanding in the consumer app world, and it was even more interesting to see the device-wise split. The fact that tablet users stuck around more than phone users suggests that the app’s experience was optimized for larger screens – perhaps the interactive and visual elements were more enjoyable on a tablet. It also aligns with user behavior: younger kids often use personal tablets (or shared Byju's Tablets) for learning, and parents might be more involved on a mobile device, leading to better retention. Byju’s had its own LearnStation tablet, and indeed ELA was very smooth on that. It’s likely that a lot of ELA’s target demographic accessed it via tablets in a more “study time” setting, as opposed to on a parent’s smaller smartphone. In any case, a 65% retention rate was a strong validation that if we got the context right (device and environment), students loved continuing with ELA.
Diagnostic Assessments Boosted Stickiness: ELA included diagnostic assessments – essentially placement tests and periodic quizzes to gauge a student’s level and progress. Users who completed these assessments showed 38% better monthly stickiness than those who didn’t. “Stickiness” here can be interpreted as a combination of retention and frequency (for example, Monthly Active Days or returning user rate within the month). A 38% uplift is significant. It suggests that when students took the time to go through a diagnostic test, they became more investedin the app afterward. This makes sense: the diagnostic would personalize their learning journey (by identifying their CEFR level and tailoring content), which probably made the subsequent lessons more on-point and satisfying. Also, completing a test might have given users a sense of accomplishment and understanding of where they stand, motivating them to come back and improve. In other words, the assessments acted as a hook – once users saw their results and learning plan, they were more likely to stick around to see their progress. It underlined the importance of having that initial assessment moment to engage users for the long haul.
Premium Users Retained Longer: By week 4 (a common benchmark for medium-term retention), the data showed premium users’ retention was 2× higher than free users’. This means that a month into using ELA, paying customers were twice as likely to still be actively using the app compared to those on the free tier. This kind of difference is expected to some degree – users who pay are often more committed – but 2× was a large gap, indicating the premium experience was much more engaging. It also emphasized the value that ELA delivered to its paid audience: those who had upgraded were finding enough benefit to continue consistently. For the business, this was a positive signal because it implied that if we could convert a user to premium, their lifetime with the app (and thus lifetime value) would be substantially higher. From a product perspective, it told us that the premium features (such as more content, perhaps more AI Buddy conversations or advanced levels) were really effective in retaining users. It justified our strategy of offering a freemium model: hook users with the free content, then provide such a good premium offering that they not only convert, but also stay much longer.

Learning Outcomes

Ultimately, the true measure of an educational product is the learning outcome – are students actually improving? For ELA, the outcomes were very encouraging and demonstrated real impact on language proficiency:

CEFR Level Improvements: Across the user base, ELA achieved a 24% improvement in CEFR levels in a short time. In practical terms, this could mean that if you took all our users and measured their English level when they started vs. a few months later, on average there was a 24% progression in proficiency. This is a broad metric, but we also had more concrete stats: among premium users, 17% advanced by at least one full CEFR level, and several even achieved two-level improvements. To put this in perspective, moving up one CEFR level (say from A2 to B1) is a significant jump that often requires many months of study or hundreds of hours of instruction. The fact that 17% of our paying users did that, and a notable subset jumped two levels (e.g. A1 to B1, or A2 to B2) within the duration of using ELA, is remarkable. It highlighted the effectiveness of the personalized and immersive approach. We were essentially compressing what might traditionally take a year of classes into a much shorter period, thanks to targeted practice and AI guidance. This outcome was a huge validation of ELA’s pedagogical model. (And likely, the premium users had more access and time with the app, contributing to faster progress – another reason they retained better as mentioned.)
Tangible Life-Skills Gains: Beyond exam scores, we saw that ELA’s personalized journeys ensured a direct, measurable impact on students’ real-world language proficiency. The learning wasn’t abstract. We heard feedback and could observe through the app data that kids were actually improving in speaking and listening skills – fulfilling ELA’s promise as a life-skills enabler. For example, if a student started at CEFR A1 (beginner), after a couple of months on ELA they might reach A2 or even higher, meaning they could handle basic conversations and understand simple English in daily situations. Parents reported improvements like “My child can now describe his day in English” or “She corrected someone’s pronunciation, which she learned from the app!” These are qualitative evidences, but they align with the quantitative improvements we measured. It was clear that the combination of practice + personalization led to genuine skill development. In the end, that’s what we set out to do – not just have students memorize some new words, but actually use English as a tool. ELA proved that with the right technology and approach, young learners can make significant strides in a short time, becoming more confident English communicators.

(As an aside, these learning outcomes were particularly rewarding for our content team. Many of us came from backgrounds where we knew the struggle of learning English just from school curriculum and not being able to speak well. Seeing students overcome that through our product was a proud moment.)

The Product Team’s Strategic Approach

Building a great product is one side of the coin; ensuring it reaches the right users and fits the business is the other. From day one, we complemented ELA’s development with a well-thought-out Go-To-Market (GTM) strategy. This strategy covered how we positioned ELA in the market, who our target audience was, how we planned to monetize the product, and even how we could expand it internationally. Below is an overview of how the product team approached each of these strategic facets:

Positioning

We positioned ELA as a skill-based learning platform for young students, rather than a curriculum or syllabus-based solution. This was a deliberate differentiation. Byju’s is known for its curriculum-focused products (like apps that align with school textbooks or exam prep courses), but ELA’s identity was different: it emphasized life skills like speaking, pronunciation, and confident communication, all delivered in a judgment-free, AI-powered environment. We wanted parents and students to see ELA not as “another tutoring app” but as a companion that helps kids actually use English.

This positioning was encapsulated in how we described and marketed ELA. For example, instead of saying “learn English grammar for Grade 4,” we said things like “become a confident English speaker.” Unlike many traditional edtech offerings that might focus on improving exam scores, we highlighted scenarios like speaking in front of the class, telling a story, or talking to a friend in English. ELA was portrayed as fun and empowering rather than academic and test-oriented. This was important because we were targeting a mindset shift: learning English can be interactive and enjoyable.

One of our internal taglines was that ELA provided “a judgment-free space to practice”. Kids often are too shy to speak up in a classroom due to fear of mistakes. With an AI Buddy, there was no embarrassment – the AI would never mock them and was infinitely patient. This angle resonated with parents who understood that their child needed more speaking practice. We essentially carved out a niche: if you want your child to speak English confidently and practically, ELA is the go-to product (whereas if you want them to score in English exams, there are other Byju’s courses for that).

This positioning, focusing on communication skills for K1–K8, was unique in the market. Not many products for that age group were doing speaking-focused AI tutoring at the time. So, we were staking a claim as an innovator in the kids’ language learning segment.

Target Audience

ELA’s content and design were exclusively tailored for students in grades K1–K8 (roughly ages 5 to 13). We deliberately narrowed the target audience to this range of young learners, rather than attempting to serve high-schoolers or adults. The reason was to create an experience highly optimized for kids in those formative years of language acquisition.

Focusing on K1–K8 influenced a lot of our decisions. The visuals in the app were colorful and child-friendly. The vocabulary and topics covered things children care about (like animals, family, school life, simple stories) rather than, say, business English or advanced literature. Even the AI Buddy’s personality was crafted to be like a friendly mentor or buddy that a child would enjoy interacting with (sometimes playful, sometimes encouraging). We also ensured the difficulty progression matched the cognitive and language development stages of that age range. For instance, a 6-year-old using ELA would get very simple sentences and more basic vocabulary, whereas a 12-year-old might get longer dialogues and more complex grammar practice – yet both would still be within the app’s scope of “practical everyday English.”

By zeroing in on this young audience, the product team could create a truly engaging, interactive experience for young learners. We incorporated lots of elements known to appeal to kids: cute characters, game-like reward systems, stickers, etc., alongside pedagogically sound content. Another part of targeting this group was involving parents. We added features like progress reports or little sharable certificates, knowing that parents are the ones who decide to continue a subscription. If a parent of a 3rd grader sees their child using ELA happily every day and improving in speaking, that’s a win – and we designed with that use-case in mind.

In summary, the target audience strategy was: keep it narrow, know them deeply, and serve them brilliantly. By not trying to be everything to everyone, ELA became very good for the specific group it aimed to help. And indeed, our user base was primarily elementary and early-middle school students, just as intended, and their engagement levels validated that the content was spot-on for them.

Monetization and Bundling

Monetization for ELA was a combination of premium subscriptions and strategic bundling with other Byju’s offerings. The base app was free with limited content, and we offered a premium tier unlocking full access and features (this freemium model is common in consumer edtech). But beyond subscriptions, we explored ways to package ELA to maximize its reach and revenue potential within Byju’s ecosystem.

One big idea the product team worked on was bundling ELA with Byju’s flagship products, such as the Byju’s LearnStation tablet or other course packages. For example, if a parent was purchasing a Byju’s kit or enrolling in a class program, ELA could be included as an add-on at a discounted rate. This approach could dramatically drive adoption because it leverages the existing sales channels of Byju’s. Instead of selling ELA entirely on its own, we could piggyback on the popularity of other products. From a user standpoint, this is convenient – your tablet comes pre-loaded with this cool English app. From Byju’s standpoint, it could increase the average revenue per customer.

We did market surveys and found a strong willingness to pay for ELA’s premium features among parents, especially if they clearly saw the speaking skills benefit. The data suggested that offering ELA as part of a bundle could be highly lucrative. In fact, our analysis showed that certain bundling options could yield a 700% margin compared to the cost of running the service. In other words, because ELA’s content (once developed) could be delivered at scale digitally, adding it to a hardware bundle or course bundle was very high-margin. For Byju’s, which is a business mindful of margins, this was a compelling figure. A 700% margin meant that if the cost to serve one user of ELA (in terms of server, development amortization, etc.) was, say, $1, we could effectively charge $7 extra in a bundle and people would pay it for the value they perceive – a huge return. This highlighted ELA’s potential as a profitable and scalable product when tied into the broader product suite.

Additionally, bundling had a strategic value: it could improve customer stickiness to the Byju’s ecosystem. If a student is using the tablet for general studies and also for learning English with ELA, they’re more deeply embedded in Byju’s platform and more likely to continue with other Byju’s offerings.

We also considered school partnerships or selling ELA licenses to schools as part of a bundle with Byju’s educational content, although the primary focus remained direct-to-consumer.

In summary, the monetization strategy was two-fold: direct premium subscriptions for those who come through app stores, and bundled sales through Byju’s existing channels to boost volume. We were careful to ensure that even with monetization, the free experience was valuable (to attract users) but the premium upsell was highly attractive (to convert users). This approach was quite successful in the pilot and early launch phases – we saw good conversion rates, and the bundling discussions were progressing with promising revenue forecasts.

(From a product manager perspective, seeing a 700% margin potential was exciting – it’s not often you get such a high margin product in edtech, which often has heavy content costs. This reinforced our belief that ELA could be a star product in the portfolio, both educationally and financially.)

Next Steps for LATAM Expansion

Given ELA’s success in its initial market, we also scoped out expansion into Latin American (LATAM) markets as a promising next step. Regions like Latin America present a huge opportunity for English learning products – there’s a large youth population and a strong demand for English proficiency. In fact, the Latin American e-learning market is growing rapidly (projected to reach over $50 billion by 2030, up from around $30B in 2024), which indicated fertile ground for a product like ELA.

Our expansion strategy for LATAM focused on a few key adaptations:

Localized Features: We planned to tailor the AI Buddy and content to support Spanish and Portuguese speakers for seamless adoption. This meant the app’s instructions, interface, and even some of the AI Buddy’s conversational capabilities would be adapted to the local languages. For instance, a child in Brazil might be greeted by the AI Buddy saying “Olá” and could ask for help in Portuguese if they got stuck. The core English learning content would remain, but any supporting text or guidance would be in the user’s native language to make it easy for non-English-speaking parents and beginners. Localization also extends to cultural context – using names, places, and references familiar to Latin American children in the examples and exercises. We knew that to succeed in LATAM, ELA must feel local even while teaching English.
Affordable Pricing: Adjusting price points was crucial for regional affordability. We researched the income levels and typical education spending in target countries like Mexico, Brazil, and Colombia. The plan was to offer lower subscription prices or installment options to match what the market could bear, while still maintaining value. We also considered offering more robust free content in those markets initially to gain traction. The idea was to ensure ELA is seen as accessible and worth it for families in LATAM. We might not achieve the same price as in, say, the US or India’s premium segment, but by scaling to millions of users, it could still be a big win. This pricing strategy was about finding the sweet spot so that ELA could penetrate the market quickly and not be held back by cost concerns.
Scalability to Underserved Regions: Latin America has a mix of urban centers and underserved rural or semi-urban areas where access to quality English education is limited. We aimed to leverage ELA’s asynchronous learning model to penetrate those regions. Since ELA doesn’t require live tutors and can work anywhere, it can scale geographically with relatively few barriers (just need a mobile device and internet). We planned to partner with local organizations or run marketing campaigns especially in areas where hiring English teachers is hard, pitching ELA as an affordable digital tutor for anyone with a smartphone. Scalability also meant ensuring our infrastructure could handle potentially a large influx of new users, and that the app could run well even on low-end Android devices common in those markets. The product team was confident that the same features that made ELA successful in its initial market – personalization, low-cost delivery, engaging AI – would make it attractive in LATAM, where there’s a real hunger for English learning solutions at scale. We envisioned ELA could become as impactful in LATAM as it was proving to be back home, by giving kids in, say, a small town in Mexico, the same opportunity to practice English as a kid in Mumbai or Bangalore had.

In summary, our strategy for LATAM was to speak the user’s language (literally and figuratively), price it right, and exploit ELA’s natural scalability. This international expansion was an exciting avenue, because it showed that the innovations of ELA weren’t confined to one market – they had global relevance wherever students needed help with conversational English.

A Missed Opportunity

With all the positive signs around ELA – strong engagement metrics, happy users, a 4.7 Play Store rating, and clear educational impact – one would think it’d be a top priority to scale it up. Yet, inside Byju’s, ELA struggled to gain the traction and focus it deserved. The crux of the issue was not the product’s quality or user love; it was a strategic and business conflict. ELA’s offering came at a relatively lower price point (or “ticket size”) compared to Byju’s flagship offerings like its Online Classes or one-on-one Tuition packages. Those flagship products are big revenue drivers (often costing hundreds of dollars per student annually). In contrast, ELA, being an affordable app, would generate less revenue per user. This created an internal hesitation: there were concerns that aggressively pushing ELA might cannibalize the sales of larger products. In other words, if a parent opted to spend time and money on ELA (which is lower cost and perhaps taking up the child’s study time), maybe they would be less inclined to buy a more expensive Byju’s course.

This kind of fear isn’t uncommon in big companies – sometimes a great new product is seen as a threat to existing cash cows. For example, Kodak famously invented the digital camera but initially suppressed it for fear of undercutting their film business. It’s a known cautionary tale of how internal dynamics can stifle innovation!
In Byju’s case, ELA was like an innovative “startup” within the company that needed nurturing, but the core business was focused elsewhere and potentially even viewing ELA as a distraction.

As a result, ELA received limited organizational focus and resources. It remained a bit under the radar, without big marketing campaigns or sales pushes. From a team perspective, this was disheartening. We had a product that by all user-facing metrics was doing great – but we couldn’t get the larger organization to rally behind it. The leadership’s attention was largely on products that brought in higher immediate revenue, which is understandable from a short-term business view, but it meant ELA’s growth was throttled not by user rejection, but by internal deprioritization.

This was a significant challenge, especially because ELA’s quality genuinely rivaled Byju’s flagship offerings in many ways. User experience, engagement, learning outcomes, user feedback – on each of these, ELA excelled, sometimes even outperforming bigger products. The 4.7/5 user rating on the Play Store was a testament to its quality (very few educational apps maintain such a high rating). We would hear comments like “This is the best English app for kids I’ve seen” from users. It’s rare to achieve both strong metrics and love in edtech, and ELA had both. However, in a company of Byju’s scale and trajectory at that time, if something wasn’t contributing big revenue or fitting the main narrative, it was hard to make it a core focus. ELA unfortunately fell into that category: an awesome product that didn’t align with the immediate big-money strategy.

In the end, despite ELA’s promise and all the hard work behind it, it failed to find its place as a core product within the company. It remained more of a side project when it could have been so much more with the right push. From the inside, it felt like watching a plant that could have grown tall, but was kept in a small pot. The experience carried a bittersweet lesson: sometimes, the success of a product isn’t just about building something great – it’s also about timing, internal alignment, and business strategy. And if those don’t line up, even a great product can stall.

(This “missed opportunity” for ELA has often made me reflect on how organizations should handle innovative projects. The fear of cannibalization can be shortsighted – companies that embrace new ideas, even at the cost of old ones, often end up leading the future. I believe, in a different scenario, ELA could have been scaled to millions of learners, potentially as a global offering. That it wasn’t, is something that still feels like a loss – but also a valuable experience.)

The Untapped Potential

Despite the outcome within Byju’s, I remain incredibly proud of what ELA achieved and what it demonstrated for the future of learning. ELA was a trailblazer in showing how AI-driven education could improve practical language skillsand personalize learning journeys for each student. It combined the best of technology (real-time AI feedback, adaptive algorithms) with solid pedagogy (communication practice, gamification, leveling), setting a model that others can follow. In many ways, ELA was ahead of the curve – it proved that young children will engage deeply with an AI tutor if it’s done right, and that they can significantly improve their skills outside of a traditional classroom.

The potential ELA showed was tremendous, not just as a single product, but as an approach to language learning. It could be a scalable and impactful tool for English education, particularly in markets or regions where access to quality English teachers is limited. For example, imagine a rural area where schools don’t have good English classes – a solution like ELA (on a tablet or phone) could provide those kids with a near-equivalent of a personal tutor, at a fraction of the cost. That’s transformative. In places around the world, from India to Latin America to Africa, there are millions of children who need help to improve their English to access better opportunities. A product like ELA can bridge that gap at scale.

ELA’s story, in the end, serves as a powerful reminder of the potential of innovation in education when paired with the right strategy and focus. We saw firsthand that innovation (like the AI Buddy, adaptive learning, etc.) can unlock new ways of learning and achieve outcomes traditional methods struggled with. However, we also saw that without the right strategy and organizational support, even the best innovation can remain untapped. The concept of ELA – an AI English tutor for kids – is something I strongly believe the world will see more of. In fact, other startups and companies are now pursuing similar ideas, validating that the vision was sound.

While ELA as our project may not have scaled to its zenith, the idea behind it still holds immense promise. I hope that its legacy contributes to future educational products. Sometimes a pioneer doesn’t reach the widest audience, but it paves the way for those who come next. In that sense, ELA was a pioneer. It showed what was possible by blending interactive technology with human-centric learning design. The lessons we learned through ELA – about engagement, personalization, and even the pitfalls of corporate strategy – are all part of that legacy.

In conclusion, the journey of Byju’s AI Tutor (ELA) was filled with innovations and achievements that demonstrated a new path for language learning. Its ultimate hurdle was not a lack of efficacy or user love, but the complexities of business strategy. However, the vision we carried is very much alive: using AI to make learning personalized, engaging, and effective. ELA’s untapped potential continues to inspire us and, I believe, will inspire others in the edtech community to build the next generation of human-centric, AI-powered learning tools. The experience reinforced my faith in the idea that when the right technology meets the right educational need – and when supported by the right strategy – the impact can be groundbreaking. ELA was a glimpse of that impact, and I’m confident the future holds even more.