openclaw/docs/youtube/how-i-used-ai-to-improve-my-english-for-open-source-collaboration.md
⚠️ Note: This is a transcript from a specific date. Information may be outdated. Do not treat as authoritative — verify against current codebase and documentation.
As a software engineer and open-source community maintainer, English communication is essential for my daily work. For a long time, my English listening and speaking were a real bottleneck, even after working in an English environment.
In early 2025, my CELPIP scores for listening and speaking were both 6, and I still depended heavily on subtitles in meetings and avoided speaking when possible. After using AI consistently for daily listening and speaking practice, my scores improved to 8, and more importantly, my real-life performance changed: I can now follow meetings without subtitles and express my ideas more naturally.
In this article, I share how I used AI to improve my English listening and speaking so I could communicate more effectively in a global open-source community.
There is a common belief that once you live in an English-speaking environment, language skills will naturally improve within a few months. My experience proved otherwise. I work in Canada, my colleagues are mostly in the United States, and English is the working language. Yet after many months, my listening and speaking were still weak. The reality is simple: environment alone does not guarantee practice, and exposure does not automatically turn into ability.
I need to learn English not because one language is better than another, but because English is the shared working language of software development. Code, documentation, and open-source communities all rely on English. Writing and communicating in English makes collaboration easier for others, even if it costs me more effort. Relying on translation tools every time creates friction, especially in real-time discussions where clarity and speed matter.
Speaking became my biggest bottleneck because it is the easiest skill to avoid. In daily work, I could listen more than I spoke. Reading felt familiar as a programmer, and writing could always be revised. Speaking, however, requires instant output. There is no time to think, no chance to edit. Over time, avoidance turned into habit, and habit turned into low confidence.
This gap showed clearly in both my work and my test scores. Other skills improved slowly, but speaking stayed behind. The problem was not a lack of effort or discipline — it was the lack of sustained, low-pressure speaking practice. Realizing why I needed English, and why speaking was holding everything back, was the first step toward changing how I learned.
Listening was the first skill I chose to focus on, because without it, everything else felt unstable. If I could not understand what others were saying, speaking confidence was impossible, and meetings were exhausting. I eventually realized that weak listening was the real bottleneck behind most of my communication problems.
The most important mindset shift was letting go of the idea that listening practice only works when I understand everything. For years, I treated listening like reading comprehension: if I did not understand a sentence, I replayed it, translated it, or gave up. This approach made listening stressful and inefficient. Instead, I adopted a more natural rule: keep listening, even when large parts are unclear. Understanding comes later, not first.
Content choice mattered more than difficulty. Simplified materials, such as children’s cartoons, did not work well for me because I was not genuinely interested in them. I switched to content I actually cared about, such as news analysis, long-form discussions, and TED-style talks. Even when I understood very little at the beginning, interest kept me engaged long enough for improvement to happen.
AI supported this process, but not by translating everything. I tried to avoid direct translation as much as possible. When a word or phrase felt important, I paused and asked AI about it in English. The explanation itself became additional listening input and helped me stay in English instead of switching back to my native language.
For content I found especially interesting, I combined extensive and intensive listening. I listened first for overall meaning, then revisited parts with subtitles or AI assistance. Because the topic already made sense, details became easier to absorb without frustration.
Over time, listening stopped feeling like training and became a natural habit. I was no longer practicing English; I was simply listening to things I wanted to understand. The biggest value of AI was not intelligence or accuracy, but patience. It allowed me to stay in English longer, with less stress, until my listening ability finally caught up.
After rebuilding my listening skills, I realized that speaking was still lagging behind. I could understand meetings much better, but when it was my turn to talk, I often hesitated or chose silence. Listening alone was not enough. Speaking required deliberate, sustained output, and it was the part I had avoided the longest.
Speaking is fundamentally different from reading and writing. When speaking, there is no time to think carefully or revise sentences. You must produce language instantly, even when your thoughts are unclear. This pressure made speaking easy to avoid in real life, especially at work, where listening quietly was usually acceptable.
AI changed this dynamic by removing social pressure. I could speak freely without worrying about judgment, impatience, or embarrassment. Mistakes were allowed, pauses were acceptable, and repetition was unlimited. This low-pressure environment made daily speaking practice possible for the first time.
One key mistake I made early on was letting AI lead the conversation. Casual, unfocused chatting quickly became boring and ineffective. I learned that speaking practice works best when I bring my own content. If I had nothing real to say, the practice had little value.
Daily life turned out to be the best source of speaking material. I described what I was cooking, summarized news I had just listened to, explained my work tasks, or talked through my daily diary aloud. When I got stuck, AI helped me rephrase or offered alternative expressions, which I immediately reused in my own speech.
I also combined speaking with listening instead of treating them as separate skills. While listening to news or reading books, I discussed unfamiliar words with AI in English instead of translating them. Explaining a word back in my own sentences helped turn passive vocabulary into active speaking ability.
Another important habit was learning to pause while speaking. I noticed that I tended to speak too fast, with few breaks, which hurt clarity and fluency. By consciously adding pauses between sentences, I gained time to think and made my speech easier to follow.
Over several months, speaking slowly became more natural. I was no longer practicing isolated sentences; I was practicing expressing real thoughts. The improvement showed not only in test scores, but in meetings, discussions, and my willingness to speak up.
AI did not make me fluent automatically. What it did was make consistent speaking practice possible. By lowering friction and pressure, it allowed speaking to become a daily habit rather than a stressful event. That consistency was what finally moved my spoken English forward.
Reaching a CELPIP score of 8 showed me that my approach was working, but it also made my next goal clearer. I want to continue practicing until I can reach a CELPIP 10, which, to me, represents the ability to express myself fluently and precisely in English. This is not just about a test score, but about communicating ideas naturally without hesitation.
I do not expect this level to come from shortcuts or tricks. Progress at this stage depends almost entirely on time, repetition, and consistency. My plan is simple: keep practicing every day and let improvement accumulate gradually.
At the moment, I spend about four to five hours a day on English. This time is not arranged as formal study blocks. Instead, it is integrated into my daily routine so that learning feels sustainable rather than exhausting.
Every morning, while preparing breakfast, I listen to English news or podcasts. This has become a stable listening habit rather than a conscious training session. It helps me stay exposed to natural speech patterns and current topics without adding pressure.
After listening, I usually discuss my thoughts with AI. Sometimes this takes the form of a spoken diary, where I talk through what I heard or reflect on my own day. Once I can express the ideas clearly in speech, I write them down. This process connects listening, speaking, and writing into a single loop.
I plan to continue refining this routine and adjusting it as my ability improves. The goal is not to rush toward a number, but to make fluent English expression a natural part of my thinking and communication. As this process continues, I will share what works, what changes, and how far consistent practice can really take me.
In the end, improving my English was not about talent, environment, or finding the perfect method, but about making long-term practice possible. AI did not replace effort or thinking; it removed friction and lowered the cost of daily listening and speaking, which allowed consistency to take over. By turning English into part of my everyday life rather than a separate task, real progress became inevitable. This experience convinced me that with enough meaningful input, honest output, and sustained time, fluent communication is not a privilege reserved for a few, but a reachable outcome for anyone willing to keep going.