TalkDrill Team
English Learning ExpertsThe promise sounds almost too good: talk to an AI on your phone for 20 minutes a day, and your English will improve. No expensive tutor. No embarrassment in front of classmates. No scheduling headaches. Just you, a screen, and an AI that never judges you.
But does it actually work? Or is it just another edtech fantasy dressed in slick marketing?
We wanted a real answer. Not a press release, not a product demo. So we ran an experiment. We asked five TalkDrill users to practice English exclusively with AI for 30 consecutive days. No human tutors, no conversation groups, no English-speaking friends as practice partners. Just AI. Every day. For a full month.
What we found was encouraging, surprising, and honest about the limitations. Here's the full breakdown.
Key Takeaways
A 2024 report from Duolingo's shareholder letter revealed that AI-powered conversation features drove a 40% increase in user engagement compared to traditional exercise formats (Duolingo Q4 2024 Shareholder Letter, 2024). That's a massive number. But engagement isn't the same as learning.
We kept hearing the same question from users: "Is talking to AI enough, or do I still need a human tutor?" Fair question. The honest answer is that most studies measure AI tools alongside human instruction, not in isolation. Very few researchers have tested what happens when AI is the only practice partner.
So we decided to test it ourselves. Not in a lab. Not with college students being paid to participate. With real Indian adults who genuinely wanted to improve their spoken English.
Citation Capsule: Duolingo's 2024 shareholder data showed that AI-driven conversation features increased platform engagement by 40% over traditional drill-based exercises, but the company's own research team acknowledged that engagement metrics don't directly measure speaking proficiency gains.
We chose this experiment because our own support inbox was full of users asking a simple question: "Can I get fluent with just the app?" We owed them an honest answer, not a marketing one.
The group represented a realistic cross-section of Indian adults learning English. According to the British Council, approximately 10% of English learners in India can speak fluently despite years of formal study (British Council India, 2019). Our participants fit squarely into that other 90%.
Here's who signed up:
We recorded three metrics at Day 0 and Day 30:
All baseline and final recordings were evaluated by two independent English language assessors who didn't know which recording was "before" and which was "after." This double-blind setup removed confirmation bias from the scoring.
Each participant committed to a minimum of 20 minutes per day of spoken English practice using AI conversation tools. A 2023 meta-analysis in Computer Assisted Language Learning found that 15-30 minutes of daily speaking practice produces measurable fluency improvements within 4-8 weeks (Computer Assisted Language Learning, 2023). We targeted the middle of that range.
The daily routine wasn't rigid, but it followed a loose structure:
Participants were allowed to choose topics freely. Some days, Ravi practiced explaining technical concepts. Other days, Meena rehearsed parent-teacher meeting conversations. The key constraint was simple: no human practice partners for the full 30 days.
Was that rule hard to follow? Absolutely. Sneha admitted she almost broke it during a team meeting at work. "I realized that was technically real English practice," she laughed. "But I stuck to the rule. The meeting was in Hindi anyway."
The average speaking speed across all five participants increased from 87 WPM to 108 WPM, a 24% improvement. Research from Cambridge University Press suggests that conversational fluency typically falls in the 120-150 WPM range for non-native speakers (Studies in Second Language Acquisition, 2020). Our participants weren't there yet, but the trajectory was clear.
Here's the full breakdown.
| Participant | Day 0 | Day 30 | Change |
|---|---|---|---|
| Ravi | 102 | 124 | +22% |
| Sneha | 84 | 105 | +25% |
| Arjun | 95 | 118 | +24% |
| Meena | 68 | 88 | +29% |
| Farhan | 72 | 91 | +26% |
Meena showed the largest percentage gain. That makes sense. Learners starting from a lower baseline typically see faster initial improvement, a pattern consistent with second language acquisition research.
| Participant | Day 0 | Day 30 | Change |
|---|---|---|---|
| Ravi | 8 | 5 | -38% |
| Sneha | 12 | 7 | -42% |
| Arjun | 9 | 6 | -33% |
| Meena | 14 | 10 | -29% |
| Farhan | 11 | 7 | -36% |
Average filler reduction: 35%. That's significant. Fewer filler words generally signal that the brain is retrieving vocabulary faster, reducing the need for "um" and "basically" as placeholder sounds while thinking.
| Participant | Day 0 | Day 30 | Change |
|---|---|---|---|
| Ravi | 5 | 7 | +2 |
| Sneha | 3 | 6 | +3 |
| Arjun | 6 | 8 | +2 |
| Meena | 2 | 5 | +3 |
| Farhan | 3 | 5 | +2 |
Average confidence increase: 2.4 points on a 10-point scale, or roughly 42% relative to their baselines. Sneha and Meena, the two participants with the lowest starting confidence, showed the biggest jumps.
The confidence scores were self-reported, which introduces subjectivity. However, the independent assessors confirmed that the confidence gains matched their own qualitative observations. "You can hear the difference," one assessor noted. "The Day 30 recordings sound like someone who's gotten used to speaking."
Citation Capsule: In a 30-day AI-only English practice experiment with five Indian adult learners, average speaking speed increased by 24% (87 to 108 WPM), filler word usage dropped by 35%, and self-rated confidence rose by 42%, with the strongest gains among lower-proficiency participants.
Three specific benefits stood out clearly. According to a 2023 NBER working paper on AI tutoring, the low-stakes nature of AI interaction significantly reduces performance anxiety, particularly in learners who report high levels of speaking fear (NBER Working Paper, Korinek, 2023). Our experiment confirmed this.
Every participant mentioned this independently. Speaking to AI removed the fear of embarrassment entirely. Meena put it best: "With a person, I think about what they're thinking about me. With AI, I just think about what I want to say."
This isn't a small thing. For millions of Indian adults who understand English perfectly well but freeze when they need to speak it, the psychological barrier is the real problem. AI removes that barrier completely.
Ravi practiced explaining the same technical concept seven times in one session. Try doing that with a human tutor without feeling awkward. With AI, he could restart, rephrase, and refine without anyone rolling their eyes.
Repetition is how fluency is built. You don't get fluent by saying something once. You get fluent by saying the same thing enough times that it stops requiring conscious effort. AI makes that kind of repetition painless.
All five participants completed at least 27 of 30 days. That's a 90% adherence rate. By comparison, research on language tutoring attendance shows that students using scheduled human tutors average about 65-70% session attendance over a month. Why the difference?
Availability. No booking slots, no cancellation guilt, no rescheduling. Farhan practiced at 11 PM most nights after finishing his delivery shifts. Meena practiced during her daughter's afternoon nap. The AI was ready whenever they were.
We've seen this pattern across thousands of users: when the friction of scheduling disappears, practice frequency goes up. The best session is the one that actually happens.
The results weren't all positive. If we only reported the wins, you'd have every reason to distrust us. A 2024 study in Language Learning and Technology found that AI conversation partners currently struggle with pragmatic competence training, meaning they can't reliably teach when to be formal versus casual, how to read social cues, or why certain phrases land differently in different contexts (Language Learning and Technology, 2024). Our participants noticed this gap firsthand.
Four of the five participants reported that improvements felt "slower" in the final week. Their WPM and filler metrics confirm this. Most of the measurable gains happened in weeks 1-3. Week 4 showed minimal additional improvement.
This is consistent with learning curve theory. Early gains come from removing obvious bottlenecks (hesitation, filler habits, vocabulary retrieval speed). But after those quick wins, further improvement requires more complex input: nuanced feedback, cultural context, real-time social dynamics. AI isn't great at providing those yet.
Without a human expecting them to show up, two participants (Farhan and Meena) skipped 3 days each. With a paid human tutor, they said they wouldn't have skipped even once. "If I'm paying someone Rs 500 to wait for me," Farhan said, "I'll be there."
AI is patient. Maybe too patient. It doesn't guilt-trip you for missing a day. For some learners, that guilt is actually useful motivation.
Arjun practiced mock interview answers and felt confident. Then he did a real mock interview with a friend and realized his answers sounded "technically correct but robotic." The AI had helped him form complete sentences, but it hadn't taught him when to add a personal anecdote, when to pause for effect, or how to match the interviewer's energy.
Would he have caught that without the human comparison? Probably not. And that's the core limitation: AI teaches you to speak correctly, but it doesn't always teach you to speak naturally.
In real conversations, misunderstandings happen. You say something unclear. The other person looks confused. You rephrase. That recovery skill, the ability to detect and repair communication breakdowns in real time, barely got exercised during AI practice because the AI almost always "understood" what participants meant, even when their phrasing was awkward.
Citation Capsule: A 2024 analysis in Language Learning and Technology found that AI conversation partners demonstrate significant weaknesses in teaching pragmatic competence, including contextual formality, social cue reading, and repair strategies for communication breakdowns, areas where human interaction remains essential for language development.
Our findings align closely with larger academic studies. A 2023 meta-analysis published in Computer Assisted Language Learning reviewed 42 studies on AI-assisted language practice and found that AI tools produce measurable gains in vocabulary, grammar accuracy, and speaking fluency within 4-8 weeks of regular use (Computer Assisted Language Learning, 2023). However, the same analysis noted that gains in pragmatic competence and sociolinguistic awareness were minimal without human interaction.
The NBER working paper by Korinek (2023) found that AI tutoring tools improved learning outcomes by approximately 0.2 standard deviations compared to no tutoring at all, but still trailed expert human tutors (NBER Working Paper, 2023). Our experiment sits right in that gap: clear improvement over no practice, but not a complete replacement for human guidance.
What surprised us most was the confidence data. The raw fluency numbers (WPM, filler reduction) were useful but expected. The confidence gains were harder to predict and, arguably, more valuable for this audience. For Indian adults whose primary barrier is speaking hesitation rather than grammatical knowledge, the judgment-free repetition that AI provides might be more important than any specific skill improvement. Confidence isn't a soft metric here. It's the difference between someone who stays silent in meetings and someone who speaks up.
Based on our experiment and the research, the most effective approach combines AI for daily practice with periodic human interaction. A study in ReCALL (Cambridge University Press, 2024) found that learners using AI tools alongside monthly human coaching sessions outperformed both AI-only and human-only groups on overall communicative competence (ReCALL, 2024).
Here's the framework our participants wish they'd followed from the start:
Yes, with a caveat. AI-only practice produced real, measurable improvements in speaking speed (24% average increase), filler word reduction (35% decrease), and self-rated confidence (42% increase) over 30 days. Those aren't trivial gains. For someone who freezes before every English conversation, that kind of progress changes daily life.
But "learn English" is a broad claim. If by "learn" you mean build vocabulary, improve fluency, and gain confidence, then yes, AI works. If you mean develop full communicative competence, read social cues, handle misunderstandings gracefully, and speak with the cultural awareness of someone who's practiced with real humans, then no, AI alone isn't enough. Not yet.
The most useful takeaway from our experiment isn't that AI is perfect or broken. It's that AI solves the biggest problem most Indian English learners face: not having a regular, judgment-free, always-available practice partner. For millions of people who understand English but never speak it because they don't have anyone to practice with, that single problem is the entire bottleneck.
AI removes the bottleneck. What you build beyond that still depends on you.
Try the same experiment: 30 days on TalkDrill. Track your WPM, filler words, and confidence. See the results for yourself.
Not yet. AI matches human tutors on grammar drilling and vocabulary building, according to a 2024 ReCALL study (Cambridge University Press, 2024). But human tutors still outperform AI on pragmatic skills, cultural context, and accountability. The most effective approach combines both: daily AI practice with periodic human sessions.
Research in Computer Assisted Language Learning (2023) suggests 15-30 minutes of daily speaking practice produces measurable fluency improvement within 4-8 weeks (CALL, 2023). In our experiment, participants averaged 22 minutes per day and saw significant gains. Consistency matters more than session length.
Both work, but for different reasons. Self-talk builds vocabulary retrieval and reduces mental translation. AI conversation adds an interactive element: you respond to questions, handle unexpected topics, and practice real dialogue patterns. The AI also provides pronunciation feedback and tracks your progress, which self-talk can't do.
Partially. Our participant Arjun improved his interview answer fluency significantly but found his responses sounded "technically correct but robotic" in a real mock interview. AI is excellent for building the base fluency needed for interviews. Pair it with at least 2-3 practice sessions with a real person before the actual interview.
In our 30-day experiment, participants noticed subjective improvements within the first week. Measurable WPM and filler word improvements appeared by Day 14. The biggest gains happened in weeks 2-3, with a plateau emerging in week 4. Research suggests continued improvement requires adding complexity, through new topics, harder scenarios, or human interaction.
This experiment was conducted internally with volunteer TalkDrill users in early 2026. Sample size is small (n=5) and results are observational, not peer-reviewed. We share them transparently as a real-world data point, not a definitive study. Your results will vary depending on your starting level, consistency, and how you practice.
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