Inside Praktika's conversational approach to language learni…
OpenAI NewsPraktika 源于一个非常个人化的体会：语言能开启机会。
联合创始人 Adam Turaev 、 Anton Marin 与 Ilya Chernyakov 都是在移民家庭中长大，随着家人到新国家寻求更好的生活，他们不得不学会在异国环境中生存。对他们来说，学会英语不仅是为了上学，更关系到工作、流动性和归属感。
“学英语从来不只是为了交流，” Adam Turaev 说，“它为我们打开了国际工作和职业发展的门。”
但传统的语言教学未能真正满足需求。创始人们发现，尽管多年学习后在读写上已很流利，但在关键时刻——工作场合、会议上或日常交流中——却难以自信地说出来。课堂学习与现实流利度之间的差距比他们想象的要大得多。
为弥合这一差距，团队打造了 Praktika 。这是一款通过每日会话帮助学习者建立真实沟通能力的语言学习应用，配备个性化的 AI 导师，提供互动性强、以目标为导向的课程。用户包括备考学生、为职场需求打磨语言技能的专业人士，以及在国外重新安家的移民群体。
构建一个会适应并即兴调整的多代理辅导系统
随着产品成熟， Praktika 从单一模型架构演进为多代理系统，旨在模拟真实导师在课堂中实时调整课程的方式。
Lesson Agent 是主要的对话代理，扮演导师角色。它运行在 GPT‑5.2 ，将导师个性、课程情境、学习目标和最近的对话结合起来，提供自然、不显程式化的课程体验——这是系统开始更像真人导师而非预设脚本的重要节点。
在后台持续运行的 Student Progress Agent 跟踪学习者在各次互动中的语言表现。该代理同样运行于 GPT‑5.2 ，监测流利度、准确性、词汇使用和反复出现的错误。这些数据形成一个持续反馈回路，既影响 Lesson Agent 在单次课程中的行为，也指导长期学习策略，使学习体验能够随着时间自然演进。
Learning Planning Agent 专注于塑造学习者的长期进展。它以学习者的个人目标为依据，利用来自 Student Progress Agent 的洞见决定下一步学什么、如何安排技能顺序以及哪些活动最有效。该代理由 GPT‑5 Pro 提供动力，其职责是不断调整学习计划，使进展保持个性化、高效并与学习者预期结果一致。
图示说明了一个基于语音的 AI 辅导系统：用户通过语音界面发言，音频经由 push-to-talk 或使用 Voice Activity Detection ( VAD ) 的连续语音检测处理；语音经 speech-to-text 模型转为文本，生成的文字记录存入课程会话历史。课程会话输入 Lesson Agent ( GPT‑5.2 )，它与记忆模块交互，并将学生进展数据传给 Student Progress Agent ( GPT‑5.2 )。学生技能被追踪并传递给 Learning Planning Agent ( GPT‑5 Pro )，后者运用方法论生成学习目标。Lesson Agent 在需要时也会调用 Web AI Agent ( GPT‑4.1 )。Lesson Agent 输出文本后再经 text-to-speech 转成语音回放给用户，完成闭环。
所有代理共享一个持久记忆层，用来存储学习者的目标、偏好和历史错误。Praktika 并不在对话前预先加载大量上下文，而是在学习者一说完话后立即检索记忆，确保回应基于最相关、最新的信息。
“如果学习者对某个练习不感兴趣，系统可以立刻切换到完全不同的练习，” Turaev 说，“这才有魔力，感觉更接近真人导师。”
让 AI 对话更像现场互动
要让会话式学习显得自然，记忆必须像现实中一样运作。 Praktika 的记忆层仅在学习者说完话后检索相关上下文，这样导师能回应刚刚说出的内容，而不是对先前的预判。
“如果学习者此刻犯了错，导师会回应这个错，而不是回应昨天的错误，” 联合创始人兼 CEO Adam Turaev 说。“这种时间上的差别很微妙，但正是它让互动显得专注而非机械。”
语音识别在这里也起到类似作用。语言学习者常常犹豫、重来句子、发音不准。 Praktika 使用 Transcription API 来更可靠地处理零碎、有口音和非母语的语音，这比传统基于流利语音训练的系统更宽容，让学习者能把注意力放在沟通上，而不是因为初学者状态而被“判错”。
记忆时机与语音识别共同构成一个单一回路：认真听、召回恰当上下文、立即回应。
把模型进步转化为更高效的学习体验
早期的 Praktika 将有表现力的虚拟形象与基于规则的 NLP 及早期 davinci 模型配对，但对话仍显得受限。随着 GPT‑3.5 的出现，团队迎来第一次重大突破。
“那是我们首次把先进的语言理解与富有表现力、逼真的虚拟形象结合起来，” Adam Turaev 说。“对话不再像脚本，而是变得自然、有情感、真实。”
在评估更新模型时， GPT‑4.1 在他们的内部指标上表现最佳，这些指标包括入门完成率、首日留存、试用转付费率和定性用户反馈。
“ GPT‑4.1 在推理深度、情感细腻度和可靠性之间给了我们最好的平衡，” Turaev 说。“它支持多语言对话和复杂的教学逻辑，显著提升了会话质量。”
这些模型改进直接反映在用户和业务结果上。引入新的长期记忆系统后， Praktika 的首日留存提高了 24%，营收在数月内翻倍。
最近， Praktika 开始使用 GPT‑5.2 系列模型来驱动其架构：主要对话代理由 GPT‑5.2 提供动力，监督推理由 GPT‑5.2 Pro 负责，持续进度跟踪由 GPT‑5 mini 支持。这些模型并行推理，使系统在大规模下同时兼顾对话质量、教学效果和效率。
探索学习语言的新方式
如今， Praktika 支持数百万学习者、覆盖九种语言，并在持续扩展。随着代理式基础搭建完成，团队正专注于扩展 AI 导师的理解、记忆和创造能力，使它能与每位学习者共同构建内容。
“我们不仅在教语言，” Turaev 说，“我们在打造能让人们在现实世界中自信使用语言的 AI。”
Praktika was born from a deeply personal insight: language unlocks opportunity.
Co-founders Adam Turaev, Anton Marin, and Ilya Chernyakov all grew up navigating new countries after their families immigrated in search of better opportunities. English quickly became essential, not just for school, but for work, mobility, and belonging.
“Learning English was never just about communication,” Turaev said. “It opened doors to international work and career growth.”
But traditional language education fell short. Despite years of study, the founders found that while they could read and write fluently, they struggled to speak confidently when it mattered most: at work, in meetings, and in daily life. The gap between classroom learning and real-world fluency was wider than they’d imagined.
Praktika was built to close that gap. It’s a language learning app designed to help people build real-world fluency through daily conversations, with personalized AI tutors who guide them through interactive, goal-based lessons. Users include students preparing for exams, professionals working on job-related language skills, and immigrants building new lives in foreign countries.
Building a multi-agent tutoring system that adapts and improvises
As the product matured, Praktika moved beyond a single-model architecture into a multi-agent system designed to mirror how real tutors adapt lessons in real time.
Lesson Agent is the primary conversation agent, interacting with learners as the tutor. Running on GPT‑5.2, it blends tutor personality, lesson context, learner goals, and recent conversations to deliver lessons that feel natural and unscripted. This is the point where the system starts to feel like a real tutor rather than a scripted experience.
Running continuously in the background, Student Progress Agent tracks the learner’s language performance across interactions. Using GPT‑5.2, this agent monitors fluency, accuracy, vocabulary usage, and recurring mistakes. This data forms a continuous feedback loop that informs both the Lesson Agent’s in-session behavior and the longer-term learning strategy, allowing the experience to evolve naturally over time.
Learning Planning Agent focuses on shaping the learner’s long-term progression. Grounded in the learner’s individual learning goal, it uses insights from the Student Progress Agent to determine what to learn next, how to sequence skills, and which activities will be most effective. Powered by GPT‑5 Pro, its role is to continuously adapt the learning plan so progress remains personalized, efficient, and aligned with the learner’s desired outcome.
All agents share access to a persistent memory layer that stores learner goals, preferences, and past mistakes. Rather than preloading context, Praktika retrieves memory immediately after the learner speaks, ensuring responses are grounded in the most relevant, up-to-date signal.
“The system can switch to a completely different exercise if the learner isn’t feeling it,” says Turaev. “That brings the magic back. It starts to feel much closer to a real human tutor.”
Making AI conversations feel like a live exchange
For conversational learning to feel natural, memory has to work the way it does in real life. Praktika’s memory layer retrieves relevant context only after the learner finishes speaking. That allows the tutor to respond to what was just said, not what it anticipated.
“If a learner makes a mistake right now, the tutor responds to that mistake, not one from yesterday,” says co-founder and CEO Adam Turaev. “That timing difference is subtle, but it’s what makes the interaction feel attentive instead of robotic.”
Speech recognition plays a similar role. Language learners hesitate, restart sentences, and pronounce words imperfectly. Praktika uses Transcription API to handle fragmented, accented, and non-native speech more reliably than traditional systems trained on fluent speech. That lets learners focus on communicating without being penalized for their beginner status.
Together, memory timing and speech recognition form a single loop: listen carefully, recall the right context, and respond immediately.
Turning model improvements into more effective learning experiences
Early versions of Praktika’s product paired expressive avatars with rule-based NLP and the first davinci models, but conversations still felt constrained. With the release of GPT‑3.5, the team experienced its first major breakthrough.
“For the first time, we could merge advanced language understanding with expressive, lifelike avatars,” says Adam Turaev. “The conversations stopped feeling scripted. They became natural, emotional, and real.”
As Praktika evaluated newer models, GPT‑4.1 proved to be the strongest fit across its internal evaluations measuring onboarding completion, Day-1 retention, trial-to-paid conversion, and qualitative user feedback.
“GPT‑4.1 gave us the best balance of reasoning depth, emotional nuance, and reliability,” says Turaev. “It supported multi-language conversation and complex tutoring logic at the quality we needed, significantly increasing conversation session quality.”
Those improvements translated directly into user and business results. After introducing their new long-term memory system, Praktika saw a 24% increase in Day-1 retention and doubled revenue in just a few months.
More recently, Praktika began using GPT‑5.2 models to power its architecture. GPT‑5.2 now powers the primary conversation agent, while GPT‑5.2 Pro handles supervisory reasoning and GPT‑5 mini supports continuous progress tracking. Together, these models allow the system to reason in parallel, balancing conversation quality, pedagogy, and efficiency at scale.
Exploring new ways to learn a language
Today, Praktika supports millions of learners across nine languages, with more on the way. With its agentic foundation in place, Praktika is now focused on expanding what an AI tutor can understand, remember, and create alongside each learner.
“We’re not just teaching languages,” says Turaev. “We’re building AI that helps people feel confident using them in the real world.”
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