PROGRESSIVE CONVERSATIONAL FOREIGN LANGUAGE LEARNING: An LLM-First, Adjustable Immersion System for Everyday Conversation; An Open-Source Secretary Suite

PROGRESSIVE CONVERSATIONAL FOREIGN LANGUAGE LEARNING

An LLM-First, Adjustable Immersion System for Everyday Conversation

An Open-Source Secretary Suite

John Swygert  |  October 26, 2026  |  Concept and technical specification

Abstract

This open-source proposal specifies a progressive foreign-language learning layer designed primarily for large language model (LLM) conversational systems. It introduces an optional, adjustable percentage of a target language into normal dialogue—3%, 5%, 10%, 25%, 50%, 75%, or 100%—without replacing the user’s original purpose. LLMs are the primary implementation environment because they can sustain contextual, adaptive, open-ended dialogue, select appropriate phrases, and respond to comprehension signals. The same model-independent open-source design may subsequently extend to messaging applications, voice assistants, browsers, operating systems, and educational software. Independent exposure and difficulty controls, optional translations, local-first mastery records, and measurable semantic-fidelity requirements support incremental learning without separate lessons.

1. The Opportunity and Primary LLM Use Case

Language study often competes with everyday activities for time and attention. LLM-based conversational assistants create an opportunity to turn the conversations people already have into voluntary, contextual language exposure. A user may discuss music, research, cooking, writing, or everyday tasks while learning words and expressions relevant to that conversation. The primary product is therefore an LLM conversation feature, not a standalone course or a generic operating-system setting. Non-LLM applications are supported through optional adapters and may offer reduced capabilities when contextual generation is unavailable.

2. Core Principle

Teach without hijacking the conversation. The assistant must first satisfy the user’s actual request. Language exposure is a configurable secondary layer, never an excuse to omit requested details, change technical meaning, or obscure important instructions.

3. User Experience

A user chooses the target language, immersion level, difficulty, translation policy, and optional correction mode. Default introduction is 3% exposure, with short inline glosses. A sample interaction might read: “Ja, natürlich! (Yes, naturally!) Otto has done it again.” Familiar phrases later appear without glosses. A one-tap “explain” command restores the translation and provides pronunciation or grammar help.

4. Two Independent Controls

Exposure measures the intended share of assistant-generated conversational language in the target language. Difficulty controls vocabulary frequency, sentence length, idiom density, and grammar complexity. A user can therefore request 25% beginner German or 5% advanced German. These settings must not be collapsed into one slider.

5. Exposure Schedule

Suggested presets: 3% (gentle introduction), 5% (light), 10% (regular), 25% (mixed conversation), 50% (balanced bilingual), 75% (target-language dominant), and 100% (full immersion). The percentage is a soft target calculated over a rolling window of eligible conversational words, not a rigid quota per sentence. Code, quotations, proper names, formulas, citations, and user-requested verbatim outputs are excluded from the denominator.

6. Contextual Teaching Engine

The system preferentially introduces terms relevant to the current topic, reuses recently learned material, and avoids abrupt substitutions that distort meaning. Short, common phrases appear before complex clauses. Target-language insertions should be semantically equivalent to what would otherwise be said in the base language. An explanation mode can reveal translations, pronunciation, usage, and alternate phrasing on demand.

7. Mastery and Repetition

Each introduced phrase receives a local learning record: phrase, meaning, first exposure, last exposure, approximate comprehension confidence, and next review priority. Comprehension can be inferred cautiously from explicit user signals (e.g., “I know that,” “translate,” or a correct paraphrase), never asserted as a diagnosis of learning. A simple spaced-review queue can increase reuse of uncertain expressions and retire redundant glosses for well-understood ones. Users can reset or delete this record.

8. LLM-Native Conversational Integration

The primary runtime is an LLM chat interface or LLM-powered assistant. An instruction or middleware layer reads the user’s target language, exposure setting, difficulty, translation preference, and permitted mastery signals; it then guides the model to weave accurate target-language material into its response. The system must remain compatible with different model providers and locally hosted models. A model can generate candidate phrasing, while a separate validation layer checks protected spans, semantic fidelity, and approximate exposure. LLM integration must not assume privileged access to model weights or training pipelines; a prompt-level adapter is sufficient for an initial prototype, while application-level APIs can support stronger measurement and persistence.

9. Safety, Accessibility, and Boundaries

Keep medical, legal, emergency, financial, and other consequential guidance fully intelligible in the user’s chosen primary language unless explicitly requested otherwise. Do not modify commands, addresses, quantities, filenames, or quoted text. Provide opt-out per conversation and per message; support screen readers, pronunciation playback when available, dialect choice, and user-selected accessibility accommodations. Do not imply that approximate exposure percentages are exact linguistic measurements.

10. LLM-First, Model-Independent Open Architecture

The reference implementation is open source and LLM-first but not bound to a particular model vendor. The core consists of a configuration schema, contextual selection policy, glossing rules, learning-state store, fidelity checks, and integration adapters. Priority adapters are for LLM chat applications and API-driven assistants; secondary adapters may support messaging clients, browser extensions, voice interfaces, educational apps, and operating-system assistants where appropriate. Secondary applications may use an LLM service or a simpler rule-based implementation, with capability differences disclosed. Preference and mastery records should be local-first, exportable, deletable, and encrypted when persisted. Telemetry is opt-in and excludes conversation content by default. Secretary Suite / COSMOSIS is a proposed integration destination, not a required dependency.

11. LLM Reference Processing Pipeline

(1) Receive the user request and immersion preferences in the LLM application. (2) Identify exact-output and high-stakes spans that must remain unchanged or clearly intelligible. (3) Draft or generate a response that fulfills the user’s primary request. (4) Identify eligible conversational spans and relevant target-language candidates. (5) Apply mastery-aware selection at the requested exposure and difficulty. (6) Add optional glosses or pronunciation cues. (7) Check semantic fidelity, protected content, and observed exposure before display. (8) Store only authorized learning signals. In the simplest implementation, steps may be performed by an LLM under structured instructions; more robust deployments can split selection and validation into separate software components.

12. Minimal Configuration Schema

Fields: target_language (BCP-47), primary_language (BCP-47), exposure_percent (0–100), difficulty (1–5), gloss_policy (always / new-only / on-request / never), correction_mode (off / gentle / active), pronunciation_enabled (boolean), protected_topics (array), local_mastery_enabled (boolean), and privacy_mode (local-only / opt-in sync). Every setting must be editable without restarting a conversation.

13. Algorithmic Sketch

Let E be the eligible word count over a rolling response window, and T the count of target-language words within those spans. The observed immersion ratio is R = 100 × T / E, for E > 0. The engine minimizes |R − P|, where P is the user-selected target, subject to higher-priority constraints: fidelity, clarity, safety, fluency, and topic relevance. A constrained selector can score candidate phrases by contextual fit, learning value, comprehension estimate, and repetition interval. No language substitution should be accepted solely to satisfy the numerical target.

14. Open-Source LLM Implementation Deliverables

The initial repository should include: a public specification; README and contribution guide; proposed license (Apache-2.0 or MIT, subject to owner approval); JSON settings schema; provider-neutral LLM prompt adapter; API/middleware reference integration; language-span selector; gloss renderer; local mastery store; semantic-fidelity and exposure tests; sample chats; and an optional UI slider. Secondary integration examples should demonstrate a messaging client, browser extension, voice assistant, or educational application. Clearly document which features require an LLM and which can run without one.

15. Evaluation and Falsifiability

Compare optional immersion with ordinary conversation on: user comprehension, retention of introduced phrases after 1 and 7 days, interruption burden, semantic fidelity, perceived naturalness, and opt-out rate. Test 3%, 10%, 25%, and 50% settings across multiple languages and proficiency levels. Report actual observed ratios alongside target ratios. Predefine failure thresholds for mistranslations, protected-content leakage into the target language, and user confusion. A controlled pilot can test whether gradual immersion improves retention without degrading the original task.

16. Implementation Roadmap

Phase A (LLM proof of concept): model-independent prompt adapter, adjustable exposure, and inline glosses in a chat interface. Phase B (LLM application integration): local mastery, separate difficulty control, fidelity checks, and API middleware. Phase C (evaluation and Secretary Suite / COSMOSIS integration): accessible controls, multilingual testing, privacy review, and Bubble adapter. Phase D (secondary platforms): optional messaging, browser, voice, educational, and OS integrations. Phase E: publish the open-source reference implementation, tests, and contribution documentation. Each phase must remain independently usable.

17. Governance and Attribution

Project: Secretary Suite / COSMOSIS. Concept: John Swygert. Proposed open-source contribution model: public issue tracking, documented changes, community translation review, and reproducible tests. The name, branding, code license, and contributor agreements require explicit project-owner approval before release. This paper is a proposal and specification, not a claim that a finished product exists.

Primary: conversational LLM products (hosted or local), LLM APIs, and AI assistants with open-ended dialogue. Secondary: messaging platforms, browser extensions, voice interfaces, educational applications, and operating systems. LLM-enabled versions should offer contextual phrase generation and adaptive follow-up; non-LLM versions may provide constrained substitution and review. The open-source module must expose stable interfaces so developers can integrate it without adopting Secretary Suite / COSMOSIS or any single LLM provider.

18. Platform Scope and Priority

19. Conclusion

Progressive Conversational Foreign Language Learning is first and foremost a feature for LLM-based conversations: a user-controlled, context-aware way to learn another language while continuing to use an assistant for ordinary purposes. Its open-source, model-independent architecture allows other applications to adopt the approach where practical, but those secondary uses do not displace the primary LLM focus. It can begin with “Ja, natürlich” and progress at the learner’s pace toward full immersion.

Illustrative Acceptance Criteria

  • At 3% immersion, the assistant adds only light, relevant target-language material while completing the primary task.

  • A user can change exposure from 3% to 25% without resetting proficiency or phrase history.

  • Previously mastered expressions can appear without glosses; any expression can be translated on request.

  • Technical instructions, exact quotations, and high-stakes guidance remain faithful and intelligible.

  • The user can export or erase the learning record and operate without linked external accounts.

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