سنجاق‌شده‌ها

تیم لرالینک

LeraLink is a multilingual knowledge and learning platform built around small, meaningful units of knowledge. Instead of treating knowledge as fixed inside books, courses, articles, or videos, LeraLink breaks it into smaller pieces that can be connected, translated, reviewed, reused, and combined in different ways (For instance: This very answer is a Block of knowledge).  These pieces can include ideas, explanations, arguments, examples, quotations, images, audio, video, and other forms of knowledge. LeraLink makes it possible to organize these pieces into larger structures such as articles, learning paths, guides, lessons, and courses. The same knowledge can also appear in different languages, formats, and contexts without being recreated from scratch. The goal is to make high-quality knowledge easier to access, understand, connect, contribute to, and share.

تیم لرالینک

تیم لرالینک

A Content Management System (CMS) is mainly designed to create, organize, and publish content such as web pages, articles, posts, or media. LeraLink is designed around knowledge rather than pages. In a typical CMS, content is usually created for a specific page or publication. In LeraLink, knowledge is stored as smaller, independent pieces that can be connected, translated, reviewed, reused, and placed in different structures without being copied. A CMS helps you manage content. LeraLink helps you manage the relationships, meaning, reuse, and development of knowledge. This makes it possible to use the same knowledge in different articles, guides, courses, collections, or learning experiences while keeping its identity and history intact.

LeraLink Team

From Raw Data to a Block of Knowledge: An Example in LeraLink

This Block is an example of how structured data is published in LeraLink. A dataset can be published along with a description, methodological context, source, and calculated variables, and the same data can then be used for a table, chart, or other forms of display. The example below is drawn from a Pew Research Center national survey on Americans’ attitudes toward the country’s two major parties. Respondents were asked whether the Republican Party and the Democratic Party evoke feelings such as frustration, anger, hope, or pride in them.

FeelingRepublican PartyDemocratic PartyDifference in percentage pointsYearPopulationSource
Frustration6475112025U.S. adultsPew Research Center
Anger495012025U.S. adultsPew Research Center
Hope362882025U.S. adultsPew Research Center
Pride2716112025U.S. adultsPew Research Center
The American Trends Panel survey, Wave 180, was conducted from September 22 to 28, 2025. The full survey included 3,445 respondents, and questions concerning these four feelings were asked of a random half-sample of 1,727 people. — Pew Research Center

LeraLink Team

First, the main text of each independent Piece or Block is analyzed. Artificial intelligence extracts up to 20 important concepts or entities that are genuinely present in the text and have an importance score of at least 0.5. Recognized names are identified through exact vocabulary matching. New names are first searched in the global LeraLink knowledge registry and then, if necessary, in Wikidata. Using the context of the text, the model determines the correct identity among the retrieved options; cases with confidence of at least 0.85 are automatically confirmed, while ambiguous cases remain for human review. In the next stage, the system measures the presence and importance of each concept across the entire knowledge corpus of the same institution: mention frequency relative to text length, presence density, placement in the title, summary, and headings, key definitions, and semantic proximity. The multilingual bge-m3 model, with 1024-dimensional vectors and cosine similarity, is used to measure semantic neighborhood. Connections between Pieces are also derived from shared concepts and entities, vector proximity, references, authorship relationships, and publication structure, and are stored with weights and evidence. Finally, LeraLink produces three levels of knowledge for each concept: a valid general definition, a contextual description based on evidence from the institution's knowledge corpus, and an integrated account. As the knowledge corpus expands significantly, these descriptions are reconstructed while human revisions are preserved. The result is a dynamic multilingual knowledge graph whose semantic connections are traceable and reviewable.

تیم لرالینک

Instructional Boxes, Editorial Apparatus, and Self-Assessment in LeraLink

Which statement most accurately explains the architecture of inline boxes in LeraLink?

  • A box is a separate type of content, and the Blocks within it are removed from the normal Block mechanisms.
  • A box is a structural layer that assigns a role to ordinary Blocks, and that same role can be preserved in reading, translation, and print.
  • The role of a box is indicated solely by color and disappears in monochrome print.
  • Each platform owner can freely define new roles for boxes.

What Can’t You Do with Leralink?!

For example, a researcher can create a project on the history of Iranian political thought and develop each concept, thinker, source, event, or argument as separate pieces. These pieces can first be published as a collection of research notes, then brought together in a long-form article (what I call composition, inspired by music), and later become the basis for an educational course or a book. In more developed versions, the same pieces can be offered in different languages and through different media[1], connected to academic sources, placed in different learning pathways, and recombined[2] according to audience needs.

I will explain the matter of media elsewhere. For now, it is enough to say that every piece of knowledge may have a medium that plays a fundamental role in transmitting it. For example, suppose we turn a poem into an image. The reality is that the primary medium of poetry is “text,” but this does not mean that the poem cannot be presented elsewhere through another medium, such as “image,” “video,” or “audio.” Thus, every independent and coherent unit of knowledge can have one primary medium and several other media.

The recombination of pieces is one of LeraLink’s most significant innovations. Recombination can be one of the most important applications of agentic intelligence, or an agent, in the system. Artificial intelligence, or hooshvareh (هوشواره), can propose new molecules from a collection of pieces, just as in other fields AI has designed, discovered, and can propose entirely new molecules and pharmaceutical compounds. Suppose you have a set of separate, coherent pieces. AI sees shared themes, patterns, and concepts in them and proposes a composition that functions like pharmaceutical formulations, from which wholly different forms of effectiveness can be extracted.

What Can’t You Do with Leralink?!

The practical meaning of this approach is that LeraLink can begin with small projects: a ten-piece collection, a booklet, a short course, or a thematic archive. Over time, the same project can become a library, an educational network, a publishing program, or a complete knowledge ecosystem. Product development and project growth are not two separate paths here; every real project helps test the architecture, and each new development increases the capacity of earlier projects.

سجاد سپهری

از سند مشترک تا فضای مشترکِ دانش

امکان همکاری چند نفر بر روی یک فایل، تا اندازه‌ای مشکل همکاری را حل کرد. وُرد اکنون هم‌نویسندگی بلادرنگ دارد و حضور و تغییرات دیگر نویسندگان را به خوبی نشان می‌دهد. نسخه آنلاین هم دارد. مایکروسافت وُرد حتی محل تقریبی کار همکار دیگر را در متن نمایش می‌دهد. گوگل‌داکس مسألهٔ را یک گام جدی‌تر حل کرد: یک سند مشترک، نقش‌های ویرایشگر، پیش‌نهاد، نظرنویس و مشاهده‌گر، و ثبت و نام‌گذاری تاریخچهٔ نسخه‌ها. تا صد نفر هم می‌توانند تحت شرایط معمول، به‌طور هم‌زمان، روی یک فایل «کار» کنند. اما آیا به معنای واقعی به مسأله «همکاری» پاسخ داده‌شده است؟ این دستاوردها واقعی‌اند. با این همه، شیء اصلی همچنان همان سند واحد است.

سجاد سپهری

از سند مشترک تا فضای مشترکِ دانش

پاسخ ما یک معماری میان-گستر[1] است. واحد هماهنگی نه کل سند است و نه حرف و کلمه و موقعیت نشانگر. واحد هماهنگی، «پاره» است: کوچک‌ترین واحد ساختاری معنادار و قابل ردیابی و بازجای‌گذاری در بدنهٔ یک قطعه. چند نفر می‌توانند در یک قطعه به طور هم‌زمان کار کنند، اما هر پاره در هر بازنمایی زبانی در هر لحظه فقط یک عاملِ تغییردهنده دارد. دیگران، آن پاره و دگرگونی آن را مشاهده می‌کنند، می‌دانند چه کسی دارد روی آن کار می‌کند، اما تا پایان آن مداخله، نمی‌توانند وارد فضای عمل او شوند. پس از خروج عامل تغییر دهنده، پاره بلافاصله پایدار می‌شود و برای نویسندهٔ دیگر، ویراستار، مترجم، متخصص داده، طراح یا نظر‌دهنده و ناظر در دسترس قرار می‌گیرد. این مقاله منطق فنی و معرفت‌شناختی این معماری، نسبت آن با پژوهش‌های جمعی دیجیتال، تفاوت آن با ابزارهای رایج و پیامدهایش برای پژوهش، نویسندگی، نشر و مدیریت دانش را توضیح می‌دهد.

middle-out approach

سجاد سپهری

از سند مشترک تا فضای مشترکِ دانش

بخش بزرگی از نرم‌افزارهای هم‌نویسندگی دیجیتال یک فرض را تقریباً بدیهی گرفته‌اند: هرچه افراد بیشتری بتوانند هم‌زمان و با اصطکاک کمتری در یک سند بنویسند، همکاری بهتر و بهره‌وری بیشتر خواهد شد. وُرد، گوگل‌داکس، نوشن، اُوِرلیف و نسل بزرگی از ویرایشگرهای مشترک، با تفاوت‌های فنی و حرفه‌ای مهم، حول همین مسئله شکل گرفته‌اند: چگونه چند نفر را در یک سند حاضر کنیم، تغییراتشان را با هم آشتی دهیم، حضورشان را نشان دهیم و تعارض‌های فنی را تا حد ممکن پنهان کنیم. معماری هم‌نویسندگی لرالینک از پرسش دیگری آغاز می‌شود: چگونه چند انسان بتوانند واقعاً یک اثر مشترک بسازند، بی‌آنکه استقلال شناختی، تمرکز، مسئولیت و فضای عمل یکدیگر را از بین ببرند؟ 

LeraLink Team

From Raw Data to a Block of Knowledge: An Example in LeraLink

This Block is an example of how structured data is published in LeraLink. A dataset can be published along with a description, methodological context, source, and calculated variables, and the same data can then be used for a table, chart, or other forms of display. The example below is drawn from a Pew Research Center national survey on Americans’ attitudes toward the country’s two major parties. Respondents were asked whether the Republican Party and the Democratic Party evoke feelings such as frustration, anger, hope, or pride in them.

FeelingRepublican PartyDemocratic PartyDifference in percentage pointsYearPopulationSource
Frustration6475112025U.S. adultsPew Research Center
Anger495012025U.S. adultsPew Research Center
Hope362882025U.S. adultsPew Research Center
Pride2716112025U.S. adultsPew Research Center
The American Trends Panel survey, Wave 180, was conducted from September 22 to 28, 2025. The full survey included 3,445 respondents, and questions concerning these four feelings were asked of a random half-sample of 1,727 people. — Pew Research Center

LeraLink Team

Trust in Scientists across Selected Countries (An example of LeraLink Data Visualization)

Survey responses are often easier to interpret as proportions rather than isolated numbers. The example below compares the distribution of trust in scientists across four countries using data from the Wellcome Global Monitor.

CountryHigh trustMedium trustLow trustNo opinion
Belgium424882
Spain395461
Australia335881
New Zealand326152
Distribution of the Wellcome Global Monitor Trust in Scientists Index in four selected countries. Percentages represent respondents classified as having high, medium or low trust, or no opinion. — Wellcome Global Monitor 2018, Table 3.2, Trust in Scientists Index.