{"id":52,"date":"2026-11-02T20:50:24","date_gmt":"2026-11-02T20:50:24","guid":{"rendered":"https:\/\/totonacapan.com\/wordpress\/?page_id=52"},"modified":"2026-11-06T11:04:20","modified_gmt":"2026-11-06T11:04:20","slug":"en-saberes-ancestrales","status":"publish","type":"page","link":"https:\/\/totonacapan.com\/en-saberes-ancestrales\/","title":{"rendered":"AI &#038; Ancestral Wisdom"},"content":{"rendered":"<style id=\"iict-fi3-integracion\">\n#main .fi3-unified-note{box-sizing:border-box;margin:1.5rem 0;padding:1.1rem 1.4rem;background:rgba(224,234,238,.45);border-inline-start:3px solid #b19a60;color:#254761;}#main .fi3-unified-note p{margin:0 0 .65rem;line-height:1.65;text-align:justify;hyphens:auto;}#main .fi3-unified-note a{display:inline-block;font-weight:600;line-height:1.5;}#main .fi3-ancestral{min-width:0;scroll-margin-top:2rem;}#main .fi3-ancestral .editorial-dossier{width:100%;max-width:none;margin:0;padding:0;background:transparent;border:0;box-shadow:none;}#main .fi3-ancestral .editorial-dossier > header{display:block!important;max-width:none;margin:1.5rem 0;padding:0;border:0;}#main .fi3-ancestral .editorial-dossier > header>.eyebrow{margin:0 0 .7rem;}#main .fi3-ancestral .editorial-dossier > header>h2{width:100%;max-width:none;margin:0;text-align:start;}#main .fi3-ancestral .editorial-reading,#main .fi3-ancestral .prose{width:100%;max-width:none;margin:0;padding:0;}#main .fi3-ancestral details.editorial-model{margin:2rem 0;padding:0 0 1.3rem;border-block:1px solid #baa475;}#main .fi3-ancestral details.editorial-model>summary{cursor:pointer;padding:1.1rem .2rem;font-family:var(--serif,Georgia,serif);font-size:clamp(1.12rem,1.9vw,1.5rem);line-height:1.4;color:#254761;}#main .fi3-ancestral details.editorial-model p{font-family:var(--serif,Georgia,serif);font-size:clamp(1.05rem,1.65vw,1.3rem);line-height:1.65;text-align:justify;hyphens:auto;}#main .fi3-ancestral details.editorial-model h4{font-family:var(--serif,Georgia,serif);font-size:clamp(1.18rem,2vw,1.55rem);line-height:1.35;font-weight:400;color:#315749;margin:1.7rem 0 .7rem;}#main .fi3-ancestral details.editorial-model>summary:focus-visible{outline:2px solid #254761;outline-offset:5px;}@media(max-width:720px){#main .fi3-unified-note{padding:1rem;}#main .fi3-ancestral .editorial-dossier > header>h2{font-size:clamp(1.65rem,7.5vw,2.3rem);line-height:1.18;}}\n<\/style><aside class=\"fi3-unified-note\" id=\"programa-integrado\"><p>AI &amp; Ancestral Wisdom is integrated into <strong>Formaci\u00f3n Integral 3.0<\/strong>. The programme\u2019s presentation, history and this methodological development are brought together on one page.<\/p><a href=\"https:\/\/totonacapan.com\/?page_id=301#ai-ancestral-wisdom\">Read the integrated programme \u2192<\/a><\/aside>\n\n<style id=\"iict-alineacion-cuadernos-global\">body.wp-theme-totonacapan-instituto:has(#primary-nav) #main .editorial-dossier > header{display:grid;grid-template-columns:minmax(0,1fr) minmax(0,46rem) minmax(0,1fr);column-gap:0;align-items:start;}\nbody.wp-theme-totonacapan-instituto:has(#primary-nav) #main .editorial-dossier > header>.eyebrow{grid-area:1\/1;margin:0 1.5rem 0 0;}\nbody.wp-theme-totonacapan-instituto:has(#primary-nav) #main .editorial-dossier > header>h2{grid-area:1\/2;width:auto;max-width:none;margin:0;text-align:start;hyphens:manual;}\n@media(max-width:1199px){body.wp-theme-totonacapan-instituto:has(#primary-nav) #main .editorial-dossier > header{display:block;box-sizing:border-box;width:100%;max-width:46rem;margin-inline:auto;}body.wp-theme-totonacapan-instituto:has(#primary-nav) #main .editorial-dossier > header>.eyebrow{margin:0 0 1rem;}}<\/style>\n\n<section class=\"editorial-dossier\" id=\"cuaderno-saberes-ancestrales\"><header><p class=\"eyebrow\">Practice notebooks \u00b7 Totonacapan Institute<\/p><h2>AI &amp; Ancestral Wisdom: from human apprentice to artificial apprentice<\/h2><\/header><div class=\"editorial-reading\"><p>The original approach begins with human learning. A community identifies a practice it wishes to transmit, agrees what may be observed and documented, and recognises the people able to evaluate learning. Artificial intelligence enters afterwards, as a subject of research within that knowledge environment.<\/p><h3>Why Totonacapan is part of the research<\/h3><p>Totonacapan offers an especially rich context for this question. Its crafts, languages, ecological practices, artisanal techniques, oral memory and transmission between masters and apprentices contain knowledge that is not fully represented in manuals or large digital corpora. The research interest is not to turn the territory into a source of data, but to understand how a judgement that depends on people, materials, relationships and specific circumstances is recognised, exercised and transmitted.<\/p><p>The project does not seek to replace masters or bearers of knowledge, nor to extract knowledge without its custodians. Documentation and any AI trial must begin with consent, attribution, cultural context, benefit to participants and the possibility of restricting or withdrawing particular uses. Technology is an instrument subject to evaluation; this does not give it authority to define or represent the practice.<\/p><h3>Two kinds of ambiguity to distinguish<\/h3><ul><li><strong>Responsibility and authority:<\/strong> who contributes a record, who may correct it and who has legitimacy to decide about a practice or a use.<\/li><li><strong>Version:<\/strong> which document is current, what changed and whether two descriptions contradict each other or express legitimate variants.<\/li><\/ul><p>Organising these aspects makes a more demanding question possible: when documentation is clear, what still cannot be transmitted through a record? The project distinguishes loss caused by documentary disorder from difficulty associated with gesture, context, material perception or expert judgement. Tacit knowledge does not automatically become a documentation error.<\/p><h3>AI as a parallel apprentice<\/h3><p>The design proposes observing what a system reconstructs from authorised procedures and comparing it with a person\u2019s learning. The evaluation should identify omitted steps, sequence errors, ignored conditions and apparently coherent responses that alter the practice. Those who can recognise such differences in the craft are responsible for assessing them.<\/p><h3>AI supporting a new learner<\/h3><p>A second stage asks whether the representation learned by the system can help teach. The criterion would be what another person can understand and perform with its support, under human review. Repeating a persuasive explanation and exercising a capability are different outcomes that must be measured separately.<\/p><h3>Comparing without erasing context<\/h3><p>The research proposes comparing AI architectures under controlled documentary conditions and agreed tasks. Variables include record quality, clarity of responsibilities, version control and the amount of context required. Each practice\u2019s limits should shape the experiment.<\/p><p>These are hypotheses and a proposed method, rather than findings already obtained. Any trial involving a community\u2019s materials requires prior agreement on participation, access, review, AI uses and the return of findings. The purpose is to learn what supports transmission and to recognise where human relationships remain indispensable.<\/p><style id=\"iict-modelo-propuesto-lectura\">#main details.editorial-model{margin:2rem 0;border-block:1px solid #baa475;padding:0 0 1.5rem}#main details.editorial-model>summary{cursor:pointer;padding:1.1rem .2rem;font-family:var(--serif,Georgia,serif);font-size:clamp(1.15rem,1.9vw,1.55rem);line-height:1.35;color:#254761}#main details.editorial-model p{font-family:var(--serif,Georgia,serif);font-size:clamp(1.05rem,1.65vw,1.3rem);line-height:1.62;text-align:justify}#main details.editorial-model h4{font-family:var(--serif,Georgia,serif);font-size:clamp(1.2rem,2vw,1.6rem);line-height:1.3;font-weight:400;color:#315749;margin:2rem 0 .8rem}#main details.editorial-model>h3{margin-top:1.2rem}#main details.editorial-model>summary:focus-visible{outline:2px solid #957341;outline-offset:3px}@media print{#main details.editorial-model>*{display:block!important}}<\/style><details class=\"wp-block-details editorial-model\" id=\"modelo-propuesto\"><summary>Complete proposed model \u00b7 21 sections<\/summary><h3>Proposed model: from living knowledge to artificial intelligence evaluation<\/h3><p>AI &amp; Ancestral Wisdom proposes a progressive, auditable method for studying how human knowledge is preserved, formalised, transmitted and tested against contemporary artificial intelligence systems. It does not begin with AI. It begins with living communities of practice, master\u2013apprentice learning, documented procedures, traceability and research into the ambiguities that arise when we try to represent a practice through information.<\/p><h4>1. The research premise<\/h4><p>A significant part of human knowledge is not fully expressed in rules, documents or datasets. It lives in gesture, timing, relationships with materials, territory, shared memory, ritual and judgement acquired through experience. Michael Polanyi drew attention to this dimension of tacit knowledge: we can often do more than we can explicitly explain. The research asks which parts of a capability can be described, taught or modelled, and which continue to depend on practice and living human communities.<\/p><h4>2. Why this is more than cultural digitisation<\/h4><p>Digitising documents, recording languages, describing crafts, organising expert knowledge and establishing ethical rules for data are valuable but distinct tasks. This proposal connects them through one question: what happens to a capability when it moves from a living practice to a formal representation? The purpose is not to turn a culture into digital content. It is to study what is retained, transformed and lost in that transition, and how to recognise those differences with the people who know the practice.<\/p><h4>3. The methodological principle<\/h4><p>Before introducing AI systems, the work identifies practices, activities, procedures, data, transformations, responsibilities, versions, evidence, decision rules, exceptions and validation criteria. This foundation makes it possible to distinguish a written instruction from the operation actually performed and, in turn, from what an expert resolves through experience without being able to reduce it entirely to a rule. Documentation is not assumed to be a perfect reflection of practice: that difference is also observed.<\/p><h4>4. First phase: census and priorities<\/h4><p>The first step is to identify capabilities, techniques, crafts and people who wish to participate in their documentation. The census seeks to recognise which practices exist, which depend on very few masters, where generational succession is weakening, which materials or spaces are indispensable and which forms of transmission need support. Its usefulness goes beyond a list of names: it provides an initial map of living capabilities and the conditions for their continuity. Priorities must be agreed with the people who practise that knowledge.<\/p><h4>5. Second phase: communities of practice<\/h4><p>Knowledge does not reside exclusively in individuals. It is also sustained in workshops, shared routines, tools, materials, learning, corrections, relationships of trust and collective quality criteria. The research identifies existing communities or the conditions in which they could be strengthened, without imposing an external structure on them. Comparing variants and sharing criteria can reduce dependence on a single master, provided each practice\u2019s legitimate diversity is respected and its bearers retain their authority.<\/p><h4>6. Third phase: observing master\u2013apprentice transmission<\/h4><p>The master\u2013apprentice relationship is the principal field of observation. Attention is paid to what is explained verbally, what is shown with the hands, what is learned through successive corrections, what depends on timing or material and what requires expert judgement. Acceptable changes are distinguished from errors, as are the conditions in which two masters may act differently without either being wrong. The aim is to make learning observable without prematurely reducing its complexity to data.<\/p><h4>7. Fourth phase: procedures and formalisation<\/h4><p>Once the real practice has been understood, procedures can be developed to describe activity sequences, tools, materials, functions, responsible people, versions, transformations, quality criteria, exceptions, tests and relationships between documents. Photographs and other records are included only under the authorised conditions. Formalising does not mean imposing a single version: it means distinguishing what can be standardised, which variants must be retained and which aspects continue to depend on circumstances and human judgement.<\/p><h4>8. Fifth phase: traceability and auditability<\/h4><p>The next step is to establish whether a description can be traced to its sources and whether a competent person can review how it was obtained. Document control, version management, quality criteria and process auditing make it possible to move from an observation to a verifiable and partially transmissible record. The question is not whether everything can become a procedure, but which parts are consistent, which variations are legitimate and which aspects would lose their meaning if artificially stabilised.<\/p><h4>9. Sixth phase: a hypothesis about informational entropy<\/h4><p>The model proposes, as a hypothesis to be tested, that certain informational ambiguities could help identify areas where unformalised knowledge persists. It pays particular attention to ambiguity of responsibility or authority\u2014who contributes, safeguards, validates or can decide about a practice\u2014and ambiguity of version\u2014which description is used, how it changed and why different variants coexist. These ambiguities do not in themselves demonstrate tacit knowledge: they can indicate either a documentary defect or real contextual complexity. Making that distinction is itself part of the research.<\/p><h4>10. Correctable disorder and structural tacit knowledge<\/h4><p>When ambiguity comes from incomplete documents, unclear responsibilities, accidental duplication or lack of version control, the response can be documentary and organisational: clarify authorities, consolidate records, establish provenance and verify compliance with agreements. By contrast, when two variants legitimately respond to different materials, seasons, territories, masters, rituals or circumstances, eliminating the difference can destroy knowledge. The task is to understand it, document its conditions and validate it without forcing a simplification.<\/p><h4>11. Four kinds of ambiguity to distinguish<\/h4><p>The working model separates four situations. Documentary disorder arises when authorship, version or control is missing and can be corrected through better records. Operational duplication occurs when two areas maintain information without coordination and requires clear responsibilities and master sources. Contextual ambiguity occurs when several versions are valid under different conditions and requires those conditions to be described. Finally, structural tacit knowledge arises when a practice depends on expert judgement that has not yet been formalised; it requires observation, comparison and preservation of context. Classifying these cases avoids treating every difference as an error or, conversely, invoking mystery to justify a correctable defect.<\/p><h4>12. Seventh phase: AI as apprentice<\/h4><p>AI enters only after a sufficient human and documentary foundation has been built. Its first role is as an apprentice alongside the human learner: it receives procedures, observations, authorised images, variants, rules, exceptions, contextual language and corrections recorded during transmission. What a human apprentice understands and performs is compared with what the system appears to understand from those materials. The coherence of a textual response is not treated as evidence of understanding the practice.<\/p><h4>13. Eighth phase: AI supporting teaching<\/h4><p>The second role is more demanding. The study examines whether the system can explain a practice in a way that helps another person learn it, such as a new apprentice, an artisan from another context or a technical observer. Remembering information is no longer sufficient: sequences, conditions, variants and limits must be retained. Evaluation distinguishes between producing a persuasive explanation and actually contributing to a capability that another person can exercise under appropriate supervision.<\/p><h4>14. Human review of AI-assisted transmission<\/h4><p>Explanations generated by AI systems must be reviewed by people who are competent and have the legitimacy to assess the practice. Review examines whether the working sequence is retained, context respected, legitimate variants recognised, exceptions identified and provenance and limits of use maintained. It also looks for omissions, invented assertions and a false sense of coherence. A system can write fluently while altering the technical, cultural or symbolic meaning of what it purports to teach.<\/p><h4>15. Experimental hypothesis<\/h4><p>The central hypothesis proposes that the greater a practice\u2019s contextual complexity and density of legitimate ambiguities, the harder it may be for an AI system to represent, stabilise and transmit it faithfully. This relationship still needs to be defined and tested empirically; it is not a demonstrated result. To study it, profiles can describe the number of variants, authority over records, version control, dependence on context, expert judgement, implicit transmission, territorial differences, ritual or symbolic significance and resistance to uniform formalisation.<\/p><h4>16. Comparative research<\/h4><p>The method is designed to compare different practices without imposing a single cultural measure. Possible cases include embroidery from Totonacapan and other regions, jewellery, vanilla cultivation and curing, weaving, watchmaking, restoration, ecological knowledge and complex workshop techniques. Each case requires its own agreement with the bearers of the knowledge and its own assessment criteria. Comparison seeks to understand which aspects can be made explicit and which require situated experience, rather than rank cultures by how easily they can be digitised.<\/p><h4>17. What the model could reveal<\/h4><p>A predominantly procedural practice may be easier to record, review and transmit. Another practice, highly dependent on changing circumstances, will need contextual rules and human validation. Other capabilities depend on material perception, gesture, timing, community authority or ritual knowledge and may resist deeper formalisation. These are working profiles, not a definitive classification of people or cultures. One of the most valuable outcomes could be to identify precisely where AI systems fail and which human conditions cannot be replaced.<\/p><h4>18. Proposed contribution to AI evaluation<\/h4><p>Much current AI evaluation focuses on language, reasoning, search, mathematics, programming or task execution. This proposal offers a complementary kind of trial: testing models against situated tacit knowledge, master\u2013apprentice transmission, legitimate variations and situations in which documentation does not exhaust the context. The aim is to observe which architectures or strategies can recognise their limits and work with ambiguity without producing falsely confident explanations or reducing cultural differences to an undifferentiated average.<\/p><h4>19. Proposed contribution to preservation<\/h4><p>The same method can help identify capabilities at risk, strengthen communities of practice, document variants, support teaching across generations, organise traceable archives and improve cultural attribution. It can also prepare useful information for studying formal protection mechanisms where appropriate and with consent. Preservation does not simply mean storing a recording or a description: it means maintaining the conditions that allow a capability to continue being practised, corrected and transmitted.<\/p><h4>20. Institutional status and implementation conditions<\/h4><p>This text describes a proposed research methodology, not a completed experimental programme or recognition granted by an external institution. Its possible implementation requires voluntary communities, clear agreements on rights and access, competent researchers and evaluators, field resources, documentation and review criteria. The Integral Management Society and the Institute bring experience of working with communities of practice, documentation, process management and complex systems, but each case must specify its own participants, scope, permissions and evidence before being presented as an activity carried out.<\/p><h4>21. The question that remains open<\/h4><p>AI &amp; Ancestral Wisdom does not seek to demonstrate that artificial intelligence can replace human wisdom. It seeks to establish, practically and verifiably, which knowledge can be documented, which can be partially taught, which can be modelled and which must remain connected to people, communities, materials and territories. The central question is not whether AI can store ancestral knowledge. It is whether it can learn, explain and support its transmission without losing the coherence of the context or erasing what makes a practice legitimate and alive.<\/p><\/details><div class=\"editorial-links\"><a href=\"https:\/\/totonacapan.com\/?page_id=48\">See the programme sequence \u2192<\/a><a href=\"https:\/\/tegrity.ai\/\">Research at Tegrity.AI \u2197<\/a><\/div><\/div><\/section><div class=\"prose\"><p class=\"eyebrow\">Conceptual area of work<\/p><p class=\"lead\">Technology can help organise an archive. Understanding knowledge also requires the people who live it.<\/p><p>AI &#038; Ancestral Wisdom explores documenting living knowledge and preparing records for educational and research uses while retaining context and provenance. It is an area of work within the Institute; this page does not present a deployed system or pilot findings.<\/p><h2>Before digitisation<\/h2><p>The first step is to agree what should be preserved and why. Records must respect language, their bearers\u2019 interpretation and access conditions. Some knowledge can be described without disclosing its content; some requires restricted access.<\/p><h2>Four questions for each record<\/h2><ul><li><strong>Provenance:<\/strong> where does it come from, and who contributes the information?<\/li><li><strong>Context:<\/strong> which language, practice and situation give it meaning?<\/li><li><strong>Permission:<\/strong> which uses have been agreed, and within what limits?<\/li><li><strong>Review:<\/strong> who can correct a translation or interpretation?<\/li><\/ul><h2>The role of artificial intelligence<\/h2><p>Digital tools may support classification, search and draft transcription. Such assistance requires human review and traceability. An automatic translation does not become a community\u2019s authentic voice, and an inference is not testimony.<\/p><h2>Access and reuse<\/h2><p>Publishing a description does not grant rights to train models or reproduce all related material. Each use needs suitable permission, attribution and responsibility. The purpose is to strengthen memory and transmission while preserving participants\u2019 ability to decide.<\/p><div class=\"note\"><h3>A connection with IMSV\u2019s research<\/h3><p>Dialogue with IMSV\u2019s technology work helps frame questions about documentation and integrity. It does not imply validation of a tool or an agreement with third parties.<\/p><a href=\"https:\/\/tegrity.ai\/\">Discover Tegrity.AI \u2192<\/a><\/div><a class=\"button\" href=\"https:\/\/totonacapan.com\/?page_id=60\">Propose a conversation \u2192<\/a><\/div>","protected":false},"excerpt":{"rendered":"<p>Technology serving context and attribution<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-52","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/totonacapan.com\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/52","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/totonacapan.com\/wordpress\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/totonacapan.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/totonacapan.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/totonacapan.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=52"}],"version-history":[{"count":6,"href":"https:\/\/totonacapan.com\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/52\/revisions"}],"predecessor-version":[{"id":1686,"href":"https:\/\/totonacapan.com\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/52\/revisions\/1686"}],"wp:attachment":[{"href":"https:\/\/totonacapan.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=52"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}