Socioformation: a pedagogical model for territorial transformation and ethical governance in the era of artificial intelligence

 

Sergio Tobon, Ph.D.
Universidad Autónoma de Chihuahua, Mexico
Correspondence: stobon@uach.mx

 

APA 7 citation

Tobón, S. (2026). Socioformation: A pedagogical model for territorial transformation and ethical governance in the era of artificial intelligence. In S. de J. Tobón Tobón & J. E. Martínez-Iñiguez (Eds.), Socioformation, society and sustainable development (pp. 19–100). Autonomous University of Baja California. https://cife.edu.mx/recursos/chapter-socioformation-a-pedagogical-model-for-territorial-transformation-and-ethical-governance-in-the-era-of-artificial-intelligence/

 

Sergio de Jesús Tobón Tobón’s chapter, “Socioformation: A Pedagogical Model for Territorial Transformation and Ethical Governance in the Era of Artificial Intelligence,” is a timely and ambitious theoretical contribution to one of the most urgent debates in contemporary education: what should universities form in human beings when artificial intelligence can increasingly automate the cognitive operations that educational systems have traditionally prized? The chapter’s central claim is direct and provocative: higher education cannot continue to define its mission around content acquisition, isolated competencies, or cognitive taxonomies designed for a pre-AI world. Instead, education must move toward the integral formation of human beings capable of ethical judgment, sociocritical consciousness, territorial transformation, and responsible governance of AI. The chapter frames this shift as a response to “educational obsolescence” and to the ethical crisis generated when curricula remain focused on skills that algorithms can replicate or surpass.

The chapter is worth reading because it does not treat artificial intelligence merely as a technological tool to be inserted into classrooms. It treats AI as a civilizational rupture that obliges education to redefine its purpose. The guiding problem is not whether students should use AI, but how education can ensure that human beings ethically direct AI toward the common good. This question is especially relevant for universities, ministries of education, curriculum designers, educational technology companies, and organizations seeking to prepare professionals for social complexity rather than only for labor-market adaptation.

The chapter seeks to answer several major questions: What remains distinctively human in professional formation when AI automates analysis, synthesis, strategy, and content generation? How can higher education avoid producing graduates whose competencies are rapidly displaced by algorithms? How can sustainability be reframed beyond environmental awareness or institutional rhetoric? How can the curriculum move from simulated cases to real territorial transformation? How should instructors, professionals, and students change their roles in AI-operated ecosystems? These questions give the chapter its intellectual force and practical urgency.

One of the most relevant innovations is the transition from Sustainable Development to Sustainable Social Development. In Table 1, the chapter contrasts traditional sustainability approaches—often reduced to environmental balance, economic viability, and the risk of greenwashing—with a socioformative approach centered on dignity, justice, inclusion, and measurable improvements in living conditions. This is a significant move because it prevents sustainability from becoming a depoliticized slogan. The chapter insists that there can be no meaningful ecological sustainability without social justice, and that education must be judged by its capacity to improve concrete territorial realities.

Another powerful contribution is the distinction between contextualization and territorialization. The chapter criticizes pedagogical approaches that use “context” merely as a case study or as a didactic resource for learning. In contrast, territorialization positions the territory as a living actor, a source of curriculum, and a field of co-created transformation. Table 3 makes this difference clear: contextualization studies problems; territorialization acts with communities to transform them. This is one of the chapter’s most important conceptual advances because it shifts education from knowing about the world to ethically transforming it.

The chapter’s proposal of metaskills or metacompetencies is also highly innovative. Rather than continuing to train students in competencies that may be automated, the chapter argues for forming capacities that require ethical judgment, character, sociocritical analysis, interdisciplinary direction, collaborative territorial action, and metacognitive governance of AI. This is not simply a new terminology; it is an attempt to reposition human formation around what machines cannot internalize: moral purpose, territorial empathy, responsibility, and commitment to justice.

The most compelling conceptual development is sociocritical thinking. The chapter argues that traditional critical thinking, although necessary, is insufficient because it may remain individualistic, analytical, and ethically neutral. Sociocritical thinking goes further: it asks whether a reality is just, what structural causes produce injustice, and how communities can collaboratively transform that reality. This reframes critical thinking as a transformative praxis rather than as an abstract intellectual exercise. In the age of AI-generated misinformation, algorithmic bias, and automated decision-making, this contribution is particularly relevant.

The chapter also advances a curricular innovation through the Socioformative Taxonomy, which seeks to surpass Bloom, Marzano, and SOLO by moving from individual cognitive verbs to levels of ethical performance and territorial transformation. The proposed levels—Receptive, Resolutive, Reflexive, Critical, and Cocreative—are designed to assess not only what students know, but how they act, deliberate, co-create, and transform real problems. This is especially important because the chapter argues that many traditional learning outcomes can now be completed or simulated by AI.

The chapter is particularly useful for curriculum reform. It proposes replacing rigid subject-centered curricula with interdisciplinary socioformative projects, micro-credentials, personalized trajectories, strategic partnerships, streamlined graduation through evidence portfolios, and longitudinal monitoring of metacompetencies. This gives the chapter practical value beyond theoretical critique. The proposal is not merely to “add AI” to existing programs, but to redesign the architecture of higher education around real problems, ethical action, and evidence of territorial impact.

Its relevance also extends to the future of work. The chapter argues that professional roles must shift from technical execution to strategic direction, ethical AI auditing, human-AI ecosystem articulation, sociocritical data literacy, and responsible innovation. This is a serious contribution because it challenges universities to stop preparing professionals for tasks that AI will increasingly perform and start preparing them to guide, evaluate, humanize, and govern technological systems.

A critical reading should also note a challenge: the chapter is strongest as a conceptual and programmatic framework, but its claims require robust empirical operationalization. For example, the proposal to measure territorial impact, assess metaskills, certify micro-credentials, and audit AI ethically would benefit from implementation protocols, indicators, longitudinal studies, and case-based validation across institutional contexts. This does not weaken the chapter’s importance; rather, it identifies the next stage of research. The chapter opens a necessary agenda, but the field will need evidence-based models to test how socioformation performs in different educational systems, disciplines, and communities.

In sum, this chapter should be read because it offers a bold answer to a question that many institutions still avoid: education cannot remain centered on learning outcomes that AI can reproduce; it must form human beings capable of ethical, sociocritical, and territorial transformation. Its value lies in connecting AI governance, sustainability, curriculum design, social justice, and human formation in a single pedagogical architecture.

Problems and Guiding Questions Addressed in the Chapter

The chapter addresses the problem of educational obsolescence in an era where AI can automate complex cognitive operations. It asks how higher education can remain relevant when traditional competencies, cognitive taxonomies, and content-based curricula are increasingly insufficient. It also addresses the problem of ethical pertinence: how can education form professionals who do not merely use technology efficiently, but direct it toward dignity, equity, and the common good?

The chapter also asks how sustainability can move beyond abstract discourse. Its answer is Sustainable Social Development, a framework that links ecological responsibility with justice, inclusion, and measurable improvements in living conditions.

A further problem is the passivity of traditional “contextualized” education. The chapter asks how the territory can stop being a case study and become the living source of curriculum. Its answer is territorialization: formative action with communities, not merely analysis about communities.

Finally, the chapter asks how curriculum, assessment, professional roles, and teaching should change in the AI era. It responds with socioformative projects, socioformative assessment, micro-credentials, personalized trajectories, ethical AI auditing, and the instructor as an architect of memorable transformative experiences.

Relevance and Innovation

The chapter is relevant because it confronts a structural issue: many educational systems still prepare students for a world in which information was scarce, disciplines were stable, and professional competence was tied to technical execution. The AI era has destabilized that model. The chapter’s innovation lies in shifting the educational center from learning to integral formation, from competencies to metaskills, from context to territory, from critical thinking to sociocritical thinking, and from AI use to AI governance.

Its most original contribution is not technological but pedagogical and ethical: it argues that AI should become an operational platform subordinated to human purposes, not the organizing principle of education. In that sense, the chapter offers a Latin American humanistic alternative to purely instrumental models of AI literacy and workforce reskilling.

Innovative Ideas / Highlights

1. Sustainable Social Development as an ethical compass.
The chapter reframes sustainability by placing dignity, inclusion, justice, and human well-being at the center. This prevents sustainability from being reduced to environmental rhetoric or institutional branding.

2. Territorialization instead of contextualization.
The chapter’s distinction between context and territory is one of its strongest innovations. Contextualization often uses reality as an example; territorialization turns reality into the curriculum’s source, method, and field of transformation.

3. Metaskills beyond competencies.
The chapter argues that competencies are increasingly vulnerable to automation. Metaskills, by contrast, involve ethical life projects, sociocritical thinking, interdisciplinary direction, collaboration in AI ecosystems, metacognitive AI, and deep innovation.

4. Sociocritical thinking as AI-era human intelligence.
The chapter does not reject critical thinking but extends it. Sociocritical thinking asks about justice, power, exclusion, territory, and transformation. It is presented as a human capacity for ethical action that AI cannot authentically replace.

5. Metacognitive artificial intelligence.
The chapter proposes that professionals should not merely prompt AI but govern it. This includes planning, monitoring, auditing, identifying bias, and ensuring that AI serves social justice and sustainable social development.

6. Socioformative taxonomy.
The chapter proposes a taxonomy centered on ethical performance and territorial impact. This is an important alternative to taxonomies focused mainly on individual cognition.

7. Socioformative projects as the backbone of curriculum.
The chapter proposes that real territorial problems should organize learning, assessment, research, and community engagement. This is a strong alternative to fragmented subject-based curricula.

8. The instructor as architect of memorable experiences.
The instructor is no longer primarily a transmitter of content. The instructor becomes a designer of transformative experiences, ethical accompaniment, and sociocritical engagement with AI and territory.

Spanish Summary / Resumen en español

El capítulo plantea que la educación superior enfrenta una crisis de pertinencia debido al avance de la inteligencia artificial, que ya puede automatizar muchas habilidades cognitivas tradicionalmente valoradas por la escuela y la universidad. Ante esto, la socioformación propone desplazar el centro de la educación desde el “aprendizaje” hacia la formación integral de personas con carácter ético, pensamiento sociocrítico, compromiso con el bien común y capacidad para transformar territorios.

La principal innovación del capítulo consiste en articular la socioformación con la era de la IA. El texto propone que la educación no debe limitarse a enseñar el uso instrumental de herramientas digitales, sino formar profesionales capaces de dirigir éticamente la inteligencia artificial, auditar sus sesgos, anticipar consecuencias sociales y emplearla como plataforma operativa para resolver problemas reales del territorio.

El capítulo también distingue entre desarrollo sostenible y desarrollo social sostenible, colocando la justicia, la dignidad humana, la inclusión y la mejora medible de las condiciones de vida como ejes de la formación. Asimismo, propone pasar de la contextualización a la territorialización: no se trata solo de estudiar problemas del entorno, sino de actuar con las comunidades para transformar sus causas estructurales.

En síntesis, el capítulo es relevante porque ofrece una arquitectura pedagógica para rediseñar currículo, evaluación, docencia y roles profesionales en la era de la IA. Su mayor aporte es afirmar que el valor humano no está en competir con las máquinas en procesamiento de información, sino en orientar la tecnología hacia la justicia social, el bien común y la transformación territorial.

Chinese Synthesis / 中文综合概述

本章提出，在人工智能迅速发展的时代，高等教育正面临深刻的相关性危机。传统教育长期重视知识传授、认知技能和职业能力，但这些能力越来越容易被人工智能自动化。因此，作者主张教育必须从“学习”转向“人的整体形成”，重点培养伦理判断、社会批判意识、共同利益意识以及改造现实问题的能力。

本章的核心创新在于提出“社会形成”作为一种面向人工智能时代的教育模式。人工智能不应成为教育的最终目的，而应成为由人类伦理意识所引导的操作平台。教育的任务不是简单地训练学生使用人工智能，而是培养他们能够审查算法偏见、判断社会后果、整合跨学科知识，并与社区共同解决真实的地方性问题。

本章还强调从“可持续发展”转向“可持续社会发展”，把人的尊严、社会正义、包容性和生活条件的可衡量改善作为教育的核心目标。它进一步提出从“情境化”走向“地域化”：教育不应只是把现实问题作为课堂案例，而应让学生、教师和社区共同参与现实问题的转化。

总体而言，本章的重要性在于，它为人工智能时代的课程、评价、教师角色和专业角色提供了新的教育框架。人的价值不在于与机器竞争信息处理能力，而在于以伦理、社会责任和批判意识引导技术服务于共同利益。

Synthesis:
The chapter’s core thesis is that education must stop preparing students to compete with AI in cognitive performance and start forming human beings who can ethically direct AI toward the transformation of real problems. Its strongest contribution is the integration of pedagogy, AI governance, sustainability, social justice, and curriculum reform into a coherent socioformative framework. Its next challenge is empirical: to demonstrate, through implementation studies and measurable indicators, how this model transforms institutions, communities, and professional formation in diverse contexts.