Smart Diagnostics in the Management of Interinstitutional Agreements for University Educational Quality

Introduction

The COVID health crisis exposed the inequalities existing in educational systems, driving the need to transform educational processes, and showed how fragile, vulnerable and interconnected we are, as well as the sense of uncertainty about the future, the need to change the course we are on, and the need to harness the great potential of education to guarantee the sustainable development of society; in this sense, the new social contract for education issued a call for international solidarity and cooperation (Mendoza et al., 2024).

That social contract, which urges us to imagine our futures together, advocates for the need for common knowledge; in this regard, it makes the following statements in relation to the management of interinstitutional agreements by universities:

• New research possibilities should be prioritized, rather than only creating knowledge limited by institutional and/or regional norms.
• It should be recognized that there are multiple forms of knowledge and that the use of knowledge dissemination in different languages is increasing, for which inclusion and diversity must be ensured.
• Collaboration and cooperation among universities is one of the ways to promote shared global well-being and to transform the future (Whitley, 2012; Mendoza et al., 2024).

To achieve these proposals, universities must take a proactive role in the management of interinstitutional agreements in order to reimagine the future of higher education institutions through powerful, ethical and transformative alliances that contribute to educational quality, ensuring that the academic offering responds to the real needs of the country (Mendoza et al., 2024).

The management of interinstitutional agreements, its approaches and paradigms can foster the transformation of university institutions to produce new, relevant and high-quality knowledge, with theoretical and practical references, both global and contextual. It is necessary to incorporate work with interinstitutional agreements into university dynamics, based on collaboration and cooperation influenced by the potential of Artificial Intelligence, with attention to sustainability, innovation and entrepreneurship, so that it has an impact on preparing future professionals to face the challenges of the future with a comprehensive vision committed to social transformation (Aguilar, 2013; Abreu et al., 2019; Algañaraz et al., 2023).

The management of interinstitutional agreements in the contemporary university context goes beyond the instrumental view of a mere administrative or legal procedure. It emerges as a strategic, complex and dialectical process that directly affects the capacity of the higher education institution (HEI) to fulfill its social mission, adapt to volatile environments and, fundamentally, to guarantee and enhance educational quality.

Understanding its epistemic nature requires a multidisciplinary approach that integrates contributions from systems theory, strategic management, the sociology of organizations and cultural-historical psychology, among other disciplines. In this sense, the objective of this article is to demonstrate the contribution of intelligent assessment, mediated by artificial intelligence and the cultural-historical approach, to the management of interinstitutional agreements and thereby to the improvement of university educational quality.

Methods

The research was developed with a mixed approach, descriptive-propositional in nature and predominantly qualitative, with the intention of systematizing the theoretical and methodological foundations of a smart diagnosis proposal for the management of interinstitutional agreements in the university context, aimed at contributing to educational quality.

Theoretical methods were used to build the conceptual and epistemological scaffolding of the proposal, such as the analytic-synthetic and inductive-deductive methods and theoretical modeling. In addition, the literature review method was used to organize, refine and triangulate information from different documentary sources on agreement management, interinstitutional cooperation, artificial intelligence applied to education and university quality assurance.

Journals from Scopus, Web of Science, SciELO, Redalyc, Dialnet and DOAJ were consulted, as well as institutional repositories of Latin American, European and North American universities related to the social sciences, education and university management. Additionally, specialized sources on Artificial Intelligence and education, such as IEEE Xplore and the ACM Digital Library, were consulted to retrieve literature on applications of machine learning, educational data mining and decision support systems.

Combinations of the following keywords, in Spanish and English, were used for the search strategy: management of interinstitutional agreements, educational diagnosis, university educational quality, artificial intelligence in education, university cooperation, strategic alliances, systems theory, cultural-historical approach, smart diagnosis, university social responsibility, Sustainable Development Goals. Boolean operators (AND, OR, NOT) were used to refine the results, along with time-range filters (2000–2025), prioritizing the last ten years.

The documentary review process was organized into three stages: initial exploration, in-depth analysis, and systematization and integration for the construction of a coherent theoretical framework that integrates the contributions of the different disciplines and approaches and serves as a basis for developing the theoretical proposal for smart diagnosis.

Results and discussion

The specialized literature has addressed agreement management from various angles: its impact on internationalization (Mazzeo & Baudo, 2021; Montesinos, 2023), university-industry linkage (Broström et al., 2019), administrative challenges (Mückenberger et al., 2013), the construction of monitoring indicators (Mex et al., 2023) and its influence on academic and scientific cooperation (Castillo-Leyton et al., 2021; Pratt et al., 2011).

However, a robust epistemological characterization requires overcoming these fragmented approaches and conceiving the process as an open, dynamic and complex system, as proposed by general systems theory (Bertalanffy, 1976).

From this systemic perspective, the management of interinstitutional agreements cannot be understood as a sum of isolated parts, but as an organized totality whose emergent properties, such as the capacity to generate curricular innovation, sustained academic mobility or high-impact collaborative research, are qualitatively superior to the mere aggregation of administrative activities.

This holistic view makes it possible to identify relationships of interdependence among the different subsystems that make it up, as well as the flows of information, resources and decisions that circulate through them. Consistent with the above, the management of interinstitutional agreements in the university context is understood as a systemic and strategic process that integrates structural (organizational, regulatory and stakeholder), functional (diagnosis, planning, negotiation, implementation and evaluation), relational (intra- and interinstitutional links), temporal (life cycle of the agreement, from inception to closure or renewal) and technological (analog and digital support tools) dimensions.

Its purpose is to formalize, execute and monitor alliances with external actors, other universities, companies, government bodies and civil society organizations, in order to mobilize knowledge, resources and capacities that strengthen the comprehensive education of students, relevant and socially significant research, purposeful internationalization and the continuous improvement of the academic offering, all within the framework of the Sustainable Development Goals (SDGs), particularly SDG 4 (inclusive, equitable and quality education) (Moreno et al., 2025), and of the new social contract for education proposed by Roque (2025).

This definition integrates the contributions of Abreu et al. (2019), who link interinstitutional relations to the development of university management, and of Stensaker (2018), who highlights the role of alliances in enhancing control, capacity and creativity in dynamic environments. It is also aligned with Stakeholder Theory (Freeman, 1984), in recognizing that the university must manage in a balanced way the interests of multiple groups, government, students, companies and communities, among others, through its agreements.

This theory, originally developed for the field of business management, has proven its fruitfulness in the university context by making it possible to identify and prioritize the expectations of the different actors who affect or are affected by institutional activity (Mainardes et al., 2011).

From the perspective of educational quality, agreement management takes on particular relevance. It is not simply a matter of increasing the number of agreements signed, a quantitative indicator that often hides low implementation or no impact on training processes. Rather, it is a matter of conceiving each agreement as a pedagogical and organizational device that expands learning spaces, resources and opportunities for students and teachers, while strengthening the social relevance of the academic offering.

In this sense, agreement management becomes a strategic lever for the continuous improvement of quality, understood not only as efficiency and compliance with standards, but as social transformation and the comprehensive education of professionals committed to sustainable development (Harvey & Green, 1993).

Within the systemic architecture of agreement management, diagnosis occupies a central and strategic place. Following Arias (1999) and Nieves (2000), educational diagnosis is a process of constructing knowledge about a reality on which one is going to intervene or act, which implies the active participation of the person diagnosing and a gradual, systematic and continuous approach to the complexity of what is being diagnosed. It is therefore not a static snapshot taken at a specific moment, but a dynamic, unfinished process that is permanently expanded, adjusted and fed back (Enriquez et al, 2024).

In the specific field of interinstitutional agreement management, traditional diagnosis has significant limitations. The usual practice, as identified in preliminary studies and in the researcher's experience, is characterized by being unsystematic, reactive (a diagnosis is made when a problem arises, not preventively), based predominantly on unstructured data (emails, scattered reports, unsystematized meeting minutes) and disconnected from strategic decision-making processes. This situation results in a low predictive capacity: managers cannot anticipate which agreements show warning signs, which have the greatest potential to contribute to educational quality, or what types of alliances should be prioritized in the future.

To overcome these shortcomings, it is necessary to move toward smart diagnosis, understood as one that incorporates advanced data analysis techniques, particularly artificial intelligence (AI), to process large volumes of information, identify hidden patterns, generate predictions and support collegial, evidence-based decision-making.

This approach is inspired by contemporary developments in smart academic diagnosis in education, particularly the works of Fernández et al. (2023; 2024), who have designed systems and models for personalized student support using machine learning algorithms, fuzzy logic and educational data mining.

Transferring these ideas to the field of interinstitutional agreements, smart diagnosis in the management of interinstitutional agreements is defined as a process that articulates the representation of knowledge about agreements and their actors (typologies, partners, resources, historical results), the intelligent prediction of their behavior and potential contribution (risk identification, estimation of impact on educational quality), and the assessment for strategic decision-making aimed at improving comprehensive education, social relevance and the fulfillment of the institutional mission, all within a specific cultural-historical context and mediated by artificial intelligence tools, so as to generate the sustainability of the management of interinstitutional alliances.

This definition incorporates the principles of diagnosis established by Nieves (2000), namely dynamic, continuous, systematic, preventive, feedback-generating, transformative, multi- and interdisciplinary, collaborative and participatory, and updates them in light of the potential offered by digital technologies and artificial intelligence. Furthermore, it distances itself from technocratic conceptions that would reduce diagnosis to a mere algorithmic application, by emphasizing the irreplaceable role of professional judgment, contextual interpretation and the participation of the actors involved.

On the basis of the foregoing premises, a theoretical proposal for smart diagnosis in the management of interinstitutional agreements oriented to university educational quality is put forward. This proposal is not a technical recipe book, but a theoretical-methodological construction that organizes and articulates the processes, phases and tools needed to carry out a comprehensive, intelligent diagnosis aligned with the institutional educational project and with the demands of the social and global environment.

The theoretical proposal rests on three main epistemological foundations, which operate in an integrated way and not as watertight compartments.

The first foundation is the systemic-structural approach. From this perspective, smart diagnosis is conceived as a subsystem of the broader agreement management system, with its own identity but functionally integrated into the whole. Its internal components, phases, procedures, instruments and actors, are organized so that they fulfill a specific function within the global system: to provide relevant, timely and predictive information for strategic decision-making.

The principle of totality implies that diagnosis cannot be understood or implemented in isolation; its meaning and value lie in its contribution to the improvement of agreement management and, through it, to educational quality. The principle of structural hierarchy recognizes that diagnosis operates at different levels (macro, meso, micro), from the analysis of institutional cooperation policy to the evaluation of a specific agreement.

The principle of functionality establishes that each element of the diagnosis subsystem (for example, data collection instruments, prediction algorithms, results reports) fulfills a specific function, and dysfunctions in any of them affect the whole. Finally, the principle of development recognizes that the diagnosis system is not static, but evolves through processes of self-organization, adaptation and continuous improvement.

The second foundation is the cultural-historical theory of Lev S. Vygotsky (1987), whose fundamental categories, mediation, zone of proximal development and internalization, are particularly fruitful for understanding smart diagnosis as a complex psychological and social practice. Mediation explains how technological tools (artificial intelligence systems, prediction algorithms, data analysis platforms, interactive dashboards) act as instruments that transform and expand the cognitive capacities of agreement managers.

These tools are not mere external artifacts; they reconfigure the psychological functions of those who use them, enabling them to process large volumes of information, identify patterns not visible to the human eye, generate predictions based on statistical models and visualize complex relationships among variables.

Mediation operates at three interrelated levels: the instrumental level (material tools such as computers, software and databases), the semiotic level (languages and categories such as "prediction", "early warning" or "probability of success", which structure the way of thinking and speaking about agreements) and the social level (interaction with others in communities of practice, where more experienced managers teach novices).

The zone of proximal development (ZPD), another central concept in Vygotsky's work, illuminates the processes of training and improving competencies for smart diagnosis. In the context of smart diagnosis of agreements, the ZPD manifests itself at multiple levels. At the individual level, a manager may have a current level of diagnostic competence (for example, can carry out basic SWOT analyses) and a potential level (can come to interpret AI-generated predictive models) that will be reached with the help of data experts, decision support systems and collaboration with more capable colleagues.

At the institutional level, the university has a ZPD that expresses the gap between its current agreement management capacity (for example, manual and reactive monitoring) and its potential capacity (predictive and strategic management), which can be reached through the incorporation of advanced technologies, external advice and staff training. At the level of alliances, two universities may have complementary capacities; their collaboration, mediated by a well-managed agreement and a shared diagnosis, allows them to reach levels of development that they would not achieve individually.

The third relevant Vygotskian concept is internalization, understood as the mechanism through which intersychological functions (what occurs between people, mediated by instruments and signs) become intrapsychological functions (internal structures and stable competencies of the subject). In the field of smart diagnosis, internalization involves three processes.

First, the appropriation of tools: the manager not only uses the AI system but incorporates it into their professional repertoire; the system becomes an extension of their cognition. Second, the construction of shared meanings: the terms and categories of smart diagnosis (such as "prediction", "early warning", "probability of success") cease to be external technical words and become operational concepts that structure the everyday practice and professional language of the management team.

Third, the development of stable competencies: the capacity to carry out smart diagnoses is consolidated as an institutional competence, embedded in procedures, routines, information systems and organizational culture. This process of internalization is not automatic; it requires favorable institutional conditions: time for learning, support from authorities, recognition of the new competencies, and a conception of technology not as a substitute for the human but as a mediator of human development.

The third foundation of the theoretical proposal is the integration of theoretical frameworks drawn from cooperation theory, strategic management and the study of the global environment; this integration responds to the need to give diagnosis conceptual solidity and social relevance, avoiding a narrow technicism that ignores the political, cultural and ethical dimensions of interinstitutional cooperation.

First, Network Theory, developed by authors such as Borgatti and Halgin (2011) and Granovetter (1973), provides categories for analyzing the structure of cooperative relationships. It makes it possible to view the university as a node within a broader network of actors (other universities, companies, government bodies, civil society organizations), identify the strength of ties (distinguishing between superficial alliances and deep collaborations), analyze the centrality or periphery of the institution in the network, and detect possible structural gaps or collaboration clusters that can be strategically exploited.

In the proposal, this theory underpins the contextual analysis phase, particularly the identification of opportunities and threats related to the university's position in the cooperation ecosystem.

Second, Stakeholder Theory (Freeman, 1984; Mainardes et al., 2011) complements the analysis by focusing attention on the different interest groups that affect or are affected by agreements. This theory obliges diagnosis to ask not only about the internal efficiency of agreements but about the satisfaction of the expectations of students, teachers, administrative staff, external partners, beneficiary communities and funding bodies. The theoretical proposal operationalizes this theory by including satisfaction indicators, analysis of perceived needs and mechanisms for stakeholder participation in the diagnosis and evaluation phases.

Third, knowledge management, particularly the works of Nonaka and Takeuchi (1995) and Davenport and Prusak (1998), provides a framework for assessing whether agreements are generating, transferring and co-creating knowledge relevant to educational quality. An agreement that does not produce new pedagogical, scientific or technological knowledge, or that fails to ensure that this knowledge is internalized by the university community, is a sterile agreement from the perspective of institutional transformation.

The theoretical proposal incorporates this dimension by assessing the state of collaboration, asking about knowledge flows between partner institutions, about the mechanisms of socialization, externalization, combination and internalization (Nonaka & Takeuchi, 1995), and about the impact of these flows on curricular innovation, teacher updating and joint scientific production.

Fourth, educational quality models, such as the EFQM model (EFQM, 2019) and the theoretical approaches of Harvey and Green (1993), connect agreement management with institutional quality assurance systems. These models consider partnerships with partners and suppliers a relevant criterion for organizational excellence, and establish that continuous improvement processes must be based on evidence, measurement and feedback. In the theoretical proposal, these approaches translate into the requirement of clear performance indicators, the incorporation of diagnosis results into strategic planning cycles, and accountability to accreditation bodies.

Fifth, the theory of change, developed by Weiss (1995) and operationalized through the Logic Model (Kellogg Foundation, 2004), provides the causal structure that makes it possible to predict transformation. This theory requires making explicit the sequence running from inputs (human, financial and technological resources) to activities (workshops, mobility exchanges, joint projects), outputs (number of active agreements, students on mobility, joint publications), short- and medium-term outcomes (students' global competencies, strengthened research networks, updated curricula) and the final impact (improved educational quality, social relevance, fulfillment of the mission).

Theory of Change also requires identifying the critical assumptions, those conditions that must be met for each step in the causal chain to be effective, and the external factors that may facilitate or hinder the process. That logical structure is the basis on which the transformation prediction phase of the theoretical proposal is built.

Sixth, sustainable development theory and its applications in the university sphere, particularly through University Social Responsibility (Vallaeys et al., 2009), give the theoretical proposal an ethical and forward-looking dimension.

Sustainable development, understood as development that meets the needs of the present without compromising the ability of future generations to meet their own, implies that agreements cannot be evaluated solely on their short-term efficiency but on their contribution to equity, inclusion, social justice and environmental protection.

The 2030 Agenda and its 17 SDGs (Moreno et al., 2025) offer an operational framework for this evaluation. The theoretical proposal incorporates this approach by requiring that each strategic agreement make explicit its contribution to one or more SDGs, that indicators of social and environmental impact be designed, and that these aspects be reported on periodically. It is also aligned with the new social contract for education (Roque, 2025), which advocates a cooperative, solidarity-based pedagogy centered on the common good, and which recognizes the existence of multiple forms of knowledge beyond institutional or regional norms.

Finally, Agency Theory (Eisenhardt, 1989) and developments in risk management in VUCA environments (Volatile, Uncertain, Complex and Ambiguous) (Bennett & Lemoine, 2014) provide conceptual tools for addressing the inevitable tensions, information asymmetries and coordination challenges that arise in any cooperative relationship.

Agency Theory explains the need to align the interests of the university and its partners, along with monitoring, incentives and accountability. The literature on VUCA environments, for its part, justifies the incorporation of scenario analysis, contingency plans and flexibility clauses into agreements, so that alliances can withstand and adapt to health crises, political instability, technological disruptions or other external shocks.

On the basis of these integrated foundations, the theoretical proposal is structured in three interrelated phases, not sequential but recursive, which are continuously fed back into one another.

The first phase is contextual analysis, carried out by means of a SWOT matrix (strengths, opportunities, weaknesses, threats) specifically oriented to agreement management. Unlike the generic SWOT, this contextualized version asks about the characteristics specific to the management process: internal strengths (such as the existence of a clear regulatory framework, specialized staff, a portfolio of strategic partners or information systems), internal weaknesses (reactive management, dormant "shelved" agreements with low implementation, insufficient budget, weak internal communication, lack of performance indicators), external opportunities (public funding programs, global trends favorable to internationalization, unmet needs of the productive sector) and external threats (political or economic instability, competition among universities, excessive bureaucracy, academic protectionism, changes in partners' priorities).

This SWOT analysis is enriched with additional layers that incorporate the frameworks mentioned above: the alignment of agreements with the SDGs, with UNESCO's new social contract and with the institutional mission, vision and strategic objectives is assessed. A risk analysis is also incorporated that identifies the vulnerabilities of each agreement in the face of scenarios of uncertainty.

The second phase is the assessment of the state of collaboration and cooperation, which goes beyond SWOT analysis by measuring the "health" of alliances and their degree of maturity. To this end, an evaluation matrix is proposed that considers dimensions such as governance (are there monitoring committees with regular meetings and defined goals, or are the agreements informal and without clear structure?), communication (is it fluid, transparent and multichannel, or reactive and only for solving problems?), shared resources (are activities jointly financed and infrastructure and staff systematically exchanged, or does each institution fund its own separately?), joint activities (are they limited to sporadic mobility or do they include research projects, dual-degree programs, joint laboratories and collaborative publications?), and the strategic nature of the alliance (is it transformative, creating something new that the institutions could not achieve separately, or merely transactional?).

This matrix is complemented by an evaluation of sustainability from the perspective of the SDGs, of ethics and equity in the relationship (avoiding North-South or university-industry hierarchies that reproduce inequalities), of resilience in the face of crises (flexible clauses and contingency budgets) and of the use of data and intelligence for informed decision-making.

The third phase is the prediction of transformation, which uses the structure of the theory of change to project the probable results of agreements and design strategic action pathways. This phase makes explicit the causal chain that connects inputs (budget, specialized staff, aligned partners, regulatory framework) with activities (signing of strategic agreements, creation of monitoring committees, launching of calls for proposals, efficient management of cooperation, mitigation of weaknesses and threats), outputs (number of active agreements, students and teachers on mobility, joint research projects, dual degrees, shared resources, satisfaction indices), short- and medium-term outcomes (students with global competencies, academics with strengthened international networks, high-impact joint publications, updated study programs, greater retention and recruitment of talented students, links with industry, graduate employability) and the final impact (improved educational quality at undergraduate and graduate levels, cutting-edge research, curricular innovation, financial sustainability, recognition in international rankings, positioning as an innovative and globally connected university).

Prediction is not divination, but projection based on historical data, statistical models and artificial intelligence algorithms that identify patterns of success and risk. The theoretical proposal also suggests desirable future scenarios, such as the university becoming a node in a smart global network, the educational experience being reimagined on foundations of cooperation and solidarity, and the evidence generated by alliances influencing fairer and more sustainable public education policies.

The ultimate purpose of the theoretical proposal is to contribute to university educational quality understood in a broad sense: not only as compliance with accreditation standards, but as the comprehensive education of competent, ethical professionals committed to their context; as the generation of relevant and socially significant knowledge; as effective engagement with productive sectors and communities; and as purposeful internationalization, that is, not as an end in itself but as a means for mutual learning, solidarity and the construction of global public goods.

This smart diagnosis makes it possible, specifically, to move from reactive and opportunistic management (signing agreements out of opportunity, without strategic planning) to proactive, evidence-based management; to allocate resources (time, money, human effort) to the agreements that really matter for institutional objectives; to account to the university community and accreditation bodies, demonstrating the concrete impact of cooperation; to make decisions based on data, not only on intuition or personal relationships; and to reimagine the future of the university through powerful, ethical and transformative alliances.

The role of artificial intelligence as a mediator of diagnosis

Within the theoretical proposal, artificial intelligence (AI) occupies a central place as a mediating tool in the Vygotskian sense. The aim is not to replace human judgment with algorithms, but to amplify managers' cognitive capacities and to create a space of human-machine collaboration in which each contributes specific strengths: machines process large volumes of data, identify patterns and generate predictions with a speed and consistency that exceed human capacities; humans contribute contextualization, ethical judgment, interpretation of meanings and strategic decision-making in situations of uncertainty and ambiguity.

Following the Organisation for Economic Co-operation and Development (OECD, 2024), AI is understood as that which develops algorithms and methods that enable a computer system to solve problems intelligently, taking into account the context and the available information in order to achieve the desired goal, with the capacity to learn, reason, plan and diagnose.

Particularly relevant to the diagnosis of agreements is machine learning, which automatically extracts underlying knowledge from data (Fernández, 2024). Generative AI is also relevant, capable of producing complex content (texts, summaries, reports) that emulates human creation, always under supervision and verification by experts (European Union, 2024).

The concrete applications of AI in the smart diagnosis of agreements are multiple. First, text mining and natural language processing (NLP) make it possible to analyze large volumes of meeting minutes, emails, progress reports and open-ended survey responses, extracting common themes, identifying positive or negative sentiments toward the collaboration, and automatically classifying agreements by risk level, main SDG or predominant type of activity.

Second, predictive models, based on classification algorithms such as decision trees, random forests, neural networks or Gradient Boosting Machines, can be trained with historical data from previous agreements (type of partner, duration, committed resources, bureaucratic complexity, level of implementation, results obtained) to predict which new agreements have a high probability of success (understood as effective contribution to educational quality) and which show warning signs that would require early intervention.

Third, recommendation systems can suggest ideal potential partners for an academic, a faculty or the institution as a whole, based on affinity of research interests, previous publications, ongoing projects and institutional profiles. Fourth, interactive dashboards that integrate business intelligence (BI) and dynamic visualizations make it possible to monitor the health of alliances in real time, with risk traffic lights, heat maps of contribution to the SDGs, collaboration network graphs and other elements that facilitate strategic decision-making.

However, the incorporation of AI into diagnosis also poses ethical and methodological challenges that the theoretical proposal explicitly addresses. The first is the risk of algorithmic bias, since AI models learn from historical data that may contain biases (for example, an overrepresentation of agreements with universities of the Global North and an underrepresentation of South-South alliances).

If there is no conscious intervention, AI could perpetuate or even amplify these biases, always recommending the same types of partners or ignoring valuable opportunities in less privileged contexts (Greebon, 2026). The second challenge is the opacity or "black box" nature of some models, which hinders the auditability and explainability of predictions.

The theoretical proposal, consistent with the principles of the Court of Justice of the European Union (CJEU, 2026), requires that the AI systems used be transparent, interpretable and subject to permanent human oversight. The third challenge is dependence on data quality: AI is only as good as the data that feed it. If data on agreements are incomplete, outdated, poorly structured or inconsistent, predictions will be unreliable. Therefore, the implementation of smart diagnosis must be accompanied by institutional data management policies that ensure data relevance, reliability, currency and adequate structuring.

The theoretical proposal also assumes that AI operates under the principles of ethical, human-centered governance. This implies, at a minimum: permanent human oversight in all phases of the diagnosis, without delegating critical judgment to algorithms; inalienable individual responsibility of managers and authorities for decisions made with the support of AI; a culture of verification of data and algorithmic results; and non-discrimination, ensuring that predictions and recommendations do not reproduce or amplify existing inequalities (CJEU, 2026). In this sense, the theoretical proposal is aligned with international recommendations on AI in education, which emphasize that technology must serve pedagogy, inclusion and the common good, and not the other way around (UNESCO, 2020).

Contributions of the theoretical proposal to university educational quality

The theoretical proposal for smart diagnosis described here is not an academic exercise detached from institutional reality, but a tool conceived to produce concrete transformations in agreement management and, through it, in university educational quality. Its main contributions can be summarized in five dimensions.

First, the theoretical proposal makes it possible to strategically align agreement management with the institution's mission, vision and objectives. By incorporating the university's guiding framework as the axis of the SWOT analysis and the evaluation of collaboration, diagnosis ceases to be a generic exercise and becomes a lever for fulfilling the institutional educational project. Agreements that do not contribute to strategic objectives are identified as misaligned and can be redirected, suspended or not renewed, freeing up resources for more relevant alliances.

Second, the theoretical proposal introduces a culture of data- and evidence-based management, overcoming intuition, personal relationships or improvisation as the main criteria for decision-making. Performance indicators, interactive dashboards, risk predictions and algorithmic recommendations provide an objective and systematic basis for deciding which agreements to prioritize, which partners to seek, which resources to allocate and which corrective actions to implement.

Third, the theoretical proposal strengthens the predictive and anticipatory capacity of agreement management. The early identification of agreements with warning signs, low implementation, failure to meet goals, latent conflicts, changes in the environment, makes it possible to intervene in a timely manner, before problems worsen. Likewise, predicting the transformation potential of each agreement helps select those alliances that can generate the greatest added value for educational quality, optimizing the investment of scarce resources.

Fourth, the theoretical proposal makes visible and strengthens the social and ethical dimension of interinstitutional cooperation. By incorporating the SDGs, UNESCO's new social contract and the principles of university social responsibility, diagnosis asks not only about the efficiency of agreements but about their contribution to equity, inclusion, social justice and sustainable development. This is particularly relevant in contexts such as the Dominican one, where gaps in access, territorial inequalities and environmental challenges demand an active commitment from universities.

Fifth, the theoretical proposal promotes institutional learning and continuous improvement; diagnosis is not a one-off event but a recursive process that is fed by the results of implementation and evaluation. The data generated by the smart diagnosis system feed into strategic planning cycles, accreditation processes and quality assurance systems, creating a virtuous circle of reflection, action and improvement. In addition, the use of AI tools and the associated training develop digital and analytical competencies in managers, strengthening the institution's human capital.

Conclusions

The epistemological characterization developed made it possible to establish a solid and coherent theoretical framework for the research, grounded in the integration of the systemic-structural approach, Vygotsky's cultural-historical theory, and a set of theoretical frameworks drawn from cooperation theory, strategic management, educational quality and the study of the global environment. This framework is not a superficial eclecticism but a dialectical synthesis that recognizes the complexity of the object of study and offers powerful categories for its analysis.

Interinstitutional agreement management has been defined as a systemic and strategic process, oriented to university educational quality, that integrates structural, functional, relational, temporal and technological dimensions. Smart diagnosis has been defined as a process that articulates knowledge representation, intelligent prediction and assessment for decision-making, mediated by artificial intelligence tools.

A theoretical proposal structured in three phases was achieved, SWOT+ contextual analysis, assessment of collaboration and prediction of transformation, which operate in an integrated and recursive manner.

The incorporation of Vygotsky's ideas has proven particularly fruitful, as it makes it possible to conceive smart diagnosis not as a mere technical application but as a complex social practice, mediated by instruments and signs, which develops in the zone of proximal development of managers and institutions, and which is internalized as a stable competence and organizational culture. Artificial intelligence, from this perspective, is not a substitute for human judgment but a powerful mediator that amplifies cognitive capacities and transforms management practices.

The integrated theoretical frameworks, Network Theory, Stakeholder Theory, Knowledge Management, Quality Models, Theory of Change, Sustainable Development Theory, University Social Responsibility, Agency Theory and risk management approaches in VUCA environments, provide the analytical categories needed to give the theoretical proposal conceptual depth and social relevance. Each of these frameworks contributes a specific lens that, taken together, allows a multidimensional understanding of the phenomenon.

The characterization presented here does not exhaust the theoretical work but opens it up. In subsequent studies, this theoretical framework will be contrasted with the empirical reality of the Catholic University of Santo Domingo through a diagnosis of the current state of agreement management. Subsequently, the methodological guidelines for implementing the theoretical proposal will be developed, and the software engineering design for its deployment in a web environment will be presented.

What is established, by way of conclusion, is that the management of interinstitutional agreements, when approached from a systemic, dialectical and prospective perspective, and when supported by a smart diagnosis mediated by AI, ceases to be a peripheral administrative process and becomes a strategic engine of university educational quality and social transformation. This is the main theoretical contribution that the research aims to demonstrate and validate.

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