Jorge Hochstetter-Diez

Large language models can produce convincing answers to questions about legal documents, but linguistic plausibility does not establish whether those answers preserve the meaning of the source or whether their claims can be traced to supporting evidence. Retrieval-based approaches reduce part of this uncertainty by providing external textual context, yet retrieved passages do not necessarily make explicit the legal entities, relations, conditions, exceptions, and provenance that determine why the evidence supports a particular conclusion. This study introduces Knowledge-grounded Legal AI (KGLAI), an architecture that combines Legal NLP, a domain ontology, a document-grounded knowledge graph, provenance, semantic validation, and language generation within a common evidence flow. KGLAI was evaluated against a textually grounded LLM in a controlled paired experiment involving seven non-disclosure agreements, 70 document-question cases, and 420 model executions. Both treatments used the same documents, questions, language model, and generation configuration. Legal fidelity was evaluated through the Legal Fidelity Index (LFI), documentary traceability through STI-D, and the additional semantic evidence path available in KGLAI through STI-S. KGLAI achieved a mean LFI of 0.8029 compared with 0.7050 for textual grounding and increased documentary traceability from 0.6498 to 0.7536. The difference in legal fidelity became larger as question complexity increased, from 0.0348 for direct extraction tasks to 0.1715 for questions requiring evidence integration across contractual provisions. KGLAI also preserved a mean semantic traceability score of 0.8811, while evidence coverage increased and unsupported claims decreased. These findings indicate that the contribution of explicit knowledge grounding is most visible when legal questions depend on relations, conditions, exceptions, or evidence distributed across the document. Rather than replacing legal judgment, the proposed architecture provides an experimentally testable mechanism for making the evidence behind generated legal answers more explicit and inspectable.

Organizations face increasing regulatory and stakeholder pressure to integrate environmental, social, and governance commitments into strategic decision-making, yet sustainability reporting often remains disconnected from planning, operational control, and performance measurement. This study develops MISE as a theoretically informed, design-oriented organizational architecture that couples double materiality, strategic ESG programs, governed indicators, documentary evidence, and a Balanced Scorecard–Global Reporting Initiative (BSC–GRI) dashboard. An integrated multi-source single-case study was conducted in a Colombian industrial company using documentary, perceptual, technical, performance, and expert-appraisal evidence. The evidence base comprised 62 study records: 48 internal organizational participants, 8 external stakeholders, and 6 internal expert evaluators with a separate methodological role. The structured consultation obtained 51 responses from 56 eligible participants (91.1%). The case application formalized nine strategic sustainability programs and expanded and governed the measurement architecture from 6 to 18 KPIs. Between the 2024 reference measurement and the 2025 follow-up measurement, complete governance attributes for the 18 KPIs increased from 38/72 (52.78%) to 56/72 (77.78%), equivalent to +25.00 percentage points and +47.37% relative change. Combined Scope 1 and Scope 2 emissions decreased from approximately 1000 to 880 tCO₂e (−12.0%). Six experts appraised usefulness, feasibility, traceability, governance, and scalability potential. These observed changes describe the application period and do not constitute causal effects attributable exclusively to MISE. The contribution is the explicit coupling logic that links prioritization, execution, governed measurement, evidence, review, and reassessment within one case-based governance architecture.

The management of intellectual property (IP) agreements in universities continues to rely on static legal documents that are signed, archived, and consulted when necessary, but whose content is rarely formalized to facilitate their operation and verification. Consequently, obligations, permissions, restrictions, deadlines, scopes, and exceptions often remain scattered across clauses drafted in natural language, annexes, emails, and different document versions, which hinders their monitoring and makes compliance review dependent on intensive legal and administrative work. In response to this limitation, this article proposes an ontology to formalize non-disclosure agreements (NDAs) at the University of Caldas, Colombia, understood as a specific case within the broader management of IP agreements. The proposal adopts a modular Semantic Web architecture composed of a reusable ontological core and a specialized profile for NDAs. Its construction followed the METHONTOLOGY methodology, and its specification was supported by Competency Questions (CQs), which were subsequently translated into SHACL constraints and SPARQL queries. In addition, a SKOS vocabulary is incorporated to normalize synonyms and terminological variants typical of legal drafting in Spanish, together with a lightweight weak supervision layer based on regular expressions, SKOS, and structural signals to support clause labeling and the batch generation of RDF instances. Thus, the proposal enables querying, traceability, and verification over NDA content, while offering a formal basis for progressing toward automatable controls and their eventual articulation with smart contracts.

One of the factors that has steadily eroded the legitimacy of public institutions is the recurrence of corruption and irregularities in state management, especially in procurement processes, budget allocation, and public appointments. These phenomena have intensified public mistrust and highlight the urgent need for tools to strengthen transparency in the digital sphere. In this context, e-government platforms have become key mechanisms for promoting openness and accountability in public administration. However, ensuring transparency in electronic procedures remains challenging due to the lack of standardized auditing mechanisms and system interoperability. The objective of this study is to design, implement, and technically evaluate a software architecture for an electronic platform that operationalizes a maturity-based diagnostic model to assess transparency in electronic procedures within public organizations. The proposed platform enables real-time monitoring, centralized data management, automated reporting, and evidence-based transparency assessment. The architecture follows a structured design process aligned with ISO 25010 quality attributes, incorporating microservices, database replication, and load balancing to enhance system performance and security. Performance tests identified bottlenecks in authentication, query processing, and concurrent user load, leading to optimizations such as query indexing, caching, and message queue implementation. The results indicate that a well-structured software architecture enhances government transparency by ensuring auditable, traceable, and secure public data access. The platform provides a self-diagnostic tool for institutions to assess their transparency maturity based on open government principles.

Cybersecurity maturity models, such as NIST CSF, C2M2 and ISO 27001, offer structured frameworks to evaluate organizational security postures. However, these models often fail to provide actionable and prioritized roadmaps to improve cybersecurity practices. To address this limitation, we introduce a scalable, service-oriented platform that operationalizes the AIM Triad - a context-aware strategy that encompasses Awareness, Infrastructure, and Management - to facilitate systematic improvements in cybersecurity maturity. Unlike conventional approaches, the platform generates two distinct outputs: standards-compliant assessment reports aligned with frameworks like C2M2, and customized, prioritized action plans tailored to organizational requirements. The system is built on a microservice architecture utilizing Vue.js, NestJS, and Flask, with Kubernetes orchestration ensuring modular adaptability across multiple maturity frameworks while maintaining scalability and resilience. Our proposed platform incorporates custom prioritization of security actions based on organizational needs, automated simulation of maturity assessments through a model-agnostic engine, and PDFbased reporting to support compliance and strategic planning. By bridging the gap between diagnostic evaluations and executable strategies, the platform serves as a blueprint for next-generation tools designed to advance cybersecurity improvement efforts, specially tailored for small and medium-sized organizations (SMO), but also for public institutions.

Context: Eliciting business processes is critical for understanding and optimizing organizational workflows. This study examines methodologies, tools, and techniques that aim to enhance the accuracy and reliability of process modeling while addressing challenges such as inconsistent data and the lack of standards for unstructured processes. Objectives: The primary goal is to identify and classify approaches and techniques used in process elicitation, categorizing them into key areas and assessing their impact on continuous improvement in organizations. Methodology: A systematic mapping was conducted to review existing literature, classifying selected studies into methodologies, modeling tools, and automation frameworks. Current trends and gaps in the field were also analyzed. Results: The study highlights the growing adoption of tools such as BPMN, collaborative approaches, and advanced technologies to address data inconsistencies. The findings emphasize the importance of integrating interdisciplinary approaches and adaptive tools to align with dynamic business environments. This work lays the groundwork for further research to enhance organizational process elicitation practices. Future work: Future research should explore the implementation of emerging technologies, such as artificial intelligence and big data analytics, to further automate process elicitation. Additionally, evaluating the effectiveness of collaborative approaches across diverse industries and analyzing the impact of tool customization in specific contexts is recommended to ensure scalability and efficiency. Ensure that your abstract reads well and is grammatically correct.

Bioethics has become a crucial discipline in higher education. This study aims to understand the institutionalization of bioethics within this field and examine documented initiatives related to its integration. To achieve this, a systematic mapping was conducted using Petersen and Kitchenham's methodology, analysing 444 relevant articles. The results indicate a higher concentration of studies addressing the institutionalization of bioethics at the undergraduate level (40.5 per cent), with fewer publications focused on the postgraduate level (19.4 per cent). Most of the studies are concentrated in the health sciences (81.3 per cent), while the remainder are distributed among the social sciences, humanities, biological sciences, and engineering. The analysis revealed a significant lack of studies on comparative practices and the use of replicated experiences (2 per cent), as well as limited evidence on the institutionalization of bioethics research in higher education (10.3 per cent). These gaps underscore the need for further research in these areas. Additionally, the integration of active methodologies and educational technologies has enhanced bioethics programmes (21.4 per cent), fostering more interactive and engaged learning environments. This study highlights a critical gap in the implementation of bioethics based on empirically validated experiences. It proposes the development of an implementation model that adapts these experiences to different educational contexts. Cooperation between institutions and the creation of research networks are recommended as key strategies for sharing best practices, thereby facilitating the more effective and widespread integration of bioethics across various educational settings.

Competency-based assessment in engineering education is becoming increasingly critical as the profession faces rapid technological advances and the growing need for cross-cutting competencies. This paper introduces SmartRubrics, an AI-based tool designed to support the automated generation of competency-based assessment rubrics. The development of this tool is based on a systematic literature mapping study conducted between 2019 and 2024, which identified key gaps, such as the limited integration of digital tools and the under-representation of transversal skills in current assessment practices. By addressing these gaps, SmartRubrics aims to support the standardisation, accessibility, and potential enhancement of competency-based assessment practices, aligned with UNESCO’s Sustainable Development Goal 4 (SDG4). Preliminary testing of the prototype with computer science educators has provided valuable information on the effectiveness of the tool and areas for improvement. Future work includes further experimental validation in real educational settings to assess the impact of the tool on teaching and learning practices.