Scientia et PRAXIS

Complete Journal Scientia et PRAXIS.Vol. 6 No.11.2026.

2026-06-26T17:09:46+00:00

Editorial Letter
Volume 06, Number 11 | January–June 2026

The Academia Mexicana de Investigación y Docencia en Innovación (AMIDI), through the Editorial Board of the journal Scientia et PRAXIS, presents Volume 06, Number 11, corresponding to the January–June 2026 period, as a regular issue. This edition features original and unpublished scientific works that explore how multidisciplinary activity serves as a driving force for innovation with impact on sustainable development and social transformation. The contributions included in this volume stand out for their articulation between theoretical knowledge (Scientia) and practical application (Praxis), in alignment with the Sustainable Development Goals (SDGs).

The articles presented in this issue are as follows:

1 Toward a Sustainable Innovation in the Faculty Evaluation Process: A Systematic Review of AI, Data Science, and NLP Applications in Higher Education.
(Article written in Spanish)
Authors: Cristian Ulises Barenca-Sotelo, Ma. Del Rocío Maciel-Arellano, Víctor Manuel Larios-Rosillo

Universidad de Guadalajara, Guadalajara, Jalisco.

Summary: The article analyzes, through a 2019–2024 systematic review, the use of artificial intelligence, data science, and natural language processing in university teaching evaluation. Its main

contribution lies in shifting traditional evaluation based on averages and closed-ended surveys toward analytical models capable of interpreting student comments, identifying performance patterns, and supporting institutional decision-making. The proposal is linked to process innovation, the Oslo Manual, and SDGs 4 and 9, highlighting its relevance for multicampus public universities. Its value resides in integrating technology, sustainability, and academic improvement, although further empirical validation is required in specific institutional contexts.

DOI: https://doi.org/10.55965/setp.6.11.a1

2 Emotions and Machine Learning in Innovation within Mexico’s Sustainable Used- Vehicle Market.
(Article written in Spanish)
Authors: Francisco Jacobo Murillo-López.

Universidad Autónoma de Aguascalientes, Aguascalientes, México.

Summary: The article analyzes how consumer emotions influence loyalty within Mexico’s used- vehicle market, integrating neuroeconomics, consumer behavior, and machine learning. Based on a sample of 1,000 buyers in Aguascalientes, it compares logistic regression and Random Forest, showing the greater predictive capacity of the nonlinear model. It identifies satisfaction and safety as drivers of loyalty, while fear and confusion operate as barriers. Its contribution is mainly aligned with SDG 12, through responsible consumption and circular economy, and with SDG 9, through process innovation based on predictive analytics for sustainable business decision-making in contemporary regional automotive markets.

DOI: https://doi.org/10.55965/setp.6.11.a2
3 Gender, Culture of Peace, and Citizen Participation as Evidence-Based Social Innovation for Sustainable Governance.
(Article written in English).
Authors: Tania Marcela Hernández-Rodríguez, César Omar Mora-Pérez Universidad de Guadalajara, Guadalajara, Jalisco, México.

Summary: The article analyzes how gender moderates the relationship between culture of peace, citizen participation, and the perception of neighborhood conflicts in urban communities within the Guadalajara Metropolitan Area. Using a cross-sectional quantitative approach and a sample of 229 residents, it applies validated scales, exploratory factor analysis, Cronbach’s alpha, Pearson correlations, and interaction regression. Its contribution lies in supporting evidence-based social innovation interventions for sustainable urban governance with a gender perspective, aligned with SDGs 5, 11, and 16. The study provides relevant empirical evidence, although its scope is limited by its cross-sectional design and metropolitan geographic concentration.

DOI: https://doi.org/10.55965/setp.6.11.a3
4 Extended CAITIZEN: A PLS-SEM Study of AI-Assisted Sustainable Citizenship Innovation.

(Article written in English).

Author: Juan Mejía-Trejo
Universidad de Guadalajara, Guadalajara, Jalisco, México.

Summary: The article quantitatively validates the extended CAITIZEN model through PLS-SEM, proposing an explanatory-predictive structure for AI-assisted sustainable citizenship. Its central contribution is the transformation of a previous qualitative framework into a measurable model, in which critical AI literacy predicts ethical awareness, data justice, human-AI collaboration, and metacognitive transparency. The results confirm relevant effects on CAITIZEN, except for the direct effect of ethical responsibility. The study strengthens educational innovation aligned with SDGs 4 and 9, while acknowledging limitations related to non-probabilistic sampling, cross-sectional design, and

a student-based sample, thus requiring future comparative validations across diverse university institutional contexts.

DOI: https://doi.org/10.55965/setp.6.11.a4

These articles reflect a significant contribution to sustainable development from innovative and multidisciplinary perspectives. The authors, the AMIDI authorities, and the Editorial Board of Scientia et PRAXIS who contributed to this volume sincerely hope that readers will find the information accessible, rigorous, and useful for their educational, research, or professional objectives. They also warmly invite scholars and professionals to submit their own work to future issues like this one—dedicated to critically examining national and global challenges and proposing evidence-based, socially committed solutions from an academic perspective.

Dr. Juan Mejía-Trejo

Director
Academia Mexicana de Investigación y Docencia en Innovación (AMIDI)
June 2026, Zapopan, Jalisco, México

Dr. Carlos G. Borbón-Morales

Editor-in-Chief

Scientia et PRAXIS

Academia Mexicana de Investigación y Docencia en Innovación (AMIDI) June 2026, Zapopan, Jalisco, México

Toward a Sustainable Innovation in the Faculty Evaluation Process: A Systematic Review of AI, Data Science, and NLP Applications in Higher Education

2026-02-23T05:47:18+00:00

Context. Teaching evaluation in public multicampus universities still relies on averages and closed-ended surveys, which are limited in capturing the complexity of academic performance. In response, this study presents a systematic review (2019–2024) on the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) in university teaching evaluation processes, conceptualized as process innovations from Oslo Manual and aligned with Sustainable Development Goals (SDGs) 4 and 9.

Problem. Traditional systems in high-enrollment institutions exhibit low analytical precision, limited use of qualitative data, and delayed feedback. Accordingly, the following research question is posed: which techniques and algorithms does the literature report for the integrated analysis of quantitative and qualitative data in university teaching evaluation?

Purpose. To critically analyze the use of data science and AI—particularly NLP—to enhance teaching feedback, overcoming the limitations of traditional approaches and promoting timely, in-depth, and scalable evaluation processes.

Methodology. The PRISMA protocol was followed through searches using Boolean operators in Scopus, Web of Science, IEEE Xplore, and ERIC. After applying inclusion criteria and a two-phase peer-review process, 17 studies published between 2019 and 2024 were analyzed.

Findings. Techniques such as sentiment analysis, topic modeling (LDA), and large language models (LLMs)—notably DistilBERT, with accuracy levels close to 93%—consistently outperform traditional methods in managing large volumes of information.

Originality. The study’s originality lies in integrating dispersed literature on AI and NLP in higher education within a coherent process-innovation framework, combining technical, empirical, and ethical rigor.

Conclusions and Limitations. Advanced AI and NLP models show high potential to transform evaluation in Latin American university networks by enabling personalized feedback. However, challenges remain regarding model interpretability, bias mitigation, the scarcity of labeled data in Spanish, and institutional resistance to change, opening relevant avenues for future research and applied developments

Emotions and machine learning in innovation within Mexico’s sustainable used-vehicle market

2026-03-15T05:01:51+00:00

Context. The used-vehicle market in Mexico is essential for mobility and the circular economy. However, traditional studies prioritize rational variables, overlooking emotional factors.

Problem. Traditional linear models fail to capture the interdependence of emotional responses. Thus, the Research Question arises: To what extent do emotions predict loyalty in Mexico’s used-vehicle market?

Objective. To evaluate the impact of emotions on consumer loyalty using machine learning techniques to optimize decision-making in this sector.

Methodology. A quantitative study was conducted with 1,000 buyers in Aguascalientes. The PANAS instrument was used, and a logistic regression model was compared against a Random Forest model. The superiority of the non-linear model was validated using the DeLong test (Z = 3.84; p < 0.001).

Findings. The Random Forest algorithm achieved 87% accuracy. Satisfaction and security are the main predictors of loyalty, while fear and confusion act as critical purchasing barriers.

Originality. The study provides a theoretical advancement by integrating neuroeconomics with data science (Scientia). Additionally, it offers a predictive tool for companies in the sector to design sales strategies based on the customer's emotional experience (Praxis). It provides a framework for reducing emotional barriers in the second-hand market and contributes directly to SDG12.

Conclusions and limitations. The findings confirm that emotions are robust predictors of pre-owned vehicle purchase decisions and that their strategic management is critical for fostering responsible consumption. The primary limitation lies in the use of a non-probabilistic sample from a single region. Future research should employ longitudinal designs and replicate the study across diverse contexts while maintaining the multidisciplinary approach.

Gender, Culture of Peace and Citizen Participation as Evidence-Based Social Innovation for Sustainable Urban Governance

2026-05-13T00:29:18+00:00

Context. Gender inequalities in civic participation and conflict resolution persist in urban communities of the Guadalajara Metropolitan Area (GMA), where between 62% and 91% of residents report insecurity or neighborhood conflicts, and fewer than half participate in democratic processes.

Problem. Despite growing interest in peacebuilding and participatory governance, the moderating role of gender in the relationship between culture of peace, citizen participation, and perceptions of neighborhood conflicts remains insufficiently explored. Therefore, it is relevant to ask how gender influences these relationships within urban communities.

Objective. To analyze how gender moderates the relationship between culture of peace, citizen participation, and neighborhood conflicts in urban communities of the GMA, as a basis for designing social innovation interventions oriented toward sustainable development.

Methodology. A quantitative cross-sectional study was conducted in ten communities of the GMA during March–April 2024, with a sample of urban residents (n = 229). The subjects of study were the inhabitants of these communities, while the object of analysis was the relationship among culture of peace, citizen participation, gender, and perceptions of neighborhood conflicts. Validated scales were applied for culture of peace (7 items), citizen participation (9 items), and neighborhood conflicts (12 items), using five-point Likert anchors. The analysis employed exploratory factor analysis, Cronbach’s alpha, Pearson correlations, and linear regression with interaction terms to evaluate the moderating effect of gender.

Theoretical and practical findings. High reliability was confirmed across all scales (α = .832–.931). Both culture of peace and citizen participation showed positive and significant correlations with the general perception of conflicts. Gender emerged as a significant moderator of the culture of peace–conflict relationship (B = .217, p = .043), but not of the participation–conflict relationship.

Originality. The study is classified as social innovation and organizational-process innovation, in accordance with the Oslo Manual (OECD/Eurostat, 2018), as it generates empirical evidence to redesign community practices of participation, peacebuilding, and conflict management with a gender perspective in sustainable urban governance. It also contributes to SDGs 5, 11, and 16.

Conclusions and limitations. Gender moderates the relationship between culture of peace and conflict perception, but not the relationship between participation and conflict. The cross-sectional design and geographic concentration in the GMA limit generalizability; therefore, longitudinal and comparative studies are required.

Extended CAITIZEN: A PLS-SEM Study of Sustainable AI-Assisted Citizenship Innovation

2026-06-25T01:52:44+00:00

Context. Artificial intelligence is transforming higher education by reshaping learning, creativity, decision-making, and civic participation. This study examines CAITIZEN—Citizenship Assisted by Artificial Intelligence for Sustainable, Ethical, and Networked Formation—as an extended model for validating AI-assisted sustainable citizenship as an innovation for sustainable development, aligned with SDG4 and SDG9.

Problem. Although the original CAITIZEN model was qualitatively grounded as an ethical–cognitive–social framework, its explanatory and predictive capacity had not been empirically tested. AI education still prioritizes efficiency, automation, and technical adoption, with limited evidence on how critical AI literacy, ethics, data justice, human–AI collaboration, and metacognitive prompting contribute to sustainable AI-assisted citizenship.

Purpose. This study validates the extended CAITIZEN model through PLS-SEM by examining how Critical Artificial Intelligence Literacy (CAIL) enables Ethical Awareness and Responsibility (EAR), Awareness of Fairness and Data Justice (AFDJ), Human–AI Creative Collaboration (HAIC), and Metacognitive Transparency in Prompting Practices (MTPP), and how these capacities predict CAITIZEN.

Methodology. This research builds on a previous qualitative phase conducted in Guadalajara, Jalisco, Mexico, during July–December 2025, and complements it with an explanatory-predictive quantitative design using SmartPLS 4.1.1.8 to assess reflective constructs and predictive relevance through PLSpredict.

Theoretical and Practical Findings. Results show that CAIL significantly predicts EAR, AFDJ, HAIC, and MTPP, confirming its role as foundational antecedent. AFDJ, HAIC, and MTPP significantly predict CAITIZEN, whereas EAR does not show a direct effect. Predictive relevance is confirmed because all Q²_predict values for CAITIZEN indicators are positive and all PLS-LM RMSE differences favor PLS-SEM.

Originality. The study transforms the qualitative CAITIZEN model into an empirically validated explanatory-predictive structure.

Conclusions and Limitations. The extended CAITIZEN model provides a measurable framework for responsible AI education and sustainable innovation. Limitations include non-probabilistic sampling, cross-sectional design, and student sample.