Template-Type: ReDIF-Article 1.0 Author-Name: Carmen Jaritza Petao Salazar Author-Workplace-Name: Casa Grande University, Ecuador Title: Designing operational ethical frameworks for the use of generative AI in higher education Publication-Status: Published Abstract: This doctoral study developed and validated an Operational Ethical Framework (OEF-GAI) to guide the responsible use of Generative Artificial Intelligence (GAI) in higher education. Using a sequential mixed-methods approach (documentary analysis, case study, survey n=1,247, and Delphi method), it identified a critical gap between abstract ethical principles and their practical institutional application. Results showed that the university community demands practical guidance and training over mere prohibitions. The validated OEF-GAI responds with a three-layer architecture: Foundations (guiding principles), Operational Pillars (governance, practical instruments, training), and an Application Cycle for continuous improvement. It is concluded that the ethical integration of GAI requires institutionalizing processes of reflection and support, transforming the abstract dilemma into manageable practice that fosters responsible innovation. Keywords: Generative artificial intelligence; ethics in higher education; operational framework; policy implementation; university governance. Journal: Applied Journal of Digital Innovation Pages: 15 Volume: 2 Year: 2026 Month: January DOI: 10.65835/aj.2026.2.15 File-URL: https://aj.reincisol.com/index.php/aj/article/view/15 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/15/31 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:2:y:2026:id:15 Template-Type: ReDIF-Article 1.0 Author-Name: María Teresa Nagua Velepucha Author-Workplace-Name: Faculty of Humanities and Social Sciences Technical University of Manabí Title: Comparative analysis of language models in automated educational diagnostic tasks Publication-Status: Published Abstract: This study presents a comparative analysis of language models for automated educational diagnosis, evaluating two specialized models (a feature-based ML model and a fine-tuned BERT) against three state-of-the-art Large Language Models (GPT-4, Claude 3, Gemini Pro). Using a mixed-methods approach on a corpus of 450 annotated student texts, the research assessed diagnostic accuracy, feedback quality, robustness, and hallucination rates. Results indicate that advanced LLMs, particularly GPT-4 and Claude 3 Opus, achieve superior diagnostic performance (F1-Score ≥ 0.83) and generate pedagogically higher-quality feedback, rated as more useful and actionable by expert judges. However, these models exhibit a critical weakness: a significant propensity for factual hallucinations (5.2% - 12.3%). In contrast, specialized models, while less sophisticated, offer near-perfect reliability and greater cost-efficiency. The study concludes that LLMs represent a qualitative leap in automated assessment but are not yet reliable for autonomous application. The most viable implementation model is AI-augmented education, where LLMs act as diagnostic assistants to teaching professionals, who provide essential oversight, validation, and personalization. This human-in-the-loop paradigm balances technological potential with pedagogical integrity and ethical responsibility. Keywords: Automated assessment; large language models; educational diagnosis; formative feedback Journal: Applied Journal of Digital Innovation Pages: 13 Volume: 2 Year: 2026 Month: January DOI: 10.65835/aj.2026.2.13 File-URL: https://aj.reincisol.com/index.php/aj/article/view/13 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/13/29 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:2:y:2026:id:13 Template-Type: ReDIF-Article 1.0 Author-Name: Moscú Cristian Iosif Author-Workplace-Name: University of Architecture and Planning Ion Mincu: Bucharest, Rumania Title: Assessment of learning and digital innovation in post-pandemic Higher Education Publication-Status: Published Abstract: This mixed-methods study examines the integration of digital tools and pedagogical innovation in higher education assessment practices in the post-pandemic context. Through a sequential explanatory design, quantitative data from 428 faculty surveys and qualitative insights from four institutional case studies were analyzed. Results reveal a state of "partial hybridization," characterized by the predominant use of digital tools for traditional summative assessment (M=4.12), with significantly lower adoption of formative (M=2.87) and authentic (M=2.15) digital evaluations. The perceived level of pedagogical innovation remains moderate-low (M=2.94). Key predictors of innovation are faculty self-perception of digital competence (β=.35) and pedagogical not merely technological institutional support (β=.28). Qualitative findings identify systemic barriers: a bifurcated support model, a cultural paradox privileging control over trust, and intra-institutional resistance rooted in traditional assessment paradigms. The study concludes that the post-pandemic challenge is not technological but cultural and pedagogical, requiring a systemic transformation towards an intelligent hybrid assessment ecosystem focused on trust, authentic learning, and competency development. Keywords: Digital assessment; pedagogical innovation; post-pandemic higher education; mixed methods; summative evaluation. Journal: Applied Journal of Digital Innovation Pages: 11 Volume: 2 Year: 2026 Month: January DOI: 10.59282/aj.2026.2.11 File-URL: https://aj.reincisol.com/index.php/aj/article/view/11 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/11/27 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:2:y:2026:id:11 Template-Type: ReDIF-Article 1.0 Author-Name: Andrea Alexandra Rodríguez García Author-Workplace-Name: Universidad Técnica de Manabí - Ecuador Title: Strategic Integration of Generative Artificial Intelligence into Organizational Open Innovation Processes: A Sequential Explanatory Mixed-Methods Study Publication-Status: Published Abstract: This mixed-methods study (survey n=142 + qualitative cases) found that normative uncertainty (ethical, regulatory, IP concerns) is the main barrier to integrating Generative AI into open innovation (???? = −.408), surpassing technical limitations. Leadership support and technical capabilities are key drivers. Successful integration requires a Strategic Sociotechnical Integration framework with four pillars: proactive governance, ambidextrous architecture (hybrid AI Innovation Cells), human-AI co-evolution loops, and relational capital renewal. Competitive advantage comes from orchestration capability, not superior algorithms. Keywords: Generative Artificial Intelligence (GenAI); Open Innovation; Strategic Integration; Sociotechnical Systems; Innovation Ecosystems. Journal: Applied Journal of Digital Innovation Pages: 16 Volume: 2 Year: 2026 Month: January DOI: 10.65835/aj.2026.2.16 File-URL: https://aj.reincisol.com/index.php/aj/article/view/16 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/16/32 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:2:y:2026:id:16 Template-Type: ReDIF-Article 1.0 Author-Name: Angela Noemi Sánchez Cedeño Author-Workplace-Name: Department of Social and Behavioral Sciences Faculty of Humanities and Social Sciences Technical University of Manabí Title: AI-based educational recommendation systems and their impact on student retention Publication-Status: Published Abstract: This doctoral study evaluated the impact of an Artificial Intelligence-based Recommendation System on university student retention using a mixed-methods quasi-experimental design. Results demonstrated that the experimental group using the AI system achieved significantly higher retention rates (94.2% vs 86.3%) and showed 53% greater academic engagement. Mediation analysis revealed that approximately 39% of the positive effect on retention was mediated by increased student engagement. Qualitative findings indicated high perceived usefulness, particularly in personalization and early difficulty detection. The impact was more pronounced among first-generation students and those with low initial self-efficacy, suggesting these technologies' potential as tools for educational equity. The study concludes that these systems are most effective when integrated as complements to a broader student support ecosystem, subordinating technology to pedagogical and ethical objectives. Keywords: Educational recommendation systems; Artificial Intelligence; student retention; machine learning; student engagement. Journal: Applied Journal of Digital Innovation Pages: 14 Volume: 2 Year: 2026 Month: January DOI: 10.65835/aj.2026.2.14 File-URL: https://aj.reincisol.com/index.php/aj/article/view/14 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/14/30 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:2:y:2026:id:14 Template-Type: ReDIF-Article 1.0 Author-Name: Luis Efrén Rua Sánchez Author-Workplace-Name: Technical University of Manabí, Ecuador Title: Impact of AI Virtual Assistants on Teachers' Pedagogical Decision-Making Publication-Status: Published Abstract: This study investigates the impact of AI-powered virtual assistants (e.g., ChatGPT, educational chatbots) on teachers’ pedagogical decision-making. Employing a sequential exploratory mixed-methods design (QUAL → QUAN), the research involved in-depthinterviews and focus groups with 16 educators, followed by a survey of 412 secondary and higher education teachers. Findings reveal that AI does not replace but reconfigures teacher decision-making into a hybrid cycle of diagnosis, AI consultation, and critical evaluation. The core conclusion is that teacher agency the capacity for critical judgment and contextual adaptation is the strongest predictor of positive outcomes. While AI enhances personalization and efficiency, its benefits are significantly moderated by institutional support and unresolved ethical concerns regarding bias, transparency, and learning authenticity. The study contributes an evidence-based, agency-mediated model of technology adoption, highlighting that the ultimate impact of AI in education depends less on algorithmic power and more on empowered professionals who can critically integrate these tools into their practice. Keywords: Artificial Intelligence; teacher agency; pedagogical decision-making; virtual assistants; educational technology Journal: Applied Journal of Digital Innovation Pages: 12 Volume: 2 Year: 2026 Month: January DOI: 10.59282/aj.2026.2.12 File-URL: https://aj.reincisol.com/index.php/aj/article/view/12 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/12/28 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:2:y:2026:id:12