Template-Type: ReDIF-Article 1.0 Author-Name: Surgey Bolivia Caicedo Author-Workplace-Name: Universidad de Pamplona: Norte de Santander Title: Evaluation of an AI translator for adapting curriculum content to indigenous languages in primary schools. Publication-Status: Published Abstract: This study critically evaluates the potential of Artificial Intelligence (AI) translators for adapting curricular content to indigenous languages in primary education. Using a mixed-methods approach (quantitative and qualitative) that included technical evaluation of AI models and perspectives from specialists, teachers, and indigenous communities, the findings reveal a fundamental paradox. Although specialized AI systems show technical improvements in fluency and syntactic accuracy, they exhibit a structural insufficiency in cultural-pedagogical relevance, the core of meaningful adaptation. The most serious errors were related to cultural correspondence and linguistic calques. The research identifies risks such as "hidden standardization" (amplification of majority dialectal variants) and underscores that ethical governance and community sovereignty over data and processes are non-negotiable prerequisites for any implementation. It is concluded that AI cannot replace the creative and situated role of the human translator, but could function as a technical assistant within a "Community-Supervised Hybrid Model", where technology is subordinate to indigenous agency and control. Feasibility depends less on technical advancement and more on frameworks of epistemic justice and digital decoloniality. Keywords: Machine translation, indigenous languages, primary education, cultural relevance, linguistic justice, ethical. Journal: Applied Journal of Digital Innovation Pages: 4 Volume: 1 Year: 2025 Month: January DOI: 10.59282/aj.2025.1.4 File-URL: https://aj.reincisol.com/index.php/aj/article/view/4 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/4/14 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:1:y:2025:id:4 Template-Type: ReDIF-Article 1.0 Author-Name: Rekha Sharma Author-Workplace-Name: Himachal Pradesh University, India Title: Remote monitoring of respiratory diseases using AI that analyzes cough sounds and breathing patterns Publication-Status: Published Abstract: This study developed and evaluated "RespiroGuard", a remote monitoring system based on Artificial Intelligence (AI) that analyzes cough sounds and respiratory patterns for the management of chronic respiratory diseases. The mixed methodology included the development of deep learning algorithms and a prospective validation with COPD patients. The AI models achieved high performance in cough classification (AUC 0.98) and wheeze detection (AUC 0.94). In the clinical study, the system demonstrated significant predictive capacity for exacerbations (AUC 0.82), with a Negative Predictive Value of 96.3%, highlighting its utility as a "reassurance" tool. Qualitative analysis revealed that patients valued the safety and empowerment provided by objective data, but also experienced ambivalence towards constant monitoring, sometimes perceiving it as intrusive. It is concluded that the technology is clinically promising, but its successful implementation requires ethical design that prioritizes user control and seamless integration into clinical workflows, balancing technical accuracy with the human patient experience Keywords: Remote monitoring, artificial intelligence, respiratory sounds, digital biomarker, telemedicine. Journal: Applied Journal of Digital Innovation Pages: 10 Volume: 1 Year: 2025 Month: January DOI: 10.59282/ajdi.10 File-URL: https://aj.reincisol.com/index.php/aj/article/view/10 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/10/17 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:1:y:2025:id:10 Template-Type: ReDIF-Article 1.0 Author-Name: Emily Andrea Basco Bonil Author-Workplace-Name: Andean University of Cusco, Peru Title: Artificial intelligence applied to product design for a circular economy, reuse, and efficient recycling Publication-Status: Published Abstract: This study investigates the application of artificial intelligence (AI) to product design for the circular economy using a sequential mixed method. The qualitative phase, based on interviews and focus groups, identified a "circularity gap" in current processes and key requirements for AI tools: transparent integration, explainability, and human control. The quantitative phase developed and validated "CircularDesign-AI", a prototype assisting design within CAD software. A controlled experiment (n=30) showed that AI-assisted designs significantly improved disassemblability (+62%), material compatibility (+48%), and reduced estimated carbon footprint (-28%), while also cutting design time by 18%. Users reported high usability (SUS=79.2) and perceived utility. It is concluded that AI can overcome practical barriers by operationalizing circularity principles, but its effectiveness depends on data quality, algorithmic transparency, and integration into broader systemic changes in business models and policies. AI emerges as a valuable co-pilot for designers, not a replacement, catalyzing the transition towards regenerative design Keywords: Artificial Intelligence,; Product Design,; Circular Economy; Generative Design,; Disassembly (DfD). Journal: Applied Journal of Digital Innovation Pages: 7 Volume: 1 Year: 2025 Month: January DOI: 10.59282/aj.2025.1.7 File-URL: https://aj.reincisol.com/index.php/aj/article/view/7 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/7/16 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:1:y:2025:id:7 Template-Type: ReDIF-Article 1.0 Author-Name: Angie Juliet Peñuela Pérez Author-Workplace-Name: Universidad Tecnológica Del Perú Title: Creation of AI-powered virtual assistants for monitoring and therapeutic adherence in chronic diseases Publication-Status: Published Abstract: This study evaluated the development and implementation of "GlucoGuide," a virtual assistant with artificial intelligence (VAI), to improve therapeutic adherence and monitoring in patients with type 2 diabetes mellitus (T2DM). Using a convergent mixed-methods design combining a randomized controlled trial (RCT) with qualitative research, the VAI was proven to be an effective tool. The intervention group showed a significant 12.3% improvement in objective pharmacological adherence (measured by MEMS®) and a 0.6% reduction in HbA1c, compared to the control group. However, qualitative analysis revealed that the primary mechanism of action was not the emulation of a human relationship, but the VAI's function as a tool for "cognitive offloading" that structured routines, managed reminders, and provided feedback, facilitating habit formation. Users developed operational trust based on technical reliability, not an emotional connection. The main barriers identified for scaling its implementation were the lack of integration with clinical information systems and the "digital affective gap," where purely instrumental interaction could exacerbate feelings of isolation in some patients. It is concluded that the VAI is an effective augmentation technology for self-care, but its sustainable success requires seamless integration into clinical workflows and design that acknowledges its limits in addressing complex psychosocial needs Keywords: Virtual assistant, artificial intelligence, type 2 diabetes mellitus, digital health, mixed-methods evaluation. Journal: Applied Journal of Digital Innovation Pages: 5 Volume: 1 Year: 2025 Month: January DOI: 10.59282/ajdi.5 File-URL: https://aj.reincisol.com/index.php/aj/article/view/5 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/5/20 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:1:y:2025:id:5 Template-Type: ReDIF-Article 1.0 Author-Name: Nayeli Cecibel Toala Toala Author-Workplace-Name: Border Social Magazine Author-Name: Karen Michelle Catagua Piloso Author-Workplace-Name: REMULCI Magazine Title: Assessment of the educational impact on the integration of AI into teaching with an ethical and critical approach Publication-Status: Published Abstract: This study evaluated the impact of the "Critical AI Teacher" training program on the development of digital teaching competence to integrate artificial intelligence in an ethical and critical manner. Through a quasi-experimental design with a control group (N=62) and mixed methods, knowledge, self-efficacy, behavioral intention, and ethical reasoning were measured before and after the intervention. Quantitative results showed statistically significant improvements with large effect sizes (η²p > .14) in the experimental group compared to the control group across all variables. Qualitative analysis of portfolios and interviews revealed a transformation in teaching practice: teachers evolved from an initial technical fascination toward a critical pedagogical approach, redesigning assessments and using AI as an object of analysis in the classroom. The discussion concludes that this competence is teachable through training focused on guided practice and ethical reflection. However, its sustainable implementation requires institutional support, such as clear policies and time for innovation. The study provides empirical evidence that deliberate training is key for teachers to exercise their essential role as critical guides in the AI era, transforming technological disruption into an opportunity to reaffirm pedagogy centered on human values. Keywords: teacher training; AI in education; digital competence; ethical reasoning; professional development Journal: Applied Journal of Digital Innovation Pages: 9 Volume: 1 Year: 2025 Month: January DOI: 10.59282/ajdi.9 File-URL: https://aj.reincisol.com/index.php/aj/article/view/9 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/9/23 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:1:y:2025:id:9 Template-Type: ReDIF-Article 1.0 Author-Name: Dania Narcisa Petao Salazar Author-Workplace-Name: Revista Reincisol Title: Generative AI in telemedicine: clinical validation, ethics, and implementation to optimize remote diagnosis and adherence. Publication-Status: Published Abstract: This mixed-methods study investigates the implementation of Generative Artificial Intelligence (GenAI) in telemedicine, focusing on optimizing remote diagnosis and therapeutic adherence. Through a systematic review, expert interviews, and two experimental studies, it validates GenAI as an effective "copilot," improving physicians' diagnostic accuracy by 15% and efficiency by 30%, while AI-generated care plans significantly increase patient adherence and understanding. However, the core findings identify three critical and interdependent pillars for responsible adoption: Validation (requiring evidence of net clinical benefit beyond technical accuracy), Ethics (where explainability is revealed as a clinical facilitator, not merely a moral imperative), and Implementation (demanding a human-centric redesign of workflows). The proposed V-E-I Framework is an iterative model integrating these pillars, arguing that success depends on advancing them simultaneously and in balance. The conclusion emphasizes that the future of GenAI in healthcare is not guaranteed by technology alone, but by responsible governance that prioritizes safety, equity, and real clinical utility over mere innovation. Keywords: Generative Artificial Intelligence (GenAI), Telemedicine, Remote Diagnosis, Therapeutic Adherence, Clinical Validation Journal: Applied Journal of Digital Innovation Pages: 6 Volume: 1 Year: 2025 Month: January DOI: 10.59282/ajdi.6 File-URL: https://aj.reincisol.com/index.php/aj/article/view/6 File-Function: Abstract page File-Format: text/html File-URL: https://aj.reincisol.com/index.php/aj/article/download/6/21 File-Function: Full text File-Format: application/pdf Handle: RePEc:ajd:ajdinn:v:1:y:2025:id:6