This paper examines the pedagogical impact of Artificial Intelligence-assisted tools in a university software construction course. Through a 10-week case study with 25 engineering students, the study captures qualitative insights into how students interact with AI tools, the challenges they face, and the learning strategies that emerge. While students reported perceived time savings, the core contribution lies in understanding the educational implications, both positive and challenging of integrating AI into project-based learning environments. The findings reveal that Artificial Intelligence tools enhance prototyping and debugging skills but require structured integration to mitigate over-reliance. The study contributes a framework for balancing Artificial Intelligence use with traditional pedagogy, supporting Sustainable Development Goals 4’s goal of equitable education, while the opinion survey carried out with students shows a result of 96% satisfaction with the use of assisted artificial intelligence tools during the development of the project during the studies.

Building upon foundational Item Response Theory (IRT) research conducted at Tecnologico de Monterrey with University of São Paulo (USP), this study presents CONF.i, a framework integrating Canvas LMS with a three-variable IRT model (Grade-Confidence-Performance) and Google's Gemini AI. Using design-based research methodology, an external Google Apps Script application was developed, interfacing with Canvas LTI standards, implementing IRT-based assessment with student confidence ratings and AI-generated personalized feedback and learning resource recommendations. Pilot testing with twenty-three undergraduate students at Tecnologico de Monterrey, Mexico, with theoretical validation from USP collaborators, demonstrated technical feasibility and pedagogical value. Results revealed that 82% of students rated the interface positively, 87% understood the confidence rating mechanism, and 91% would recommend the approach. The three-variable model revealed four learning patterns within the pilot sample that would be invisible to traditional scoring: aligned mastery (34.8%), underconfident competence (21.7%), overconfident struggle (26.1%), and aligned struggle (17.4%). These observed patterns suggest potential for enabling targeted instructional interventions, warranting further investigation with larger samples. This Brazil-Mexico collaboration demonstrates that sophisticated educational technologies can be integrated within existing institutional infrastructure without commercial licensing costs, contributing to Sustainable Development Goal #4 (Quality Education) by making adaptive learning technologies more accessible through mainstream platforms.

The increasing adoption of generative artificial intelligence (AI) in higher education has created new opportunities to enhance Learning Management Systems (LMS) with personalized feedback, adaptive assessment, and learning analytics. Despite these advances, many LMS platforms remain primarily focused on content delivery and grade management, with limited support for metacognitive assessment and intelligent feedback. This study presents CONF.i, a confidence-informed assessment and AI feedback framework integrated with Canvas LMS using Google Apps Script and Google Gemini AI. Developed through a design-based research approach, the framework combines traditional assessment scores with student self-reported confidence levels to support personalized formative feedback and diagnostic learning insights. The proposed system integrates Canvas LTI standards, a Google Apps Script backend, and Gemini AI services to automate scoring, confidence tracking, and AI-generated educational feedback within existing institutional infrastructure. A prototype implementation was evaluated using simulated learner profiles representing different combinations of performance and confidence patterns. The framework identified four illustrative assessment profiles: aligned mastery, underconfident competence, overconfident struggle, and aligned struggle. These patterns demonstrate how confidence-informed assessment can reveal metacognitive dimensions of learning that are not visible through conventional grading alone. Preliminary usability observations indicated positive perceptions regarding the integration within the familiar Canvas environment and the relevance of AI-generated feedback, while also identifying limitations related to response latency and feedback specificity. The findings suggest that integrating confidence-informed assessment with generative AI may support more personalized and reflective learning experiences without requiring major institutional infrastructure changes or commercial licensing costs. This study contributes an exploratory prototype framework for AI-enhanced formative assessment in higher education and provides a practical model for institutions seeking to extend existing LMS platforms with confidence-aware analytics and personalized feedback capabilities.

The excessive cost and complexity of Industry 4.0 laboratory infrastructure limit the adoption of Digital Twin concepts in engineering education. This paper proposes a low-cost Digital Twin framework for sustainable manufacturing education integrating SAP NetWeaver, Node-RED, and AI-based decision support. The framework adopts a layered architecture that connects PLC-based simulation, IoT middleware, enterprise resource planning systems, and intelligent decision-making components. Node-RED enables real-time data exchange, while SAP NetWeaver provides enterprise-level integration through OData services. An AI module supports decision-making for production and inventory management. The framework has been validated through the implementation of a functional prototype and a series of end-to-end integration tests that evaluated communication reliability, system interoperability, API response performance, and AI-assisted decision-support capabilities. Competency-based mapping aligns the framework with Industry 4.0 engineering skills, supporting its use in academic environments. A sustainability assessment highlights reductions in infrastructure cost, energy consumption, and resource usage compared to traditional laboratory approaches. The results indicate that the framework has the potential to provide a scalable and accessible solution for teaching Digital Twin concepts, pending further classroom-based validation.

Project-Based Learning (PBL) has become an important pedagogical strategy for developing technical and professional competencies in engineering education through authentic, multidisciplinary experiences. This paper presents a PBL case study conducted in an undergraduate Computer Engineering course in which students designed and implemented Smart Fisio Borregos, an Internet of Things (IoT) and Artificial Intelligence (AI) system designed as both a learning vehicle for students and a prototype tool intended to support rehabilitation training through real-time exercise guidance and monitoring; the educational effectiveness of the rehabilitation-support function has not yet been formally validated. The project followed a prototype-driven methodology that integrated low-cost sensors, embedded systems, cloud databases, computer vision, and AI services into a unified platform. The resulting prototype incorporated equipment occupancy monitoring, environmental control, RFID-based access management, a web dashboard for data visualization, and a computer-vision module based on MediaPipe Pose Landmarker for exercise analysis and feedback. The project provided students with opportunities to apply knowledge from programming, embedded systems, databases, networking, and AI while developing collaboration, problem-solving, and project-management skills. The paper describes the pedagogical framework, system architecture, implementation process, and project outcomes, illustrating how multidisciplinary engineering projects can be used to create authentic learning experiences connected to real-world challenges. The proposed approach offers a replicable model for integrating IoT and AI technologies into engineering curricula while contributing to educational innovation related to health and well-being. The study aligns with Sustainable Development Goal 3 (Good Health and Well-Being) and Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure).