THEMATIC AREAS

• Implementation of generative AI as a support tool for teaching and instructional design.

• Development of teachers’ digital competencies related to artificial intelligence.

• Responsible and ethical use of AI tools in the classroom.

• Design of learning activities in which AI complements learning processes, tutoring, or feedback.

• Project-Based Learning (PBL), Challenge-Based Learning, case studies, flipped learning, role-playing, among others.

• Design of teaching experiences that promote critical thinking, creativity, and autonomous learning.

• Evaluation of learning outcomes and analysis of impact on student motivation.

• Integration of technology to enhance active participation.

• Design of gamified learning experiences and their impact on student motivation.

• Use of simulators, virtual reality (VR), and augmented reality (AR) to explain complex content.

• Integration of AI elements into gamified experiences.

• Pedagogical evaluation of immersive learning activities.

• Redesign of assessment strategies in response to the expansion of generative AI tools.

• Promotion of academic integrity and prevention of misuse.

• Critical reflection on bias, privacy, and data security in educational contexts.

• Analysis of institutional policies and teaching best practices.

• Use of technology to enhance teaching coordination, student monitoring, and academic management.

• Decision-support systems in educational institutions.

• Innovation in professor–student communication processes.

• Institutional faculty training in digital competencies.

• Integration of the Sustainable Development Goals (SDGs) into courses through digital tools.

• Awareness-raising activities, social responsibility, and technological ethics.

• Development of interdisciplinary projects linked to sustainability.

• Digital materials and resources to promote informed digital citizenship.

• Teaching activities that foster entrepreneurial skills, innovation, and problem-solving.

• Challenges and project-based learning integrated with digital tools.

• Simulation of professional contexts through AI or digital platforms.

• Design of projects applied to real or simulated companies.

• Use of data (participation, activities, grades, attendance, analytics) to inform teaching decisions.

• Design of strategies for monitoring student progress.

• Critical reflection on the benefits and limitations of learning analytics.

• Applied studies on retention, academic performance, and learning patterns.

• Design of new approaches to assessing knowledge, competencies, and performance.

• Authentic assessment, peer assessment, and self-assessment.

• Digital rubrics, automated feedback, and hybrid assessment systems.

• Continuous assessment in face-to-face, hybrid, and virtual environments.

• Strategies to address diversity in digital or hybrid classrooms.

• Universal Design for Learning (UDL) supported by technological tools.

• Accessible tools for students with diverse learning paces or specific needs.

• Evaluation of the impact of inclusion initiatives.

• Changes in professional skills and competencies driven by AI and their impact on education.

• Artificial intelligence as a catalyst for educational and labour transformation in the 21st century.

• AI redefining professional competencies: is education prepared for this shift?

• The impact of artificial intelligence on employability and the need for lifelong learning.

• Education and the labour market: how to balance human-centred education in the face of automation?

• Soft skills and digital competencies: the emerging dual foundation for employability in the age of AI.

• Emerging proposals exploring novel teaching and learning formats.

• Interdisciplinary innovations.

• Pilot experiences currently in experimental phases.

• Studies or reflections on the future of education.