Teaching Methodologies
The methodology to be adopted should be based on the capacity for autonomous and collaborative production, through theoretical-practical
classes with an expository and interactive component; practical classes with AI-based digital tools; analysis of case studies; preparation of
projects or didactic sequences with AI integration; group and individual work and discussion and critical reflection sessions.
In this sense, the methodology relies on the contribution of students in the individual and collective learning process, guided by the teacher.
Learning Results
1. Know the fundamental concepts of Artificial Intelligence (AI) and their application in educational contexts
2. Identify the opportunities and limits of AI in pedagogical practice with a focus on the integral development of children aged 3 to 12
3. Develop a critical, ethical and humanistic attitude towards the use of AI-based technologies in school environments
4. Promote critical and creative thinking through the use of AI with pedagogical intentions
5. Selecting, evaluating and using tools with AI components appropriate to the contexts of pre-school education and primary and secondary
education
6. Understand the implications of AI for the role of the educator/teacher and the mediation of learning
Program
1. Artificial Intelligence in everyday life and education: practical examples and current trends
2. Fundamentals of Artificial Intelligence and its application in the educational context
3. Personalizing teaching and learning through Artificial Intelligence: challenges and opportunities
4. Artificial Intelligence in School Management and Administration
5. Ethics, privacy and security in the use of Artificial Intelligence in the school environment
6. The role of the educator in the critical and creative use of Artificial Intelligence
Grading Methods
- - Exam - 100.0%
- - Individual and/or Group Work - 60.0%
- - Attendance and Participation - 40.0%
Internship(s)
NAO
Bibliography
Cope, B., Kalantzis, M., & Searsmith, D. (2020). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning
ecologies. Educational Philosophy and Theory , 53 (12), 1229–1245. https://doi.org/10.1080/00131857.2020.1728732.
Holmes, W., & Porayska-Pomta, K. (2022). The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates. New York:
Routledge. https://doi:10.4324/9780429329067
Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. London: UCL IOE Press.
Mintz, J., Holmes, W., Liu, L., & Perez-Ortiz, M. (2023). Artificial Intelligence and K-12 Education: Possibilities, Pedagogies and Risks.
Computers in the Schools, 40 (4), 325–333. https://doi.org/10.1080/07380569.2023.2279870
Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Cambridge: Polity Press.
UNESCO (2021). AI and Education: Guidance for Policy-makers.