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Artificial Intelligence in Digital Education and Training

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Base Knowledge

Not applicable

Teaching Methodologies

The UC adopts a learning methodology based on project and experimentation (“learning lab methodology”), combining moments of theoretical exposition, demonstration, guided practice and critical reflection. The online sessions are interactive in nature, promoting peer-to-peer collaboration and the co-construction of knowledge.

Continuous evaluation:

  • Participation in the sessions and involvement in the laboratory dynamics: 20%
  • Final project – educational prototype developed in the AI Learning Lab and reflective report: 80%

Approval criteria: final grade ≥ 10 points.

The evaluation favors experimentation, creativity, the capacity for pedagogical integration and ethical reflection on the use of AI in education.

Learning Results

This course aims to provide students with a practical and reflective experience of educational innovation, through the creation of an Artificial Intelligence Learning Lab (AI Learning Lab).

The course aims to develop skills to design, implement and evaluate pedagogical practices supported by AI tools, exploring their application in the personalization of learning, automation of teaching tasks, analysis of educational data and development of digital skills.

Participants will be challenged to create prototyping of educational experiences with AI, integrating technical, ethical and pedagogical dimensions.

Program

  1. Artificial Intelligence in Digital Education and Training

Fundamental concepts, international panorama and impact on pedagogical practice.

  1. AI Learning Lab Design

Design, objectives, collaborative dynamics and experimentation tools.

  1. Exploring educational AI tools and environments

Chatbots, intelligent tutors, content generators, adaptive platforms, and analytics.

  1. Pedagogical application of AI

Personalization of learning, automated formative assessment and tutoring support.

  1. Prototyping teaching and learning experiences with AI

Development and testing of microprojects in the laboratory.

  1. Ethics, literacy, and critical thinking about AI in education

Privacy, algorithmic bias, self-regulation, and responsible use.

  1. Presentation and reflection of the results of the Learning Lab.

Curricular Unit Teachers

Professor a definir - ISCAC

Grading Methods

Summative assessment
  • - Project - 80.0%
  • - Attendance and Participation - 20.0%

Internship(s)

NAO

Bibliography

  • Holmes, W., Bialik, M. & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

  • Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL IOE Press.

  • Popenici, S. & Kerr, S. (2017). Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education. Research and Practice in Technology Enhanced Learning.

  • UNESCO (2023). Guidance for Generative AI in Education and Research. Paris: UNESCO.

  • Holmes, W. & Tuomi, I. (2022). AI and the Future of Skills, Education and Learning. OECD Publishing.

  • Ferrão, M. (2023). Inteligência Artificial Aplicada ao Ensino. Edições Sílabo.

  • Recursos digitais: OpenAI for Education, Google for Education AI Tools, Microsoft Copilot in Education, UNESCO AI Ethics Portal, Edutopia AI Classroom Resources.