Session Contents and Structure:

November 6, 2026 — Asynchronous Session (2 hours)

Pre-Course Reading: Participants will read selected articles and case studies on AI in higher education to get ready for the in-person sessions.

November 10, 2026 — In-person Session (3 hours) 10:00 AM – 1:00 PM

• Introduction to AI and its Conceptual Framework: Understanding the foundations of AI and its role in higher education.
• Key Principles for Responsible AI Use: Explore the core principles of fairness, transparency, accountability, and privacy in AI applications.
• Essential European Guidelines on AI Ethics: A comprehensive overview of the most important AI ethics frameworks, including the Artificial Intelligence Act, UNESCO’s 2021 Recommendation on AI Ethics, Trustworthy AI Guidelines, the AI Competency Frameworks for Educators and Students (2024), and INTEF’s Guidelines on AI in Education.
• Exploring Ethical Risks and Limitations of AI: What constitutes AI-related plagiarism? Analysing the limitations of AI tools in educational contexts, examining potential biases, over-reliance, and the implications for critical thinking and originality.

November 12, 2026 — In-person Session (3 hours) 10:00 AM – 1:00 PM

• Best Practices for Implementing AI in the Classroom: Examples of how AI can facilitate student learning with a focus on collaborative projects and co-creation with AI tools.
• Useful AI tools for faculty (Vidnoz, Natural Reader, DeepSeek, Poe, etc.) and AI tool directories (AIfindy, AllthingsAI, There is an AI for that, and ToolifyAI) and how they can support classroom activities.
• Lesson plan design using Bloom’s Taxonomy.

November 17, 2026 — In-person Session (4 hours) 10:00 AM – 2:00 PM

Presentation of a Practical Case Using AI in the Classroom:

Participants will present a creative activity they have implemented or plan to implement using AI, providing a clear overview of its objectives, methods, and expected outcomes. The presentation should also include a reflection on how the ethical use of AI is ensured, addressing potential risks such as AIgiarism, bias, and over-reliance on technology.

Duration: 8 minutes per presentation, followed by peer feedback. Each participant will be assigned a fellow faculty member as commentator to provide constructive feedback on their presentation, followed by a group reflection.

A template will be provided to guide participants in constructively co-evaluating their colleagues’ work. This peer feedback, along with input from the facilitator, will help refine and improve their approach to integrating AI into teaching.

Methodology

Hands-on Exploration: Participants will collaboratively explore their current knowledge of AI and share examples of how they are integrating AI tools into their teaching practice.

Group Activity: Through case studies and discussions, participants will analyze ethical risks and practical scenarios involving AI misuse, with a particular focus on challenges like AIgiarism.

Group Feedback: Participants will exchange insights and provide peer feedback to help each other refine their understanding and improve the ethical use of AI in education.

This activity is part of the actions of the Ibero-American Network on Digital Ethics and Educational AI (REDIE, CYTED) (626RT0201) (https://go.uv.es/2bBnV8e) and the PIEE project ref. 3898146 (“The influence of generative AI on the discursive production of university students”) of the Vice-Rectorate for Lifelong Learning, Teaching Transformation and Employment at the University of Valencia. It is also part of the actions of the IA-FIP Project, funded by MICIU/AEI/10.13039/501100011033 and FEDER “A way of making Europe” (ref. PID2022-141031NB-I00); the AI-UpskillED Project (ref. 2025-1-ES01-KA220-HED-000355590) aimed at strengthen teachers' abilities to critically, ethically, and effectively integrate artificial intelligence (AI) into teaching; the research groups TeLL-ME (Teaching and Learning Languages in Multilingual Education), GRIAC (Academic and Scientific Integrity) and the Red-IA.

• Understand the ethical frameworks and principles guiding responsible AI use in higher education, including fairness, transparency, accountability, and privacy.
• Identify and critically assess risks and challenges related to the use of AI in the classroom, such as AIgiarism, bias, over-reliance on AI tools, and impacts on academic integrity.
• Apply ethical principles to real classroom contexts, designing or adapting lesson plans that integrate AI tools in a pedagogically sound and responsible way.
• Collaborate with peers to analyze scenarios and provide constructive feedback on the ethical and practical integration of AI in education.
• Reflect on their own teaching practices and how AI can be used to support student learning while promoting creativity, originality, and critical thinking.