AI, CRITICAL THINKING, AND KNOWLEDGE-MAKING

The course is designed to explore the epistemological, social, and methodological implications of integrating Artificial Intelligence (AI) into contemporary knowledge-making practices. AI is no longer a peripheral tool but a central actor in the way we generate, assess, and communicate information. This shift invites a critical reconsideration of epistemic authority, the nature of intellectual creativity, and the competences required to navigate a profoundly transformed knowledge ecosystem. Framed around the guiding question — How do we know what we know? — the course engages students in a multidisciplinary investigation into how AI is reshaping not only the content of knowledge but also the cognitive and collaborative processes through which it is constructed. Students will be encouraged to reflect on the evolving relationship between human and machine reasoning, fostering an approach to learning that values critical thinking, epistemic curiosity, and adaptability to the rapidly changing knowledge economy. Particular emphasis will be placed on the development of new cognitive and analytical competences, capable of responding to a evolving epistemic landscape marked by ambiguity, complexity, and the coexistence of divergent perspectives. The course adopts an integrative methodology, combining theoretical discussion with controversial case-based analysis, collaborative projects, and applied experimentation with AI systems. This course treats AI not as a threat to human intelligence but as a new epistemic technology that, like writing and printing before it, can extend and amplify human cognitive capabilities. Students learn to be neither AI skeptics nor uncritical adopters, but thoughtful stewards of human-AI collaborative knowledge creation.

No prerequisites required

Knowledge and understanding: - Develop a nuanced understanding of how AI technologies impact knowledge production, epistemic authority, and critical reasoning. - Acquire a solid interdisciplinary foundation by engaging with key contributions from different disciplines (such as semiotics and philosophy), which offer essential tools for interpreting and critically assessing the current debates surrounding AI. - Reflect on how conceptual tools from the humanities and critical theory can inform and expand the understanding of AI’s role in contemporary epistemic practices, particularly in relation to meaning-making and the framing of knowledge. Applying knowledge and understanding: - Develop the ability to engage critically with AI tools and platforms, applying them not merely as functional aids, but as epistemic partners within research, analysis, and knowledge design processes. - Transfer theoretical insights into applied contexts, using AI to explore complex, real-world scenarios and case studies, while critically reflecting on its epistemological, ethical, and communicative dimensions. - Navigate and analyze hybrid human–machine environments, developing strategies for responsible innovation, context-sensitive use of AI, and collaborative knowledge practices that enhance — rather than replace — human judgement and creativity. Making judgements: - Exercise informed and context-aware judgement in assessing AI-generated content, taking into account issues such as the limits and potentialities of AI systems - Demonstrate an ability to navigate uncertainty and ambiguity in knowledge practices shaped by AI, engaging with competing perspectives and embracing a pluralistic and responsible approach to evaluation and decision-making. Communication skills: - Communicate complex and abstract ideas clearly and effectively across disciplinary boundaries, demonstrating the ability to adapt content, terminology, and conceptual framing to different audiences; - Engage in informed and respectful debate around controversial, unresolved, or ethically sensitive topics related to AI and knowledge, articulating reasoned arguments supported by relevant theoretical frameworks and empirical evidence - Use diverse communicative modalities (written, oral, visual, digital) to convey insights and critical reflections

The course investigates the role of Artificial Intelligence in reshaping knowledge practices across disciplines. Key areas of focus include: - Knowledge design/ knowledge-making - AI Generative Critical Thinking - Critically assessment and evaluation of AI-generated content - Epistemic authority, epistemic trust, and intellectual wellness - Learnability and adaptive mindsets in evolving environments - Ethical implications of algorithmic systems - Interdisciplinary applications across fields, grounded in real-world case studies - Theoretical frameworks and critical perspectives on Human-AI Knowledge Collaboration

ATTENDING STUDENTS Slides are part of the course's program BOOK: - PAOLO GRANATA, 2025. Generative Knowledge. Think, Learn, Create with AI (Chapters 1; 3;5;8); - Vallverdú, J. (2025). Role-Based Prompting. In: Prompting Causal Events. SpringerBriefs in Intelligent Systems. Springer, Cham (Chapters 1;3;4) PAPER: - Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5-14; - Kang, O., & Hirschi, K. (2025). Bias and stereotyping: Human and artificial intelligence (AI). Annual Review of Applied Linguistics, 1-16; - Maeda, T., & Quan-Haase, A. (2024, June). When human-AI interactions become parasocial: Agency and anthropomorphism in affective design. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 1068-1077); - Floridi, L. (2024). The Ethics of Artificial Intelligence: exacerbated problems, renewed problems, unprecedented problems-Introduction to the Special Issue of the American Philosophical Quarterly dedicated to The Ethics of AI. Available at SSRN. NON ATTENDING STUDENTS Non-attending students are required to study ALL the materials covered by attending students and, in addition, two supplementary papers: - Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanities and Social Sciences Communications, 12(1), 1-21. - Luciano, F. (2024). Hypersuasion–On AI’s persuasive power and how to deal with it. Philosophy & Technology, 37(2), 64.

Lectures Classroom discussions Project works

Attendance will be monitored through five roll calls, conducted during randomly selected classes throughout the semester. Students who are absent from more than two roll calls will be automatically classified as non-attendant students. Please note that attendance status determines the applicable assessment method For attending students: - 40% project work (in groups) - 60% written exam (paper-based). 1 hour, 3 questions regarding Part 1+ Part 2 + Course project For non attending students: - 100% written exam (paper based) 75 minutes, 4 questions regarding Part 1 + Part 2 + Supplementary paper 1 + Supplementary paper 2.

Strong interest in the topics reviewed.