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The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future
Anadolu University, Türkiye.
KTH, School of Industrial Engineering and Management (ITM), Learning, Digital Learning.ORCID iD: 0000-0002-9984-6561
Carnegie Mellon University, United States.
Number of Authors: 472024 (English)In: Open Praxis, ISSN 1369-9997, E-ISSN 2304-070X, Vol. 16, no 4, p. 487-513Article in journal (Refereed) Published
Abstract [en]

This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that GenAI is not ideologically and culturally neutral. Instead, it reflects worldviews that can reinforce existing biases and marginalize diverse voices. Furthermore, as the use of GenAI reshapes education, it risks eroding essential human elements— creativity, critical thinking, and empathy—and could displace meaningful human interactions with algorithmic solutions. This manifesto calls for robust, evidence-based research and conscious decision-making to ensure that GenAI enhances, rather than diminishes, human agency and ethical responsibility in education.

Place, publisher, year, edition, pages
International Council for Open and Distance Education , 2024. Vol. 16, no 4, p. 487-513
Keywords [en]
AI in education, AIEd, algorithms, chatbots, collective writing, educational technology, GenAI, Generative artificial intelligence, higher education, human-GenAI interaction, large language models, learning, LLMs, manifesto, teaching
National Category
Pedagogy
Identifiers
URN: urn:nbn:se:kth:diva-357942DOI: 10.55982/openpraxis.16.4.777ISI: 001377298600002Scopus ID: 2-s2.0-85211218756OAI: oai:DiVA.org:kth-357942DiVA, id: diva2:1922649
Note

QC 20241219

Available from: 2024-12-19 Created: 2024-12-19 Last updated: 2025-01-28Bibliographically approved

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Hrastinski, Stefan

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