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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

  • Aras Bozkurt
  • , Junhong Xiao
  • , Robert Farrow
  • , John Y.H. Bai
  • , Chrissi Nerantzi
  • , Stephanie Moore
  • , Jon Dron
  • , Christian M. Stracke
  • , Lenandlar Singh
  • , Helen Crompton
  • , Apostolos Koutropoulos
  • , Evgenii Terentev
  • , Angelica Pazurek
  • , Mark Nichols
  • , Alexander M. Sidorkin
  • , Eamon Costello
  • , Steven Watson
  • , Dónal Mulligan
  • , Sarah Honeychurch
  • , Charles B. Hodges
  • Mike Sharples, Andrew Swindell, Isak Frumin, Ahmed Tlili, Patricia J.Slagter VAN TRYON, Melissa Bond, Maha Bali, Jing Leng, Kai Zhang, Mutlu Cukurova, Thomas K.F. Chiu, Kyungmee Lee, Stefan Hrastinski, Manuel B. Garcia, Ramesh Chander Sharma, Bryan Alexander, Olaf Zawacki-Richter, Henk Huijser, Petar Jandrić, Chanjin Zheng, Peter Shea, Josep M. Duart, Chryssa Themeli, Anton Vorochkov, Sunagül Sani-Bozkurt, Robert L. Moore, Tutaleni Iita Asino
  • Anadolu University
  • Open University of Shantou
  • Open University Milton Keynes
  • University of Oldenburg
  • University of Leeds
  • University of New Mexico
  • Athabasca University
  • University of Bonn
  • University of Guyana
  • Old Dominion University
  • University of Massachusetts Boston
  • HSE University
  • University of Minnesota Twin Cities
  • Open Polytechnic
  • California State University
  • Dublin City University
  • University of Cambridge
  • University of Glasgow
  • Asian University for Women
  • Constructor University
  • Beijing Normal University
  • East Carolina University
  • University College London
  • University of Stavanger
  • American University in Cairo
  • East China Normal University
  • The Chinese University of Hong Kong
  • Seoul National University
  • KTH Royal Institute of Technology
  • FEU Institute of Technology
  • Graphic Era Hill University
  • Georgetown University
  • Queensland University of Technology
  • Zagreb University of Applied Sciences
  • State University of New York at Albany
  • Universitat Oberta de Catalunya
  • Norwegian University of Science and Technology
  • Autonomous University of Madrid
  • University of Florida
  • Carnegie Mellon University

Research output: Contribution to journalArticlepeer-review

155 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)487-513
Number of pages27
JournalOpen Praxis
Volume16
Issue number4
DOIs
StatePublished - 2024

Scopus Subject Areas

  • Education
  • Library and Information Sciences

Keywords

  • AI in education
  • AIEd
  • GenAI
  • Generative artificial intelligence
  • LLMs
  • algorithms
  • chatbots
  • collective writing
  • educational technology
  • higher education
  • human-GenAI interaction
  • large language models
  • learning
  • manifesto
  • teaching

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