The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future

Манифест преподавания и обучения в эпоху генеративного искусственного интеллекта: критическая коллективная позиция для более эффективной навигации в будущем
Ramesh C. Sharma, Olaf Zawacki‐Richter, Melissa Bond, Mutlu Cukurova, Helen Crompton, Thomas K. F. Chiu, Isak Froumin, Aras Bozkurt, Robert Farrow, Chrissi Nerantzi, Maha Bali, Jon Dron, Eamon Costello, Christian M. Stracke, Kyungmee Lee, Mark Nichols, Stefan Hrastinski, Petar Jandrić, Junhong Xiao, Angelica Pazurek, Tutaleni I. Asino, Ahmed Tlili, Charles B. Hodges, Stephanie Moore, Apostolos Koutropoulos, Bryan Alexander, Manuel B. Garcia, Lenandlar Singh, Steven Watson, Henk Huijser, John Y. H. Bai, Andrew Swindell, Mike Sharples, Josep M. Duart, Chryssa Themelis, Evgeniy Terentev, Alexander M. Sidorkin, Dorothy Mulligan, Sarah Honeychurch, Patricia J. Slagter van Tryon, Jing Leng, Kai Zhang, Chanjin Zheng, Peter Shea, Anton Vorochkov, Sunagül Sani-Bozkurt, Robert L. Moore
2024-01-01

Generative AIalgorithmic biasethical responsibilityhigher educationhuman agency
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.
1
Although GenAI may personalize learning, improve efficiency, and expand access, it raises challenges for human agency, equity, and meaningful learning.
2
GenAI should be used to enhance, rather than diminish, human agency and authentic educational experiences.
3
Generative AI in higher education is not ideologically or culturally neutral; its designs embed worldviews, values, and biases.
4
Maintaining human oversight and ethical responsibility requires robust, evidence-based research and deliberate decision-making about GenAI integration.
5
The increasing use of GenAI risks weakening creativity, critical thinking, empathy, and authentic human interaction by replacing them with algorithmic solutions.

The integration of Generative AI (GenAI), chatbots, and algorithms into higher education

The implications for human agency, equity, bias, values, and meaningful human-centered teaching and learning

Publication Details
Publication Date
2024-01-01
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Authors
Ramesh C. Sharma
Olaf Zawacki‐Richter
Melissa Bond
Mutlu Cukurova
Helen Crompton
Thomas K. F. Chiu
Isak Froumin
Aras Bozkurt
Robert Farrow
Chrissi Nerantzi
Maha Bali
Jon Dron
Eamon Costello
Christian M. Stracke
Kyungmee Lee
Mark Nichols
Stefan Hrastinski
Petar Jandrić
Junhong Xiao
Angelica Pazurek
Tutaleni I. Asino
Ahmed Tlili
Charles B. Hodges
Stephanie Moore
Apostolos Koutropoulos
Bryan Alexander
Manuel B. Garcia
Lenandlar Singh
Steven Watson
Henk Huijser
John Y. H. Bai
Andrew Swindell
Mike Sharples
Josep M. Duart
Chryssa Themelis
Evgeniy Terentev
Alexander M. Sidorkin
Dorothy Mulligan
Sarah Honeychurch
Patricia J. Slagter van Tryon
Jing Leng
Kai Zhang
Chanjin Zheng
Peter Shea
Anton Vorochkov
Sunagül Sani-Bozkurt
Robert L. Moore
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