Tailoring Scientific Knowledge: How Generative AI Personalizes Academic Reading Experiences

https://microblogging.infodocs.eu/2025/04/04/tailoring-scientific-knowledge-how-generative-ai-personalizes-academic-reading-experiences/

#AnnaMagorzataKamiska #GenerativeArtificialIntelligence #ScholarlyPublishing #ScientificArticle #ScientificCommunication

The Origins and Veracity of References ‘Cited’ by Generative Artificial Intelligence Applications: Implications for the Quality of Responses

https://microblogging.infodocs.eu/2025/03/13/the-origins-and-veracity-of-references-cited-by-generative-artificial-intelligence-applications-implications-for-the-quality-of-responses/

#ChatGPT #DirkHRSpennemann #GenerativeArtificialIntelligence #ScientificCommunication

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

I’m proud to be the 7th of 47 authors on this excellent new paper, led by the indefatigable Aras Bozkurt and featuring some of the most distinguished contemporary researchers in online, open, mobile, distance, e- and [insert almost any cognate sub-discipline here] learning, as well as a few of us hanging on their coat tails like me.

As the title suggests, it is a manifesto: it makes a series of statements (divided into 15 positive and 20 negative themes) about what is or what should be, and it is underpinned by a firm set of humanist pedagogical and ethical attitudes that are anything but neutral. What makes it interesting to me, though, can mostly be found in the critical insights that accompany each theme, that capture a little of the complexity of the discussions that led to them, and that add a lot of nuance. The research methodology, a modified and super-iterative Delphi design in which all participants are also authors is, I think, an incredibly powerful approach to research in the technology of education (broadly construed) that provides rigour and accountability without succumbing to science-envy.

Notwithstanding the lion’s share of the work of leading, assembling, editing, and submitting the paper being taken on by Aras and Junhong, it was a truly collective effort so I have very little idea about what percentage of it could be described as my work. We were thinking and writing together.  Being a part of that was a fantastic learning experience for many of us, that stretched the limits of what can be done with tracked changes and comments in a Google Doc, with contributions coming in at all times of day and night and just about every timezone, over weeks. The depth and breadth of dialogue was remarkable, as much an organic process of evolution and emergence as intelligent design, and one in which the document itself played a significant participant role. I felt a strong sense of belonging, not so much as part of a community but as part of a connectome.

For me, this epitomizes what learning technologies are all about. It would be difficult if not impossible to do this in an in-person setting: even if the researchers worked together on an online document, the simple fact that they met in person would utterly change the social dynamics, the pacing, and the structure. Indeed, even online, replicating this in a formal institutional context would be very difficult because of the power relationships, assessment requirements, motivational complexities and artificial schedules that formal institutions add to the assembly. This was an online-native way of learning of a sort I aspire to but seldom achieve in my own teaching.

The paper offers a foundational model or framework on which to build or situate further work as well as providing a moderately succinct summary of  a very significant percentage of the issues relating to generative AI and education as they exist today. Even if it only ever gets referred to by each of its 47 authors this will get more citations than most of my papers, but the paper is highly cite-able in its own right, whether you agree with its statements or not. I know I am biased but, if you’re interested in the impacts of generative AI on education, I think it is a must-read.

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

Bozkurt, A., Xiao, J., Farrow, R., Bai, J. Y. H., Nerantzi, C., Moore, S., Dron, J., … Asino, T. I. (2024). The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future. Open Praxis, 16(4), 487–513. https://doi.org/10.55982/openpraxis.16.4.777

Full list of authors:

• 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

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.

#AIInEducation #AIEd #algorithms #chatbots #collectiveWriting #educationalTechnology #genAI #GenerativeArtificialIntelligence #googleDocs #higherEducation #human #interaction #largeLanguageModel #learning #learningTechnology #LLM #manifesto #paper #research #teaching

@romancelandia @bookstodon
"Burn It Down: a reason for (generative) AI resistance"

#GenerativeArtificialIntelligence #GenAI

https://www.insidehighered.com/opinion/views/2024/11/12/burn-it-down-license-ai-resistance-opinion

Global insights: ChatGPT’s influence on academic and research writing, creativity, and plagiarism policies

https://microblogging.infodocs.eu/2024/11/08/global-insights-chatgpts-influence-on-academic-and-research-writing-creativity-and-plagiarism-policies/

#AbdulnaserFakhrou #AcademicWriting #AmjadIslamAmjad #ChatGPT #GenerativeArtificialIntelligence #MuhammadAbidMalik #SarfrazAslam #ScientificCommunication

The impact of generative AI on the scholarly communications of early career researchers: An international, multi-disciplinary study

https://microblogging.infodocs.eu/2024/10/10/the-impact-of-generative-ai-on-the-scholarly-communications-of-early-career-researchers-an-international-multi-disciplinary-study/

#AbdullahAbrizah #AnthonyWatkinson #ArtificialIntelligence #BlancaRodrguezBravo #DavidClark #DavidNicholas #EtiHerman #GenerativeArtificialIntelligence #JieXu #JorgeRevez #Marzenawigon #

How to make sense of generative AI as a science communication researcher? A conceptual framework in the context of critical engagement with scientific information

https://microblogging.infodocs.eu/2024/10/09/how-to-make-sense-of-generative-ai-as-a-science-communication-researcher-a-conceptual-framework-in-the-context-of-critical-engagement-with-scientific-information/

#ArtificialIntelligence #AyeletBaramTsabari #EstherGreussing #EvelynJonas #GenerativeArtificialIntelligence #InbalKleinAvra

10 reasons why AI may be overrated [NPR]

"... [A] growing chorus of naysayers is expressing doubts about how game-changing generative AI will actually be for the economy."

https://www.npr.org/sections/planet-money/2024/08/06/g-s1-15245/10-reasons-why-ai-may-be-overrated-artificial-intelligence

#artificialIntelligence #generativeArtificialIntelligence #AI #generativeAI #largeLanguageModel #LLM #debunked #disappointing #overhyped #technology #NPR

Open Science at the Generative AI Turn: An Exploratory Analysis of Challenges and Opportunities

https://microblogging.infodocs.eu/2024/06/19/open-science-at-the-generative-ai-turn-an-exploratory-analysis-of-challenges-and-opportunities/

#ArtificialIntelligence #GenerativeArtificialIntelligence #KristiLHolmes #MohammadHosseini #OpenScience #SergePJMHorbach #TonyRossHellauer