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How is the credibility of educational research evidence perceived?

Studying public perceptions of Artificial Intelligence is challenging because AI has such a broad definition for so many people.  For AI to be adopted effectively, more work is required from stakeholders to create a shared understanding of its abilities and limits and to communicate that to the public.  The following research shows a hierarchy of public perceptions to research evidence, and suggests researchers and developers need to do more to recover the public image of AI and ensure that research and news of the field is communicated honestly and accurately.

Abstract

Artificial Intelligence (AI) is attracting a great deal of attention and it is important to investigate the public perceptions of AI and their impact on the perceived credibility of research evidence. In the literature, there is evidence that people overweight research evidence when framed in neuroscience findings.

 

In this paper, we present the findings of the first investigation of the impact of an AI frame on the perceived credibility of educational research evidence. In an experimental study, we allocated 605 participants including educators to one of three conditions in which the same educational research evidence was framed within one of: AI, neuroscience, or educational psychology. The results demonstrate that when educational research evidence is framed within AI research, it is considered as less credible in comparison to when it is framed instead within neuroscience or educational psychology. The effect is still evident when the subjects’ familiarity with the framing discipline is controlled for. Furthermore, our results indicate that the general public perceives AI to be: less helpful in assisting us to understand how children learn, lacking in adherence to scientific methods, and to be less prestigious compared to neuroscience and educational psychology.

 

Considering the increased use of AI technologies in Educational settings, we argue that there should be significant attempts to recover the public image of AI being less scientifically robust and less prestigious than educational psychology and neuroscience. We conclude the article suggesting that AI in Education community should attempt to be more actively engaged with key stakeholders of AI and Education to help mitigate such effects.

About the Authors:

Dr Mutlu Cukurova

Mutlu is an academic faculty member at University College London and has a particular interest in researching the potential of emerging Educational Technologies such as Artificial Intelligence and Learning Analytics to continuously evaluate and support human development. In addition to this, Mutlu works with UNESCO’s international expert group on ICT in Education. He is Director of Research at EDUCATE and sits in the working group of UCL’s Grand Challenges on Transformative Technologies.


Mutlu's work is interdisciplinary and encompasses research in learning sciences, psychology, computer science, and Human-Computer Interaction. For more, visit his profile here.

Professor Rose Luckin

Cited as the ‘Dr. Who of AI’, Rose is Director of EDUCATE, and Professor of Learner Centred Design at UCL Knowledge Lab. She was named as one of 20 most influential people in Education on the Seldon List 2017, and her research involves the design and evaluation of EdTech using theories from the learning sciences and techniques from Artificial Intelligence.

 

A prolific editor and author, Rose’s 2018 book: ‘Machine Learning and Human Intelligence: The Future of Education for the 21st Century’ describes how we can benefit from Artificial Intelligence to support teaching and learning, and how futureproofing both involves revising what and how we teach and learn right now.

 

Co-Founder of the Institute for Ethical Artificial Intelligence in Education and President of the International Society for AI in Education, Rose is also a pickler and confectioner of garden produce, and will often be found running, baking, recommending the Ang Lee canon, and changing education for the better.

Dr Carmel Kent

Carmel is the head of data science at EDUCATE and a senior research fellow at UCL. She is a computational social scientist, with a research focus on Artificial Intelligence for education and learning analytics.

Her other areas of interest focus on online learning communities, interactivity in online discussions and collaborative learning technologies.  She has 20 years of industry and academic experience, having worked as a software engineer, data scientist, entrepreneur, teacher and researcher, and for IBM research, EdTech and healthcare providers’ companies and a number of startups.

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