Education & Technology in Hybrid Formats and Worlds

3-8 October 2021

Deep Learning – Prospectives for Educational Research

Prof. Dr. Daniel Apollon (University of Bergen)

Deep Learning (DL) is a novel approach in Artificial Intelligence to learn from large data sets and extract reusable knowledge. At the heart of DL resides a notion of « learning » that has deep ties to general learning theories in education and adaptative behaviour in biology. DL algorithms are currently implemented in various practical and aesthetic areas, e.g. autopilots, facial recognition, tumor detection, poetry generators, fake videos, and are also gaining popularity large scale survey methods, e.g., sentiment analysis, feature extraction.  However, beyond immediate benefits of DL, to quote Margareth Boden: « Im interested in how computational technology can help us understand human creativity. Many examples of creativity involve learning and exploring in a hierarchical style. Neural and multilayer network systems can help us construct different frameworks to better understand those hierarchies, but theres much more to learn and discover. If you have a computer that comes up with random combinations of musical notes, most of that stuff will be utterly uninteresting rubbish, but some of it will not be. A human being who has sufficient insight and time could well pick up an idea or two. A gifted artist, on the other hand, might hear the same random compilation and come away with a completely novel idea, one that sparks a totally new form of composition. Thats a very different type of creativity. About 95% of what professional artists and scientists do is either exploratory or combinational, and the other 5% is transformational creativity. At the moment, we dont really have a good understanding of these processes. Thats where AI has the potential to play a powerful role

I will outline in this presentation how DL approaches may combine analysis, discovery, and creative transformation of various informational  materials, and stimulate new approaches in educational research. As Deep Learning and related approaches build heavily on multidisciplinary contributions and  as DL equally currently contributes to many disciplines, I will also deal with some challenges that have emerged in  the wake of this ongoing scientific turbulence.

Cited work: Boden, Margaret A, Artificial Intelligence: A Very Short Introduction. Oxford University Press, Oxford, 2018.