Abstract
Make no mistake AI is learning. Many authors have documented that ‘people’ are already learning in different ways, but seem to underestimate the ability of generative AI to solve problems in unexpected ways. The research project is framed by an interdepartmental collaboration is led by English studies and Telecommunications and deals with speech analysis and machine learning. This project explored the effects of online platforms and attention. The present study focuses on beta brain waves using bioelectrical measurements (electroencephalogram, EEG), as these signals can correlate to attention in subjects when listening to a second language. The objective was to see if online discourse can cause the same attention response as live face-to-face speech and then use the results for machine learning. The student engineers worked closely with undergraduate and postgraduate students in English studies who oversaw the mapping of the texts and identifying stimuli for the research design. The students effectively created an interface between natural language stimuli and machine coding, so that the data could be interpreted statistically and verified as viable for machine learning and text interpretation. This reinforced the connection between natural language and technology with the emerging research. This student led project has allowed us visualize key correlations between EEG data and linguistic features as related to emotion and attention. How to define what happens in the brain as related to attention and emotion has far-reaching influence on teaching, on communication, as well as in training artificial intelligence using human data.
Presenters
Mary GriffithPhD Lecturer, Didactics of Language, the Arts and Sport, Universidad de Málaga, Málaga, Spain
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Design Thinking, Attention, Cognitive Discourse, Machine Learning, Higher Education
