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Human+ Tech Talks | Learning to Learn: How Can AI Unlock Learning Patterns?

Tuesday, 22 November 2022, 1 – 2pm

Human+ Tech Talks | Learning to Learn: How Can AI Unlock Learning Patterns?

An online roundtable discussion hosted by Dr Qian Xiao as part of the Human+ Tech Talks series.

Online education has become a mainstream form of education in many countries during the pandemic. The market for AI-enhanced personalized tutoring systems is therefore growing in great demand and popularity. This allows recording rich information of learner's interactions, thus opening up the possibilities of studying 'learner's behavior at scale using machine learning techniques. In this seminar, we will share the challenges and opportunities when building personalized online education systems. We'll also showcase tangible examples of how we can leverage deep neural networks and large volumes of behavioral data to tackle these challenges.

Our discussion will cover the following topics:

  • What value has personalization brought for education?
  • What does that look like as an AI-enhanced personalized education system in the future?
  • How to compare learner's performance in the long-run?
  • Cognition gaps between human understanding and neural networks.
  • Discovering user behavior patterns that have potential value for pedagogical practices.

Speakers: Dr Qian Xiao, Human+ Programme Fellow; Prof Vincent Wade, Chair of Computer Science (1990), Trinity College Dublin; Dr Keith Johnston, Assistant Professor, Education, Trinity College Dublin.

Please indicate if you have any access requirements, such as ISL/English interpreting, so that we can facilitate you in attending this event. Contact:

Register here.

Campus LocationTrinity Long Room Hub Arts and Humanities Research Institute
Accessibility: Yes
Room: Galbraith Seminar Room & Online
Research Theme: Digital Humanities
Event Category: Arts and Culture, Lectures and Seminars
Type of Event: One-time event
Audience: Researchers, Faculty & Staff
Cost: Free but registration is essential

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