ENGAGING LEARNERS THROUGH EMOTION IN ARTIFICIALLY INTELLIGENT ENVIRONMENTS

T. Mehigan, I. Pitt

University College Cork (IRELAND)
Consideration of Extensive Academic Emotion (EAE) when developing Artificially Intelligent Education (AIEd) models can potentially improve overall student engagement and success. While there exists an extensive body of research literature in the area of Intelligent Tutoring Systems (ITS), little advancement has been made in extending ITS to facilitate improved student experience and learning through a reduction of negative EAE. MAPLE (Mobile Adaptive Personalised Learning Environment) (Mehigan & Pitt, 2013), an ITS model, is designed for use with any eLearning or mLearning platform to facilitate the intelligent detection of user learning-styles based on two dimensions of the Felder-Silverman Learning Style Model (FSLSM). Based on user interaction, automatic provision of adapted content display to suit the learning style needs of the user is facilitated.

EAE combines both academic emotion and, emotion and affect created through Human Computer Interaction (HCI). Where EAE is negative it can result in reduced student motivation, interaction and consequently a poor learning outcome. Emotions can significantly influence student learning and achievement (Villavicencio & Bernard, 2013). The inclusion of enjoyable and interesting activities for example, can lead to positive emotions and therefore increased student engagement (Frenzel, Pekrun & Goetz, 2007). Learner effort can also be influenced through personalisation, contemplation of cognitive variables etc.. HCI can encompass emotional affect and require extensive cognitive processing on the part of a system user. HCI can also determine motivation sentiment (Brave & Nass, 2000). Thus, the relationship between academic and HCI emotions should be considered simultaneously in the development of an AIEd-based ITS. Cognitive variables, content and learning outcome and context should be considered alongside EAE to achieve optimal AIEds. The application of these combined variables could lead to the development of improved mediated models that overcome negative EAE during the learning experience, for improved student achievement.

This paper outlines a brief history of the convergence of the AI and eLearning systems, with a particular focus on ITS. The MAPLE model is assessed in light of its strengths and weaknesses for achieving optimal learner engagement. MAPLE is compared to a descriptive and simplified educational technology driven by AIEd (Luckin et al 2016). This technology comprises core models which aims to support the social, emotional, and meta-cognitive aspects of learning. Appraisal of MAPLE as to an AIEd driven model could inform the extension of existing ITS to achieve optimal mediated AIEd to meet the emotional needs of the learner.