Emotionally Intelligent Machines in Education: Harnessing Generative AI for Authentic Human-Machine Synergy in the Classroom

Nicu Ahmadi; Lance Leon Allen White; Tracy Anne Hammond

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Multidisciplinary Engineering Division (MULTI) Technical Session 10
Tagged Division: Multidisciplinary Engineering Division (MULTI)
Permanent URL: https://peer.asee.org/47236

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Paper Authors

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Nicu Ahmadi Texas A&M University

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Nicu (Nikki) Ahmadi is a graduate research assistant researcher working under Dr. Tracy Hammond. She holds a B.S. in Computer Engineering , a M.S. in Electrical Engineering – RF Communication & DSP. She is currently working on her PhD in Interdisciplinary Engineering, and her research focuses on the intersection of human-machine interaction, and behavioral economy; specifically covering adaptive emotional systems in human-machine interactions.

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Lance Leon Allen White Texas A&M University orcid.org/0000-0002-1172-0500

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Lance White is a Ph.D. student at Texas A&M University in Interdisciplinary Engineering with a thrust in Engineering Education. He is working as a graduate research assistant at the Institute of Engineering Education and Innovation at the Texas Engineerin

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Tracy Anne Hammond Texas A&M University orcid.org/0000-0001-7272-0507

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Dr. Hammond is Director of the Texas A&M University Institute for Engineering Education & Innovation and also the chair of the Engineering Education Faculty. She is also Director of the Sketch Recognition Lab and Professor in the Department of Computer Science & Engineering. She is a member of the Center for Population and Aging, the Center for Remote Health Technologies & Systems as well as the Institute for Data Science. Hammond is a PI for over 13 million in funded research, from NSF, DARPA, Google, Microsoft, and others. Hammond holds a Ph.D. in Computer Science and FTO (Finance Technology Option) from the Massachusetts Institute of Technology, and four degrees from Columbia University: an M.S in Anthropology, an M.S. in Computer Science, a B.A. in Mathematics, and a B.S. in Applied Mathematics and Physics. Hammond advised 17 UG theses, 29 MS theses, and 10 Ph.D. dissertations. Hammond is the 2020 recipient of the TEES Faculty Fellows Award and the 2011 recipient of the Charles H. Barclay, Jr. '45 Faculty Fellow Award. Hammond has been featured on the Discovery Channel and other news sources. Hammond is dedicated to diversity and equity, which is reflected in her publications, research, teaching, service, and mentoring. More at http://srl.tamu.edu and http://ieei.tamu.edu.

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Abstract

This paper delves into the realm of Generative AI focused on Artificial Emotional Intelligence (AEI) to enhance cooperative and genuine human-machine interplay. It underscores the imperative of assimilating AEI in diverse sectors including education and usage of it in classrooms.

With the surge of interest in Generative AI, the quest to equip machines with emotional comprehension has accelerated, aiming for machines that can seamlessly interact with humans. Contemporary AI, while advanced, falls short in grasping emotions and discerning social cues, limiting their aptitude for genuine human connection. These social cues encompass verbal and non-verbal gestures, such as facial nuances, voice modulations, and body language, employed by humans to transmit emotions and thoughts.

Deploying AEI machines that can adeptly maneuver the intricacies of human sentiments presents many challenges. One is the development of machine learning models proficient in detecting and decoding human emotions with precision. Emotions, by nature, are intricate and heavily contextual. Machines need to ascertain these sentiments analogous to human processing, incorporating contextual, verbal, and non-verbal cues. Moreover, the task of designing natural language algorithms for exact sentiment analysis is large. Sentiment interpretation is intricate due to the inherent vagueness of language and its reliance on varied contexts, from situational to cultural nuances. Despite the challenges, lies the promising prospect of revolutionizing human-machine engagement. Machines adept in emotional recognition can pave the way for more organic and fulfilling human-AI interactions.

This preliminary exploration sheds light on the technical adversities and potential in AEI development, while weighing its repercussions on human-machine dynamics. It sets the stage for future AEI research, emphasizing the significance of interdisciplinary studies to bring in a truly human-centric and accountable AI paradigm. The research question at hand is: Can Generative AI, enriched by cross-disciplinary insights, take an intuitive leap to discern human emotions, driving us towards a more empathetic and ethical AI future?

Citation
Format

Ahmadi, N., & White, L. L. A., & Hammond, T. A. (2024, June), Emotionally Intelligent Machines in Education: Harnessing Generative AI for Authentic Human-Machine Synergy in the Classroom Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47236

TY - CPAPER
AB - This paper delves into the realm of Generative AI focused on Artificial Emotional Intelligence
(AEI) to enhance cooperative and genuine human-machine interplay. It underscores the imperative of assimilating AEI in diverse sectors including education and usage of it in classrooms.

With the surge of interest in Generative AI, the quest to equip machines with emotional comprehension has accelerated, aiming for machines that can seamlessly interact with humans. Contemporary AI, while advanced, falls short in grasping emotions and discerning social cues, limiting their aptitude for genuine human connection. These social cues encompass verbal and non-verbal gestures, such as facial nuances, voice modulations, and body language, employed by humans to transmit emotions and thoughts.

Deploying AEI machines that can adeptly maneuver the intricacies of human sentiments presents many challenges. One is the development of machine learning models proficient in detecting and decoding human emotions with precision. Emotions, by nature, are intricate and heavily contextual. Machines need to ascertain these sentiments analogous to human processing, incorporating contextual, verbal, and non-verbal cues. Moreover, the task of designing natural language algorithms for exact sentiment analysis is large. Sentiment interpretation is intricate due to the inherent vagueness of language and its reliance on varied contexts, from situational to cultural nuances. Despite the challenges, lies the promising prospect of revolutionizing human-machine engagement. Machines adept in emotional recognition can pave the way for more organic and fulfilling human-AI interactions.

This preliminary exploration sheds light on the technical adversities and potential in AEI development, while weighing its repercussions on human-machine dynamics. It sets the stage for future AEI research, emphasizing the significance of interdisciplinary studies to bring in a truly human-centric and accountable AI paradigm. The research question at hand is: Can Generative AI, enriched by cross-disciplinary insights, take an intuitive leap to discern human emotions, driving us towards a more empathetic and ethical AI future?
AU - Nicu Ahmadi
AU - Lance Leon Allen White
AU - Tracy Anne Hammond
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Emotionally Intelligent Machines in Education: Harnessing Generative AI for Authentic Human-Machine Synergy in the Classroom
UR - https://peer.asee.org/47236
ER -