Track Co-Chairs
Michael Sheng, Macquarie University
Kasuni Weerasinghe, Auckland University of Technology
Adnan Mahmood, Macquarie University
Johnny Chann, University of Auckland
Track Description
Affective computing involves the development of emotion-aware information systems that are capable of recognising, interpreting, processing, and simulating human emotions (Kratzwald et al., 2018). This interdisciplinary field combines computer science, engineering, psychology, cognitive science, education and sociology to endow machines with emotional intelligence, enabling more natural and intuitive human-computer interactions (Daily et al., 2017). The integration of generative AI (GenAI) into affective computing could represent a significant advancement, offering innovative approaches to the design, implementation, and application of impactful emotionally intelligent solutions (Amin et al., 2024; Han et al., 2023). This emerging fusion has the potential to disrupt traditional paradigms, leading to more adaptive, responsive, and personalised user experiences.
Affective computing plays a major role in enhancing human-computer interactions, making systems more intuitive, empathetic, and effective in various applications. Recent research includes emotionally aware systems to assist in monitoring mental health and well-being (Yang et al., 2022), systems that adapt to a student’s emotional state to motivate them and improve their learning performance (Lin et al., 2014), and developing more immersive games through the identification of the player’s emotional state (Yannakakis, 2012). Beyond these practical benefits, research in this field also addresses important ethical considerations, ensuring that as our technology becomes more emotionally intelligent, it does so in an ethically responsible and privacy-conscious manner (Booth et al., 2021).
We seek to attract a cadre of research that both delineates and critiques the concepts, methods, frameworks, architectures, functionalities, and broader implications of applying and integrating GenAI in the design and development of emotion-aware information systems. The scope of this track includes, but is not limited to, the following key areas:
- Emotionally Intelligent Conversational Agents: Investigating the development of GenAIdriven chatbots and virtual assistants capable of understanding and responding to user emotions, thereby enhancing engagement and satisfaction.
- Emotion-Aware Content Generation: Exploring how GenAI can create personalised content, such as text, music, or art, that aligns with users’ emotional states or preferences, providing more tailored and impactful experiences.
- Adaptive Learning Systems: Examining the application of GenAI in educational technologies that adjust instructional content and strategies based on learners’ emotional responses and engagement levels, thereby improving learning outcomes.
- Healthcare Support Tools: Assessing the use of GenAI in developing emotion-sensitive applications for mental health support, such as virtual therapists or monitoring systems that can detect emotional distress and provide appropriate interventions.
- Emotion-Aware Real-Time Voice Interactions: Exploring the development of GenAIpowered systems that facilitate real-time conversations with natural, emotionally expressive voices, creating immersive and persuasive interactions.
- Emotion-Aware Gaming Experiences: Investigating how GenAI can be utilised to create adaptive gaming environments that respond to players’ emotional states, enhancing immersion and personalisation.
- Emotion-Driven Human-Robot Interaction: Exploring the role of GenAI in enabling robots to perceive and appropriately respond to human emotions, improving collaboration and cohabitation.
- Emotion-Sensitive Marketing Strategies: Analysing how GenAI can tailor marketing content to consumers’ emotional states, potentially increasing engagement and conversion rates.
- Para-Social Relationships Enabled by GenAI: Examining the emergence of one-sided emotional bonds between users and GenAI-powered systems, such as virtual companions or influencers, and their psychological and social implications.
- Emotion Tracking in Social Media: Investigating how GenAI can monitor and analyse emotional expressions across social media platforms, providing insights into public sentiment and informing strategies for content creation, user engagement, and mental health interventions.
- Impact Assessment of Emotion-Aware Systems: Evaluating the impact and effectiveness of GenAI-enhanced emotion-aware systems in various applications, and assessing their performance compared to traditional methods.
- Ethical and Societal Implications: Analysing the ethical considerations, potential biases, and societal impacts of deploying GenAI in emotion-aware systems.
This track aspires to be a platform for rigorous scholarly inquiry into the multifaceted applications of GenAI in affective computing, emphasising both the innovative potential and the consequential ethical, legal, and operational challenges.