Design for individual emotion regulations from a designer’s perspective

Abstract

Since the outbreak of coronavirus disease (COVID-19) in late 2019, there has been growing concerns on mental health including emotional health in young people such as anxiety, depression, and stress. In the post-pandemic era, the far-reaching implications still affect individual’s emotional and mental health. Positive emotions are beneficial for well-being and contribute to longevity enhancement. A good balance of negative and positive emotions is key to mental health and life satisfaction. Thus, this research aims to explore how to design for individual emotion regulations via digital intervention tools and materials among university students with stressed negative emotion problems. The key research questions in designing for individual emotion regulations include, (1) what are the key factors that affect individual’s emotion regulation, (2) how can common emotion regulation strategies be adapted into design strategies to guide the creation of a digital emotion regulation tool, and (3) how can a personalised and context-aware emotion regulation tool be designed and evaluated.

To address the above research questions, I first applied the literature review method to identify the key factors of individual emotion regulation, including the Big Five personality traits, emotion regulation strategies, and their relationship. Then, I conducted a pilot study and semi-structured interviews, along with a design-led focus group, to test the implementation of human-machine cooperation in designing an individual emotion regulation framework. This is followed by proposing a new strategy to design a personalised and context-aware promptor for generative AI to generate unlimited adaptable emotion intervention content. Finally, I prototyped ‘EmoMate’ and conducted evaluation experiments to assess the proposed promptor framework design and implementation, gaining insights into their applications.

The proposed research contributes to knowledge through the following advancements: 1) understanding and identifying the key factors that affect individual emotion regulation, 2) understanding and concluding the common emotion regulation strategies can be adapted into design strategies to guide the creation of digital emotion regulation tools and a novel design framework for human-machine cooperation, and 3) developing a novel strategy for personalised and context-aware promptor design, prototyping and evaluating an AI-enabled emotion regulation system with a case study.

Date of Award	30 Apr 2025
Original language	English
Awarding Institution	Northumbria University
Supervisor	Sheng-feng Qin (Supervisor) & Matteo Conti (Supervisor)