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Lookup NU author(s): Dr Lei ShiORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
© 2026 Published by Elsevier B.V.Large Language Models (LLMs) augmented emotion research is emerging as a pivotal hotspot in the fields of affective computing and human–computer interaction. However, existing studies focus predominantly on practical applications, lacking systematic reviews from a design perspective. This paper systematically synthesises 72 LLMs-assisted emotion studies and 83 traditional Affective Design (AD) publications, constructing a reusable Emo-LLMs corpus. We clarify how LLMs reshape the four core steps of AD, namely scenario and task analysis, emotion modelling, emotion mapping, and evaluation and iteration, and distil them into a theoretical workflow termed Semantic Closed-Loop Rapid Co-creation (SCRC) for organising diverse patterns of AD iteration. Our survey reveals three primary capability enhancements afforded by LLMs: semantic distillation, plug-and-play emotion perception, and real-time self-supervised evaluation. The LLM-centric design methodologies and analytical frameworks proposed herein provide theoretical underpinnings and practical references for the sustained evolution of Affective Design.
Author(s): Lu J, Shi L, Liu Y, Lyu R, Yang G
Publication type: Review
Publication status: Published
Journal: Displays
Year: 2026
Volume: 94
Print publication date: 01/09/2026
Online publication date: 07/04/2026
Acceptance date: 14/03/2026
ISSN (print): 0141-9382
ISSN (electronic): 1872-7387
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.displa.2026.103435
DOI: 10.1016/j.displa.2026.103435
Data Access Statement: Data will be made available on request
