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New Trends in Intelligent User Interfaces and Human-Computer Interactions with...
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New Trends in Intelligent User Interfaces and Human-Computer Interactions with Large Language Models

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 2571

Special Issue Editors

Dr. Yiqing Shen

E-Mail Website
Guest Editor
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Interests: deep learning; AI; medical imaging
Dr. Yuguang Wang

E-Mail Website
Guest Editor
Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: artificial intelligence; computational mathematics; statistics and data science

Special Issue Information

Dear Colleagues,

The rapid advancement of artificial intelligence (AI) has brought significant innovations in various domains, with large language models (LLMs) standing out as transformative technologies. The proposed Special Issue, titled "New Trends in Intelligent User Interfaces and Human–Computer Interactions with Large Language Models", aims to explore the evolving landscape of intelligent user interfaces (IUIs) and human–computer interactions (HCIs) driven by the capabilities of LLMs. 

This Special Issue will concentrate on the integration of LLMs within IUI and HCI frameworks, focusing on understanding how these models enhance user interaction, improve user experience, and redefine the boundaries of machine intelligence in human-centered applications. It will delve into both theoretical and practical aspects, examining the potential and challenges of using LLMs in creating more intuitive, responsive, and adaptive interfaces.

The scope of this Special Issue encompasses a wide range of topics, including but not limited to the following:

  • Design and development of IUIs powered by LLMs;
  • Enhancements in natural language understanding and generation for HCI;
  • User experience and usability studies involving LLM-based interfaces;
  • Ethical considerations and responsible AI usage in IUIs;
  • Case studies and real-world applications of LLMs in various domains such as healthcare, education, and entertainment;
  • Technical challenges and solutions in integrating LLMs with existing HCI systems;
  • Future directions and emerging trends in the intersection of LLMs and HCI.

The primary purpose of this Special Issue is to bridge the gap between AI research and practical applications in HCI. By bringing together contributions from researchers, practitioners, and industry experts, we aim to foster a comprehensive understanding of how LLMs can be leveraged to create more intelligent and user-friendly interfaces. This Issue seeks to provide valuable insights, propose innovative methodologies, and highlight the implications of LLMs for future HCI developments.

While there has been substantial research on IUIs and HCI separately, the integration of LLMs into these fields is relatively new and rapidly evolving. The existing literature has predominantly focused on the foundational aspects of LLMs and their capabilities in natural language processing (NLP). This Special Issue will build upon this foundation by specifically addressing the intersection of LLMs with user interfaces and human interaction. It will supplement current research by providing empirical studies, design frameworks, and practical applications that showcase the tangible benefits and potential challenges of using LLMs for enhancing user interfaces.

Dr. Yiqing Shen
Dr. Yuguang Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent user interfaces (IUIs)
  • human–computer interaction (HCI)
  • large language models (LLMs)
  • natural language processing (NLP)
  • user experience (UX)
  • adaptive interfaces
  • responsible AI
  • ethical AI
  • usability studies
  • AI in healthcare
  • AI in education
  • real-world applications

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 1669 KiB  
Article
Empowering Education with Intelligent Systems: Exploring Large Language Models and the NAO Robot for Information Retrieval
by Nikos Fragakis, Georgios Trichopoulos and George Caridakis
Electronics 2025, 14(6), 1210; https://doi.org/10.3390/electronics14061210 - 19 Mar 2025
Cited by 1 | Viewed by 407
Abstract
To unlock more aspects of human cognitive structuring, human–AI and human–robot interactions require increasingly advanced communication skills on both the human and robot sides. This paper compares three methods of retrieving cultural heritage information in primary school education: search engines, large language models [...] Read more.
To unlock more aspects of human cognitive structuring, human–AI and human–robot interactions require increasingly advanced communication skills on both the human and robot sides. This paper compares three methods of retrieving cultural heritage information in primary school education: search engines, large language models (LLMs), and the NAO humanoid robot, which serves as a facilitator with programmed answering capabilities for convergent questions. Human–robot interaction has become a critical aspect of modern education, with robots like the NAO providing new opportunities for engaging and personalized learning experiences. The NAO, with its anthropomorphic design and ability to interact with students, presents a unique approach to fostering deeper connections with educational content, particularly in the context of cultural heritage. The paper includes an introduction, extensive literature review, methodology, research results from student questionnaires, and conclusions. The findings highlight the potential of intelligent and embodied technologies for enhancing knowledge retrieval and engagement, demonstrating the NAO’s ability to adapt to student needs and facilitate more dynamic learning interactions. Full article
(This article belongs to the Special Issue New Trends in Intelligent User Interfaces and Human-Computer Interactions with Large Language Models)
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12 pages, 7654 KiB  
Article
Memorizing Swin-Transformer Denoising Network for Diffusion Model
by Jindou Chen and Yiqing Shen
Electronics 2024, 13(20), 4050; https://doi.org/10.3390/electronics13204050 - 15 Oct 2024
Viewed by 1671
Abstract
Diffusion models have garnered significant attention in the field of image generation. However, existing denoising architectures, such as U-Net, face limitations in capturing the global context, while Vision Transformers (ViTs) may struggle with local receptive fields. To address these challenges, we propose a [...] Read more.
Diffusion models have garnered significant attention in the field of image generation. However, existing denoising architectures, such as U-Net, face limitations in capturing the global context, while Vision Transformers (ViTs) may struggle with local receptive fields. To address these challenges, we propose a novel Swin-Transformer-based denoising network architecture that leverages the strengths of both U-Net and ViT. Moreover, our approach integrates the k-Nearest Neighbor (kNN) based memorizing attention module into the Swin-Transformer, enabling it to effectively harness crucial contextual information from feature maps and enhance its representational capacity. Finally, we introduce an innovative hierarchical time stream embedding scheme that optimizes the incorporation of temporal cues during the denoising process. This method surpasses basic approaches like simple addition or concatenation of fixed time embeddings, facilitating a more effective fusion of temporal information. Extensive experiments conducted on four benchmark datasets demonstrate the superior performance of our proposed model compared to U-Net and ViT as denoising networks. Our model outperforms baselines on the CRC-VAL-HE-7K and CelebA datasets, achieving improved FID scores of 14.39 and 4.96, respectively, and even surpassing DiT and UViT under our experiment setting. The Memorizing Swin-Transformer architecture, coupled with the hierarchical time stream embedding, sets a new state-of-the-art in denoising diffusion models for image generation. Full article
(This article belongs to the Special Issue New Trends in Intelligent User Interfaces and Human-Computer Interactions with Large Language Models)
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