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Human-Computer Interaction: Challenges, Opportunities and Emerging Developments, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 1802

Special Issue Editor

Special Issue Information

Dear Colleagues,

Human-computer interaction (HCI) is a multidisciplinary field of research focusing on the design of computer technology and, more specifically, the interaction between humans (users) and computers. Nowadays, HCI is still the subject of considerable interest, gaining the attention of both researchers and practitioners.

The emergence of new technological advancements, including Artificial Intelligence, Extended Reality Mobile Computing, Virtual/Augmented Reality, and the Internet of Things (IoT), have imposed new requirements with respect to the design of information systems (ISs) on the one hand, as well having introduced significant opportunities for ISs to leverage user experiences, on the other.

This Special Issue is dedicated to disseminating high-quality, original research papers, covering the following topics (though not limited to them):

  • Human-computer interaction theory.
  • Multimodal interaction.
  • Natural language interaction.
  • Intelligent user interfaces.
  • User-centered design.
  • Usability and user experience.
  • Cognitive systems.
  • Machine learning methods for user interface design and customization.
  • Mobile computing.
  • Mobile applications design and development.
  • Virtual reality.
  • Augmented reality.
  • Interaction and humans in the Internet of Things.

Dr. Paweł Weichbroth
Guest Editor

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. Applied Sciences 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

  • human–computer interaction
  • usability
  • user experience
  • intelligent user interfaces
  • mobile computing
  • virtual reality
  • augmented reality

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Published Papers (1 paper)

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Research

37 pages, 1529 KiB  
Article
Differences in User Perception of Artificial Intelligence-Driven Chatbots and Traditional Tools in Qualitative Data Analysis
by Boštjan Šumak, Maja Pušnik, Ines Kožuh, Andrej Šorgo and Saša Brdnik
Appl. Sci. 2025, 15(2), 631; https://doi.org/10.3390/app15020631 - 10 Jan 2025
Viewed by 1426
Abstract
Qualitative data analysis (QDA) tools are essential for extracting insights from complex datasets. This study investigates researchers’ perceptions of the usability, user experience (UX), mental workload, trust, task complexity, and emotional impact of three tools: Taguette 1.4.1 (a traditional QDA tool), ChatGPT (GPT-4, [...] Read more.
Qualitative data analysis (QDA) tools are essential for extracting insights from complex datasets. This study investigates researchers’ perceptions of the usability, user experience (UX), mental workload, trust, task complexity, and emotional impact of three tools: Taguette 1.4.1 (a traditional QDA tool), ChatGPT (GPT-4, December 2023 version), and Gemini (formerly Google Bard, December 2023 version). Participants (N = 85), Master’s students from the Faculty of Electrical Engineering and Computer Science with prior experience in UX evaluations and familiarity with AI-based chatbots, performed sentiment analysis and data annotation tasks using these tools, enabling a comparative evaluation. The results show that AI tools were associated with lower cognitive effort and more positive emotional responses compared to Taguette, which caused higher frustration and workload, especially during cognitively demanding tasks. Among the tools, ChatGPT achieved the highest usability score (SUS = 79.03) and was rated positively for emotional engagement. Trust levels varied, with Taguette preferred for task accuracy and ChatGPT rated highest in user confidence. Despite these differences, all tools performed consistently in identifying qualitative patterns. These findings suggest that AI-driven tools can enhance researchers’ experiences in QDA while emphasizing the need to align tool selection with specific tasks and user preferences. Full article
Show Figures

Figure 1

Figure 1
<p>Conceptual model of research.</p>
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<p>Methodology for user evaluation of Taguette, ChatGPT, and Gemini.</p>
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<p>Familiarity and Frequency of use for AI Chatbots.</p>
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<p>Surveys Used in the Experiment, Including Measured Factors and Corresponding Measurement Instruments.</p>
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<p>SUS score interpretation of the tools, interpretation based on [<a href="#B82-applsci-15-00631" class="html-bibr">82</a>].</p>
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<p>Mean values of UEQ-S items per observed tool.</p>
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<p>Benchmarked values of UEQ-S scales per tool.</p>
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<p>TOAST values for Taguette, ChatGPT and Gemini.</p>
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<p>NASA-RTLX values for Taguette, ChatGPT, and Gemini, per tasks.</p>
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<p>NASA-TLX values for Taguette, ChatGPT, and Gemini, per dimension, for Task 1 (<b>a</b>) and Task 2 (<b>b</b>).</p>
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<p>Distribution of the SEQ values, per task per tool.</p>
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<p>Interpretation of mean SEQ values per tool, based on [<a href="#B85-applsci-15-00631" class="html-bibr">85</a>].</p>
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<p>Mean Emotional Affects By Tool And Task.</p>
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<p>Positive and Negative Emotion Scores per Tool And Task.</p>
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