1. Introduction

Human-Computer Interaction (HCI) is a multidisciplinary field that focuses on the design, evaluation, and implementation of interactive computing systems intended for human use. It emphasizes understanding how people interact with computers, aiming to create systems that are useful, efficient, and enjoyable. The core principles of HCI include usability, accessibility, and user-centered design, ensuring that systems are intuitive and meet users’ diverse needs.

In the era of artificial intelligence (AI), Natural Language Processing (NLP) is driving a transformative evolution in HCI, reshaping how humans interact with machines. NLP, a rapidly advancing field at the intersection of computer science and linguistics, aims to enable machines to understand, interpret, and respond to human language. Progress in NLP is breaking down barriers in HCI, allowing machines to more effectively comprehend and generate human language, paving the way for seamless communication between humans and computers [1]. This has opened new technological frontiers, with applications spanning healthcare, marketing, and entertainment, where NLP is used for tasks such as disease monitoring, sentiment analysis, and content recommendation, making it an indispensable tool across industries [2][3]. Consequently, this review aims to discuss how NLP enhances HCI by improving communication between users and machines, enabling personalized user experiences, and optimizing system feedback. By examining the applications of NLP in chatbots, voice assistants, and sentiment analysis, researchers can identify current technological limitations and propose innovative solutions, thus advancing the field. This review will focus on the application of NLP in HCI, analyze the major challenges faced in this area, and identify potential avenues for future research.

2. The Integration of Natural Language Processing and Human-Computer Interaction

HCI is increasingly integrating with artificial intelligence, with foundational technologies in NLP playing a significant role. Emerging in the early 1980s, HCI has undergone several waves of development, each focusing on different aspects and methodologies. The first wave concentrated on modeling human cognition within work environments, while the second emphasized real-life contexts and user-centered design. The third wave introduced the concept of user experience, highlighting emotional engagement and the everyday use of computers [4]. As technological advancements usher in the era of AI, particularly through the development of NLP technologies, HCI is experiencing a fourth wave of transformation. This wave emphasizes natural communication between humans and machines, enabling users to interact with computers in a more intuitive manner through language. This transformation not only enhances user engagement but also allows machines to better understand context and emotions, leading to more personalized responses.

Recent advancements in NLP have significantly improved communication between humans and robots, paving the way for more usable and personalized designs in HCI. These developments have been driven by deep learning and neural networks, resulting in substantial enhancements in applications such as voice assistants, chatbots, and sentiment analysis. Recent progress in deep learning, particularly through the use of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), has greatly improved the performance of NLP systems, enabling state-of-the-art results in tasks like sentiment analysis and machine translation [5]. Models like BERT and GPT-4 leverage large volumes of text data to learn complex patterns, allowing them to perform tasks such as language translation, sentiment analysis, and even generating images from textual descriptions. Large Language Models (LLMs) demonstrate capabilities beyond traditional NLP tasks, such as performing arithmetic operations and generating coherent narratives, showcasing their potential as tools for general artificial intelligence [6]. These significant technological breakthroughs enable systems to more effectively understand and respond to user needs, providing context-aware and intuitive interfaces [7][8], thereby profoundly transforming the design of human-computer interaction.

3. Examples of NLP Applications in Human-Computer Interaction

3.1. User Interface Design

In the realm of NLP, user interface design has also undergone significant evolution, finding wide-ranging applications. These improvements are evident not only in visual layouts but also in the enhancement of voice input, personalized experiences, and optimized feedback mechanisms, all contributing to an improved overall user experience.

3.1.1. Voice Input. Advancements in voice input technology have enabled users to interact with devices through natural language, eliminating the limitations of traditional input methods. Interfaces utilizing speech recognition technology allow users to control devices with verbal commands, simplifying the operational process. This makes interactions more intuitive and user-friendly, as users can communicate in their own language rather than relying on rigid command structures [9]. Advanced NLP models, such as ChatGPT, further enhance user engagement by delivering authentic and contextually relevant responses—crucial for applications in customer service, education, and entertainment [10]. This seamless interaction method also improves usability. In language translation, for example, these systems can adapt to the needs of users from different linguistic backgrounds by employing reinforcement learning and NLP techniques, thereby offering a smooth translation experience [11]. As the accuracy of speech recognition improves, users can more confidently execute tasks using voice commands, such as sending messages, setting reminders, or controlling smart home devices.

3.1.2. Personalized Experiences. Personalized experiences represent another significant application of NLP in user interface design. By analyzing users' historical behaviors and preferences, systems can offer tailored content and suggestions, thereby enhancing user satisfaction. For instance, recommendation systems utilize NLP techniques to analyze user feedback and interactions, generating personalized product recommendations, which are particularly prevalent on e-commerce platforms. In the healthcare sector, research is underway to develop personalized treatment pathways, especially in the field of radiation oncology. By converting unstructured medical data into structured formats, NLP can facilitate personalized patient care, improve communication, and support predictive modeling [12]. Moreover, personalized conversational experiences enable users to interact with chatbots or voice assistants in a more natural manner, as the systems can remember user preferences and make appropriate adjustments in subsequent interactions. Personalized models are better equipped to capture subjective nuances in text, making them more effective than non-personalized models—especially for applications like chatbots and recommendation systems [13]. Fine-tuning LLMs for tasks such as personalized displays, sentiment recognition, and hate speech detection has led to significant improvements. By combining sentiment analysis with deep learning algorithms, chatbots can predict user intent and provide useful responses, even when dealing with sarcasm or ambiguous messages, thereby maintaining user satisfaction during complex interactions [14].

3.1.3. Optimized Feedback Mechanisms. Real-time feedback mechanisms provide users with immediate responses and suggestions during system interaction, enhancing the overall experience while offering valuable data for further system optimization. In the business sector, companies leverage NLP to analyze customer feedback and social media posts, extracting insights on customer sentiment and emerging trends, which helps in understanding customer needs and improving service delivery [15]. In healthcare, Electronic Health Records (EHRs) are utilized to extract relevant information from clinical records, enhancing patient care and streamlining workflows [16]. Similarly, NLP is employed to strengthen the monitoring of aneurysms, identify gender differences in screening, and utilize existing EHR data to improve patient prognosis [17]. In the context of myeloproliferative neoplasms, for instance, NLP pipelines have been used to assess cardiovascular risk by analyzing a large volume of clinical documents. This approach provides near real-time updates on clinical events, aiding in patient management and risk prediction [18]. These applications demonstrate the robust capabilities of NLP in analyzing timely feedback, offering significant support in improving the quality and efficiency of healthcare services, as well as providing data analytics and decision support.

3.2. Chatbots and Virtual Assistants

Technologies such as chatbots and virtual assistants play a crucial role in enhancing user experience by providing more natural and intuitive interactions. These systems leverage language technologies to understand user queries and deliver relevant responses, thereby improving usability and accessibility [9].

3.2.1. The Fundamental NLP Techniques for Chatbots and Virtual Assistants. The design of intelligent chatbots and virtual assistants is rooted in conversational artificial intelligence, which employs NLP techniques to enable machines to understand and respond to human language. Conversational systems are central to HCI and comprise Natural Language Understanding (NLU), Dialogue Management (DM), and Natural Language Generation (NLG). The effectiveness of such systems relies heavily on the ability of NLU to accurately interpret user input in terms of intent and sentiment. NLU involves complex processes such as intent detection, entity extraction, and sentiment analysis, all of which are crucial to creating responsive and user-friendly interfaces.

In intent detection, Jayanth et al. achieved an impressive accuracy of 98.57% on the ATIS dataset by utilizing the XLM-Roberta model, which leverages contextual information and identifies subtle patterns [19]. Moreover, the joint intent detection and slot-filling method has proven invaluable for applications such as voice assistants, where joint models enhance accuracy by simultaneously detecting intents and filling slots. Recent advancements emphasize making these models inherently interpretable without compromising accuracy [20].

In sentiment analysis, recent models utilize deep learning techniques, such as bidirectional Long Short-Term Memory (biLSTM) networks and enhanced multi-head self-attention mechanisms, to improve sentiment analysis accuracy. These models outperform baseline models by effectively capturing inter-sentence information and employing advanced pre-trained structures like BERT [21]. Additionally, sentiment analysis is applied in function calling, where acoustic and linguistic features are used to build frameworks for speech emotion recognition, aiding in understanding emotional patterns in multi-party communication [22]. The latest advancements in NLU harness complex models and techniques to enhance accuracy and interpretability, addressing challenges such as ambiguity and multi-intent parsing, thereby enriching the interactive experience of chatbots and virtual assistants.

3.2.2. Applications of Chatbots and Virtual Assistants. Through NLP technologies, chatbots and voice assistants can better understand user needs, thereby providing more natural responses, making these systems increasingly common in daily life. Intelligent customer service systems represent a significant application of chatbots and voice assistants, with NLP-driven chatbots widely used to simulate human-like interactions and automatically respond to customer inquiries. These systems leverage machine learning models, such as retrieval learning and sequence learning, to comprehend and generate human language, thereby enhancing the efficiency of customer support services [23]. Their applications span various domains, including education, healthcare, mental health support, and programming, showcasing their versatility and potential to transform traditional practices.

In healthcare and mental health support, chatbots like ChatGPT are increasingly being utilized to perform tasks such as clinical record management and patient interactions. They offer potential benefits to low- and middle-income countries by improving health literacy and providing telehealth support [24]. Chatbots are also employed for emotional support, helping users express emotions such as sadness and depression. Research indicates that users are more willing to share emotional vulnerabilities with chatbots than with social media, highlighting their potential in mental health interventions [25]. In the engineering and construction field, the GPT-supported assistant DAVE facilitates real-time interaction with Building Information Modeling (BIM), allowing users to update and query models using text or voice commands. This reduces the complexity and technical barriers of construction projects, demonstrating the application of chatbots in engineering [26]. In the business sector, chatbots and voice assistants are widely used for customer service, providing instant responses to user inquiries, thereby enhancing customer satisfaction and reducing the workload of human agents [27][28]. Research by Alia et al. found that NLP helps understand customer needs and preferences by analyzing interactions on platforms such as WhatsApp. This enables companies to provide more relevant services and allows sales teams to respond more quickly and accurately, improving overall customer satisfaction [29]. In the travel and tourism industry, chatbots have been integrated into flight booking systems to streamline operations and enhance the customer experience. The travel agency Saffron Vacations is an example as it has successfully implemented chatbots to improve operational efficiency and customer satisfaction [30].

Therefore, it is evident that NLP has become a cornerstone in the development of intelligent customer service systems, offering a range of applications that enhance customer interactions, improve service efficiency, and reduce operational costs. By leveraging NLP, companies can automate responses, analyze customer sentiments, and enhance system efficiency, thereby increasing customer satisfaction and loyalty.

4. Technical Challenges and Future Developments

With the continuous advancement of NLP technologies, the field of HCI faces a series of technical challenges. Beyond concerns such as data quality, model bias, and system interpretability, ethical and security challenges also emerge. Any system that integrates NLP technology must address challenges related to data quality, system integration, and other technical hurdles to ensure the successful implementation of NLP-based functionalities [31]. Equally important is the creation of ethical, secure, and reliable human-computer interaction designs that can earn user trust. To ensure user trust and safety, developers must consider security and ethical factors during the design process and take appropriate measures to mitigate potential risks. Research has shown that a lack of empathy in chatbot interactions can negatively affect user satisfaction, but combining empathic accuracy with effective communication methods can help alleviate these impacts. Therefore, ensuring that chatbots can accurately interpret user intent and provide reliable responses is crucial for maintaining high satisfaction levels; misunderstandings can lead to user frustration and dissatisfaction [32]. The widespread use of AI agents also necessitates discussions around ethical implications, such as algorithmic discrimination and the lack of transparent accountability for AI outputs. These issues underscore the need for regulatory frameworks to ensure responsible AI deployment [33].

Balancing technological advancements with ethical considerations is crucial in the future research of NLP and HCI to ensure that user experience is enhanced while maintaining safety and fairness. On the technical front, future research should focus on improving the adaptability and flexibility of models to better meet the needs of diverse user groups. Additionally, future human-computer interactions must prioritize user privacy to genuinely gain user trust. Establishing a robust framework to ensure the transparency and accountability of NLP applications is essential for enhancing trust between users and stakeholders, requiring collaborative efforts from researchers across various fields.

In brief, the future development of NLP will rely on interdisciplinary collaboration, integrating knowledge from fields such as computer science, psychology, and ethics to create human-computer interaction products that are not only intelligent in functionality but also trustworthy in ethical standards. This approach will enable researchers to ensure that technological advancements align with social responsibility, thereby promoting the sustainable development of human-computer interaction.

5. Conclusion

Integrating NLP into HCI marks a transformative advancement that significantly enhances communication between users and machines. This review elucidates the myriad applications of NLP in HCI, with a particular focus on its role in improving user experience and developing intelligent question-answering systems. By employing various NLP techniques such as sentiment analysis, speech recognition, and language generation, this paper emphasizes how these technologies facilitate faster response times and user-friendly interfaces. However, implementing NLP in HCI is not without its challenges. Issues related to data quality, model selection, and system interpretability remain prevalent. Moreover, ethical considerations surrounding privacy and the responsible use of NLP systems underscore the necessity for ethical AI practices. Future research should focus on interdisciplinary collaboration to overcome these barriers and enhance the applicability of NLP in real-world contexts. Ultimately, the ongoing evolution of NLP in HCI is poised to create more intuitive, personalized, and ethical user experiences, paving the way for seamless and enriching human-computer interactions in the future. As technology continues to advance, the potential of NLP will expand further, driving the development of human-computer interaction to a deeper level of intelligence.