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Engaging muscle groups in proper order is an essential skill for human motor tasks such as welding, contributing to musculoskeletal health and task performance. However, learning the correct muscle engagement strategy for a dedicated human motion task like welding can be challenging due to the difficulty of acquiring the implicit information and carrying it over to novice trainees, which is becoming the bottleneck of workforce training. This paper proposes to monitor and model the implicit muscle engagement strategies using surface electromyographic (sEMG) sensors and provide real-time feedback to novice trainees via vibrotactile devices according to the muscle engagement models. The differences between expert trainers' and novice trainees' muscle engagement strategies are processed to control the vibrotactile patterns and magnitudes. A human-subject experiment (N=25) was performed to validate the system design in welding training. Our results, which demonstrated the effectiveness of the proposed method in capturing motor control patterns and improving motor skill learning, provide a strong foundation for its application in real-world training scenarios. Furthermore, it was found that providing haptic feedback according to muscle engagement was more effective than visual feedback, especially in force control. It was also noticed that haptic feedback could alleviate the reliance on external feedback compared with visual feedback. This paper presented a novel method and its implementation for welding training, contributing to innovative training methods for the workforce and beyond. [ABSTRACT FROM AUTHOR]

The utilization of high-resolution haptic feedback devices in a virtual reality (VR) environment can increase the precision of controlling remotely operated vehicles and reduce training costs for subsea construction operations. This paper introduces a novel flexible-driven wearable pin-array device. The spatial separation of the control box and actuator allows the device to attain natural and seamless interactive movements while delivering high-density haptic feedback in VR. The pin array consists of 25 haptic pin modules that run on a linear servo motor via a flexible shaft for force transmission. The analysis and calculation of the flexible transmission resistance effect using the haptic pin module's shaft length and bending radius were conducted. A series of experiments was performed to gather comprehensive data on the pin module's output, transmission resistance and error, and response delay to optimize its structural design and transmission strategy. The device's effectiveness in rendering shape and coarse texture was evaluated via two user studies. The results affirm that the introduced device enables users to precisely discern seven shapes and four distinct coarse surfaces. [ABSTRACT FROM AUTHOR]

This interdisciplinary study bridges the cultural heritage of the Pakarena dance, a key element of Makassar ethnicity, with contemporary computing and informatics to examine its social implications, particularly in terms of identity formation, community transmission, and narrative preservation. Emphasizing the dance's crucial role within the Makassar community, the research employs digital technologies for comprehensive data collection, analysis, and dissemination. Utilizing a qualitative framework supplemented by digital ethnography, the methodology includes in-depth interviews and participant observation, enriched with advanced data analysis and virtual reality (VR) presentations. This innovative approach facilitates the digital capture of the narratives and experiences of dance practitioners, cultural experts, and community members, ensuring the preservation of the dance's cultural narrative and expression. The study reveals that the Pakarena dance is not only a bearer of the Makassar community's history and traditions but also a platform for individual creativity and cultural identity, adapting while preserving its core in the face of societal shifts. The findings highlight the potential of computing and informatics in cultural preservation, suggesting new methods for documenting, analyzing, and promoting intangible cultural heritage. The study advocates for the use of technology to enhance and perpetuate cultural heritage, especially for younger generations, in our increasingly digital era. [ABSTRACT FROM AUTHOR]

LLM-based chatbots' ability to generate contextually appropriate and informative texts can be taken as an indication that they are also able to understand text. We argue instead that the separation of the two competences to generate and to understand text is the key to their performance in dialog with human users. This argument requires a shift in perspective from a concern with machine intelligence to a concern with communicative competence. We illustrate our argument with empirical examples of what conversation analysis calls 'repair', showing that the management of trouble by chatbots is not based on an underlying understanding of what is going on but rather on their use of the feedback by human conversational partners. In the conclusion we suggest that strategies for the interaction between chatbots and users should not aim to improve computational skills but to develop a new communicative competence. [ABSTRACT FROM AUTHOR]

This study investigates the potential of using advanced conversational artificial intelligence (AI) to help people understand complex AI systems. In line with conversation-analytic research, we view the participatory role of AI as dynamically unfolding in a situation rather than being predetermined by its architecture. To study user sensemaking of intransparent AI systems, we set up a naturalistic encounter between human participants and two AI systems developed in-house: a reinforcement learning simulation and a GPT-4-based explainer chatbot. Our results reveal that an explainer-AI only truly functions as such when participants actively engage with it as a co-constructive agent. Both the interface's spatial configuration and the asynchronous temporal nature of the explainer AI – combined with the users' presuppositions about its role – contribute to the decision whether to treat the AI as a dialogical co-participant in the interaction. Participants establish evidentiality conventions and sensemaking procedures that may diverge from a system's intended design or function. [ABSTRACT FROM AUTHOR]

The agent of action in Human-Computer Interaction is, as the hyphenated name of the field suggests, usually conceptualized as an contrastive binary of either human or computer. This study, informed by ethnomethodology and conversation analysis, instead describes the interactional achievement of distributed agency in a 'smart homecare' setting where a homecare worker and a disabled person coordinate shared activities using a virtual assistant. We focus on the tacit criteria, attributions, and discourses of agency embedded in the interactional details of their everyday homecare routine. The analyses reveal how collaboration in everyday care tasks involves the distributed agency of all participants, irrespective of their ostensible 'humanness'. Our findings (a) provide a critical perspective on the technological imaginary of expensive, high-tech robotic replacements for human care work; (b) advocate low-tech strategies for adapting consumer technology for smart homecare systems; and (c) suggest alternative approaches to agency in assistive technology design, grounded in detailed observation of the interactional infrastructure of real homecare settings. [ABSTRACT FROM AUTHOR]

User experience (UX) is a part of human–computer interaction research and focuses on increasing intuitiveness, transparency, simplicity, and trust for the system users. Most UX research for machine learning or natural language processing (NLP) focuses on a data-driven methodology. It engages domain users mainly for usability evaluation. Moreover, more typical UX methods tailor the systems toward user usability, unlike learning about the user needs first. This paper proposes a new methodology for integrating generative UX research into developing domain NLP applications. Generative UX research employs domain users at the initial stages of prototype development, i.e., ideation and concept evaluation, and the last stage for evaluating system usefulness and user utility. The methodology emerged from and is evaluated on a case study about the full-cycle prototype development of a domain-specific semantic search for daily operations in the process industry. A key finding of our case study is that involving domain experts increases their interest and trust in the final NLP application. The combined UX+NLP research of the proposed method efficiently considers data- and user-driven opportunities and constraints, which can be crucial for developing NLP applications. [ABSTRACT FROM AUTHOR]

Esports and high-performance human-computer interaction are at the forefront of implementing new hardware and software technologies Despite that, there is a paucity of research on how championship-level esports professionals approach aspects of their preparation. To begin addressing this gap, we conducted, transcribed, and analyzed interviews with top-level tournament players, coaches, and managers in the period 2015–2019. The interviews focused on mental, physical, and nutritional aspects of esports preparation. Initial processing included transcription and manual verification. Furthermore, where applicable, interview responses were rated and quantified by a panel of experts. Physical training was most often mentioned as a relevant or consistent activity, while nutrition was indicated as relatively unimportant. Qualitative analysis indicated that consistency and resilience are key factors for upcoming esports competitors to focus on. Interviewees also emphasized balancing their gameplay time with other activities. Lastly, we identified important areas critical for a deeper understanding of the mental and physical demands of professional esports players. [ABSTRACT FROM AUTHOR]

Tekerleğin icadından bu yana süregiden insan-makine etkileşimi günümüz modern dünyasında önemli bir dönüşüm geçirmekte; yapay zekâ teknolojileri, donanım ve yazılım, çevresel algılayıcılardaki ilerlemeler sayesinde insanlar ile otomobiller arasındaki etkileşimi değiştirmektedir. Buna bağlı olarak otonom sürüş seviyeleri artmış, otomobil ve insan arasındaki etkileşimin nasıl değiştiğini incelemek önem kazanmıştır. Bu bağlamda otomobillerde otonomluk seviyelerine bağlı olarak kullanılan yazılım ve donanım teknolojileri, insanlar ile otonom araçlar arasındaki etkileşim ve otonom araçlar ile çevre, şirketler arasındaki ilişkileri içeren üç temel kategori tanımlanmıştır. Değişen otonomluk seviyelerinin insan-makine etkileşimi üzerindeki etkisi, geçiş sürecindeki otonom araçların sürdürülebilirliği insanbilgisayar etkileşimi bağlamında ele alınmakta, geleceğe yönelik tahminler yapılmakta ve dijital pazarlamanın otonom araçlar üzerindeki etkisi örnekler üzerinden değerlendirilmektedir. Bu çalışma, otomotiv endüstrisi ve insan-bilgisayar etkileşimi bağlamında otonom araçların mevcut uygulamaları ve gelecek vaat eden yönlerinin kapsamlı bir değerlendirmesini sunmayı ve bu etkileşimleri sınıflandırmayı amaçlamaktadır. Yapılan inceleme sonucunda otonomluk seviyelerinin değişmesiyle insan-makine etkileşimi bir iş birliğine dönüşmekte, otomobillerdeki sürücüler bir yolcu olarak değerlendirilmekte ve kullanıcı deneyimi bir seyahat deneyimi haline gelmektedir.

Human-computer interaction as a coordinating element between human and machine is used in many different ways. Due to their digital processes, countless industries are dependent on an effective intermeshing of humans and machines. This often involves preparatory work or sub-processes being carried out by machines, which humans initiate, take up, continue, finalise or check. Tasks are broken down into sub-steps and completed by humans or machines. Aggregated cooperation conceals the numerous challenges of hybrid cooperation in which communication and coordination must be mastered in favour of joint decision-making. However, research into human-computer interaction can also be thought of differently than a mere aggregation of humans and machines. We want to propose a nature-inspired possibility that has been successfully practising the complex challenges of joint decision-making as proof of successful communication and coordination for millions of years. Collective intelligence and the processes of self-organisation offer biomimetic concepts that can be used to rethink socio-technical systems as a symbiosis in the form of a human-computer organism. For example, the effects of self-organisation such as emergence could be used to exceed the result of an aggregation of humans and machines as a future social anthropology 4.0 many times over.

Abstract The continuous development of high-speed Internet technology has made the application of robots increasingly widespread. Current robots and human–computer interaction systems mostly use rigid materials, such as metals and semiconductors, which have limitations in terms of deformability and flexibility. In addition, the biocompatibility and user comfort of these materials are also an issue. Therefore, research into new flexible biosensors is essential to improve the flexibility, comfort, and interactivity of these systems. This research will select polymer hydrogel as the electrode material of the sensor and polydimethylsiloxane as the base material of the sensor to design a resistance flexible biosensor to solve the poor flexibility. The research will use a template-matching method to verify the feasibility of gesture recognition of the flexible sensor. The remote control system of the robot finger is designed by a proportional-integral differential controller tuned by aradial basis function neural network. The feasibility of the research system is verified by simulation and scene experiments. The flexible sensor studied and prepared had a sensitivity of 0.7269, a tensile limit of 300%, and a thickness of 0.16 mm, showing good sensitivity and stability. The recognition accuracy of the sensor designed in the study was 92.8%, which was 8.1% higher than that of the data glove. Compared with traditional proportional-integral derivative (PID) controllers, the improved controller system error was within 10 to 3 rad, which had better adaptability and stability. Key information includes the design method of the flexible biosensor, its high sensitivity and stability under multiple stretches, and the proposal and validation of a new RBFNN–PID control model. These results showed that using this new sensor and control model significantly improved the control accuracy of mechanical fingers and the effect of gesture recognition. These results have important implications for the development of more advanced human–computer interaction systems. They not only improve the performance and reliability of the system, but also improve the user's interactive experience. These technologies are particularly promising in the fields of prosthetics for disabled people, advanced game controllers, and remotely controlled robots operating in hazardous environments. The research results are expected to lead to the development of advanced prosthetics, augmented reality devices, advanced game controllers, and automated robots. The main contribution of the research is to design a resistive flexible biosensor, which improves the traditional sensor's poor flexibility and large size and improves the sensor's ability to sense small changes. Future research may focus on further improving the sensor's long-term stability and performance under a variety of environmental conditions. In addition, commercializing these technologies and making them universal is also an important direction for the future.

Abstract This study compares how English-speaking adults and children from the United States adapt their speech when talking to a real person and a smart speaker (Amazon Alexa) in a psycholinguistic experiment. Overall, participants produced more effortful speech when talking to a device (longer duration and higher pitch). These differences also varied by age: children produced even higher pitch in device-directed speech, suggesting a stronger expectation to be misunderstood by the system. In support of this, we see that after a staged recognition error by the device, children increased pitch even more. Furthermore, both adults and children displayed the same degree of variation in their responses for whether “Alexa seems like a real person or not”, further indicating that children’s conceptualization of the system’s competence shaped their register adjustments, rather than an increased anthropomorphism response. This work speaks to models on the mechanisms underlying speech production, and human–computer interaction frameworks, providing support for routinized theories of spoken interaction with technology.

Interaction-free usage (IfU) will be one of the quantitatively dominant forms of computer use in the future. In qualitative terms, this form of use will cover a wide range of applications, also software that supports communication and cooperation. Digital twins for cooperation and communication will be employed by individual users to maintain a variety of social networking activities. Generative AI will play a decisive role in this development, autonomously identifying user needs, replacing the predominant form of use through prompting with question-and-answer dialogs. These dialogs will also be used to preconfigure systems for IfU phases. The counterpart to IfU, which will become ever less-frequent, is intervening interaction, when users intervene to explore and adjust the performance of AI-based systems in exceptional situations or to optimize them for future task handling.

Collaborative virtual environment (CVE)-based teamwork training offers a promising avenue for inclusive teamwork training. The incorporation of a feedback mechanism within virtual training environments can enhance the training experience by scaffolding learning and promoting active collaboration. However, an effective feedback mechanism requires a robust prediction model of collaborative behaviors. This paper presents a novel approach using hidden Markov models (HMMs) to predict human behavior in collaborative interactions based on multimodal signals collected from a CVE-based teamwork training simulator. The HMM was trained using k-fold cross-validation, achieving an accuracy of 97.77%. The HMM was evaluated against expert-labeled data and compared against a rule-based prediction model, demonstrating the superior predictive capabilities of the HMM, with the HMM achieving 90.59% accuracy compared to 76.53% for the rule-based model. These results highlight the potential of HMMs to predict collaborative behaviors that could be used in a feedback mechanism to enhance teamwork training experiences despite the complexity of these behaviors. This research contributes to advancing inclusive and supportive virtual learning environments, bridging gaps in cross-neurotype collaborations.

Abstract The human face is commonly used for identity verification. While this task was once exclusively performed by humans, technological advancements have seen automated facial recognition systems (AFRS) integrated into many identification scenarios. Although many state-of-the-art AFRS are exceptionally accurate, they often require human oversight or involvement, such that a human operator actions the final decision. Previously, we have shown that on average, humans assisted by a simulated AFRS (sAFRS) failed to reach the level of accuracy achieved by the same sAFRS alone, due to overturning the system’s correct decisions and/or failing to correct sAFRS errors. The aim of the current study was to investigate whether participants’ trust in automation was related to their performance on a one-to-one face matching task when assisted by a sAFRS. Participants (n = 160) completed a standard face matching task in two phases: an unassisted baseline phase, and an assisted phase where they were shown the identification decision (95% accurate) made by a sAFRS prior to submitting their own decision. While most participants improved with sAFRS assistance, those with greater relative trust in automation achieved larger gains in performance. However, the average aided performance of participants still failed to reach that of the sAFRS alone, regardless of trust status. Nonetheless, further analysis revealed a small sample of participants who achieved 100% accuracy when aided by the sAFRS. Our results speak to the importance of considering individual differences when selecting employees for roles requiring human–algorithm interaction, including identity verification tasks that incorporate facial recognition technologies.

Abstract To understand the alignment between reasonings of humans and artificial intelligence (AI) models, this empirical study compared the human text classification performance and explainability with a traditional machine learning (ML) model and large language model (LLM). A domain-specific noisy textual dataset of 204 injury narratives had to be classified into 6 cause-of-injury codes. The narratives varied in terms of complexity and ease of categorization based on the distinctive nature of cause-of-injury code. The user study involved 51 participants whose eye-tracking data was recorded while they performed the text classification task. While the ML model was trained on 120,000 pre-labelled injury narratives, LLM and humans did not receive any specialized training. The explainability of different approaches was compared based on the top words they used for making classification decision. These words were identified using eye-tracking for humans, explainable AI approach LIME for ML model, and prompts for LLM. The classification performance of ML model was observed to be relatively better than zero-shot LLM and non-expert humans, overall, and particularly for narratives with high complexity and difficult categorization. The top-3 predictive words used by ML and LLM for classification agreed with humans to a greater extent as compared to later predictive words.

Abstract In human–computer interaction systems, speech emotion recognition (SER) plays a crucial role because it enables computers to understand and react to users’ emotions. In the past, SER has significantly emphasised acoustic properties extracted from speech signals. The use of visual signals for enhancing SER performance, however, has been made possible by recent developments in deep learning and computer vision. This work utilizes a lightweight Vision Transformer (ViT) model to propose a novel method for improving speech emotion recognition. We leverage the ViT model’s capabilities to capture spatial dependencies and high-level features in images which are adequate indicators of emotional states from mel spectrogram input fed into the model. To determine the efficiency of our proposed approach, we conduct a comprehensive experiment on two benchmark speech emotion datasets, the Toronto English Speech Set (TESS) and the Berlin Emotional Database (EMODB). The results of our extensive experiment demonstrate a considerable improvement in speech emotion recognition accuracy attesting to its generalizability as it achieved 98%, 91%, and 93% (TESS-EMODB) accuracy respectively on the datasets. The outcomes of the comparative experiment show that the non-overlapping patch-based feature extraction method substantially improves the discipline of speech emotion recognition. Our research indicates the potential for integrating vision transformer models into SER systems, opening up fresh opportunities for real-world applications requiring accurate emotion recognition from speech compared with other state-of-the-art techniques.

Composite index is always derived with the weighted aggregation of hierarchical components, which is widely utilized to distill intricate and multidimensional matters in economic and business statistics. However, the composite indices always present inevitable anomalies at different levels oriented from the calculation and expression processes of hierarchical components, thereby impairing the precise depiction of specific economic issues. In this paper, we propose VisCI, a visualization framework for anomaly detection and interactive optimization of composite index. First, LSTM-AE model is performed to detect anomalies from the lower level to the higher level of the composite index. Then, a comprehensive array of visual cues is designed to visualize anomalies, such as hierarchy and anomaly visualization. In addition, an interactive operation is provided to ensure accurate and efficient index optimization, mitigating the adverse impact of anomalies on index calculation and representation. Finally, we implement a visualization framework with interactive interfaces, facilitating both anomaly detection and intuitive composite index optimization. Case studies based on real-world datasets and expert interviews are conducted to demonstrate the effectiveness of our VisCI in commodity index anomaly exploration and anomaly optimization.

In the era of intelligent media, human users and chatbots have established a deep dependency relationship through communication, making media dependence a behavioral foundation that widely permeates human social practice. This article investigates how media dependence affects human social interaction during emotional interactions between human users and chatbots. Based on the theory of media dependence, the existing mature scales of media dependence and interpersonal communication were adapted, and 496 Replika user questionnaires were collected. After screening the validity of the questionnaires, 428 valid questionnaires were obtained. Descriptive statistical analysis, correlation analysis, multiple linear regression analysis, and mediation effect testing were used to analyze the impact of media dependence on human-computer emotional interaction. Results indicate a significant positive correlation between human-chatbot emotional interaction and human user social interaction. Media dependence significantly positively regulates emotional interactions between humans and chatbots. In addition, the social interactions of human users are partially influenced by factors such as user nature, age, education, and income. [ABSTRACT FROM AUTHOR]

Notification systems that convey urgency without adding cognitive burden are crucial in human–computer interaction. Haptic feedback systems, particularly those utilizing vibration feedback, have emerged as a compelling solution, capable of providing desirable levels of urgency depending on the application. High-risk applications require an evaluation of the urgency level elicited during critical notifications. Traditional evaluations of perceived urgency rely on subjective self-reporting and performance metrics, which, while useful, are not real-time and can be distracting from the task at hand. In contrast, EEG technology offers a direct, non-intrusive method of assessing the user's cognitive state. Leveraging deep learning, this study introduces a novel approach to evaluate perceived urgency from single-trial EEG data, induced by vibration stimuli on the upper body, utilizing our newly collected urgency-via-vibration dataset. The proposed model combines a 2D convolutional neural network with a temporal convolutional network to capture spatial and temporal EEG features, outperforming several established EEG models. The proposed model achieves an average classification accuracy of 83% through leave-one-subject-out cross-validation across three urgency classes (not urgent, urgent, and very urgent) from a single trial of EEG data. Furthermore, explainability analysis showed that the prefrontal brain region, followed by the central brain region, are the most influential in predicting the urgency level. A follow-up neural statistical analysis revealed an increase in event-related synchronization (ERS) in the theta frequency band (4–7 Hz) with the increased level of urgency, which is associated with high arousal and attention in the neuroscience literature. A limitation of this study is that the proposed model's performance was tested only the urgency-via-vibration dataset, which may affect the generalizability of the findings. [ABSTRACT FROM AUTHOR]

Introduction: The field of electroencephalogram (EEG)-based emotion identification has received significant attention and has been widely utilized in both human-computer interaction and therapeutic settings. The process of manually analyzing electroencephalogram signals is characterized by a significant investment of time and work. While machine learning methods have shown promising results in classifying emotions based on EEG data, the task of extracting distinct characteristics from these signals still poses a considerable difficulty. Methods: In this study, we provide a unique deep learning model that incorporates an attention mechanism to effectively extract spatial and temporal information from emotion EEG recordings. The purpose of this model is to address the existing gap in the field. The implementation of emotion EEG classification involves the utilization of a global average pooling layer and a fully linked layer, which are employed to leverage the discernible characteristics. In order to assess the effectiveness of the suggested methodology, we initially gathered a dataset of EEG recordings related to music-induced emotions. Experiments: Subsequently, we ran comparative tests between the state-of-the-art algorithms and the method given in this study, utilizing this proprietary dataset. Furthermore, a publicly accessible dataset was included in the subsequent comparative trials. Discussion: The experimental findings provide evidence that the suggested methodology outperforms existing approaches in the categorization of emotion EEG signals, both in binary (positive and negative) and ternary (positive, negative, and neutral) scenarios. [ABSTRACT FROM AUTHOR]

Human-computer interaction is a discipline that studies the interaction between computer systems and users. Nowadays, it has become an important research topic in the field of robotics, especially in the field of auxiliary robots. As gestures can express rich information, gesture recognition is widely used in machine control, smart home, etc. Vision-based gesture recognition systems can greatly facilitate the lives of the elderly, the sick and the disabled. However, it is also a challenging task to compress the model while maintaining its accuracy in order to achieve efficient recognition on some inexpensive hardware configurations with limited resources. To this end, a lightweight gesture recognition based on an improved gated recurrent unit (GRU) for skeleton dynamic graphs is proposed in this paper. Firstly, the gesture skeleton data is preprocessed, and the coordinates of key joints are selected as input. Then, the mapped gesture feature data is cut to an appropriate size based on an adjustment and clipping module, and then input into an improved GRU for feature learning, which strengthens the ability of the GRU network to learn spatial features, so as to achieve efficient extraction of temporal and spatial features. Finally, the prediction result is output by the classifier. The extensive experiments are conducted on the public datasets DHG14/28 and SHREC'17. In terms of the model size or number of parameters, the proposed model can get the state-of-the-art performance. The average size of the proposed model decreases by more than 30% compared with the general GRU network. Furthermore, the proposed model can get a competitive accuracy performance of 92.13% and 93.98%, on the dataset DHG14/28 and SHREC'17, respectively. It is proved that our model is a lightweight and efficient model for gesture recognition of skeleton dynamic graphs. [ABSTRACT FROM AUTHOR]

This study delves into Human–Computer Intelligent Interaction (HCII), a burgeoning interdisciplinary field that builds upon traditional Human–Computer Interaction (HCI) by integrating advanced technologies like Natural Language Processing (NLP) and Machine Learning (ML). In this paper, we scrutinize 5,781 HCII papers published between 2000 and 2023, narrowing our focus to 803 most relevant articles to construct co-citation and interdisciplinary networks based on the CiteSpace Software. Our findings reveal that the publications of the United States and China are relatively high with 558 and 616 publications respectively. Furthermore, we found that machine learning and deep learning have emerged as the prevalent methodologies in HCII, which currently emphasizes multimodal emotion recognition, facial expression recognition, and NLP. We predict that HCII will be integrated into advanced applications such as neural-based interactive games and multi-sensory environments. In sum, our analysis underscores HCII's role in advancing artificial intelligence, facilitating more intuitive and efficient human–computer interactions, and its prospective societal impact. We hope that our review and analysis may guide the efforts of researchers aiming to contribute to HCII and develop more powerful and intelligent methods, tools, and applications. [ABSTRACT FROM AUTHOR]

Today, Smart Assistants (SAs) are supported by significantly improved Natural Language Processing (NLP) and Natural Language Understanding (NLU) engines as well as AI-enabled decision support, enabling efficient information communication, easy appliance/device control, and seamless access to entertainment services, among others. In fact, an increasing number of modern households are being equipped with SAs, which promise to enhance user experience in the context of smart environments through verbal interaction. Currently, the market in SAs is dominated by products manufactured by technology giants that provide well designed off-the-shelf solutions. However, their simple setup and ease of use come with trade-offs, as these SAs abide by proprietary and/or closed-source architectures and offer limited functionality. Their enforced vendor lock-in does not provide (power) users with the ability to build custom conversational applications through their SAs. On the other hand, employing an open-source approach for building and deploying an SA (which comes with a significant overhead) necessitates expertise in multiple domains and fluency in the multimodal technologies used to build the envisioned applications. In this context, this paper proposes a methodology for developing and deploying conversational applications on the edge on top of an open-source software and hardware infrastructure via a multilayer architecture that simplifies low-level complexity and reduces learning overhead. The proposed approach facilitates the rapid development of applications by third-party developers, thereby enabling the establishment of a marketplace of customized applications aimed at the smart assisted living domain, among others. The supporting framework supports application developers, device owners, and ecosystem administrators in building, testing, uploading, and deploying applications, remotely controlling devices, and monitoring device performance. A demonstration of this methodology is presented and discussed focusing on health and assisted living applications for the elderly. [ABSTRACT FROM AUTHOR]

AbstractOpen hardware and the need for ecologically valid measurements drive the Electroencephalography (EEG) democratization movement—EEG has been steadily transcending the boundaries of clinical research, making its way into interdisciplinary fields. In Human-Computer Interaction (HCI), EEG is used to measure cognitive workload and infer cognitive processes for building cognition-aware systems. We describe and evaluate our BCIglass prototype where EEG electrodes are embedded in the frame of a mainstream Head-Mounted Display (HMD) to create a skull-peripheral topology. We devised a lab study with 34 participants who completed seven established cognitive tasks. Then, we conducted a pilot field study with one participant to test BCIglass in everyday-life settings. Our findings demonstrate that BCIglass captures EEG activity in a manner comparable to a research-grade EEG-cap system. Our topology infers the cognitive task at hand, and the underlying cognitive process(es) by proxy, with an accuracy of ∼80% and only three electrodes at the skull periphery. Embedding EEG electrodes in lightweight HMDs represents a promising approach in the quest to achieve ubiquitous brain-computer interfacing in real-world settings. [ABSTRACT FROM AUTHOR]

With the rapid development of China and the continuous improvement of its global status, Chinese has attracted wide attention in the world. However, at present, China is still facing many problems, mainly manifested as: the students' enthusiasm is not high, the effect is not ideal. In this context, how to effectively promote students' learning efficiency in classroom teaching has been an urgent problem to be solved. In view of the problems of passive learning and participation consciousness in oral Chinese teaching, this paper designs a set of intelligent speech teaching platform with speech recognition, speech synthesis, natural language processing and natural language production as the core. The experiment has proved that the application of human-computer interaction technology in oral Chinese can mobilize students' enthusiasm for learning, enhance their enthusiasm for Chinese learning, and improve their oral English level. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as a cutting-edge and trending research direction. Increasing numbers of groups are engaging in BCI research and development. However, in recent years, there has been some confusion regarding BCI, including misleading and hyped propaganda about BCI, and even non-BCI technologies being labeled as BCI. Therefore, a clear definition and a definite scope for BCI are thoroughly considered and discussed in the paper, based on the existing definitions of BCI, including the six key or essential components of BCI. In the review, different from previous definitions of BCI, BCI paradigms and neural coding are explicitly included in the clear definition of BCI provided, and the BCI user (the brain) is clearly identified as a key component of the BCI system. Different people may have different viewpoints on the definition and scope of BCI, as well as some related issues, which are discussed in the article. This review argues that a clear definition and definite scope of BCI will benefit future research and commercial applications. It is hoped that this review will reduce some of the confusion surrounding BCI and promote sustainable development in this field. [ABSTRACT FROM AUTHOR]

ChatGPT, an extensively recognised language model created by OpenAI, has gained significant prominence across various industries, particularly in education. This study aimed to investigate the factors that influence students' intentions to adopt and utilise ChatGPT for their academic studies. The study used a Structural Equation Model (SEM) for analysing the data gathered from 108 participants, comprising both undergraduate and postgraduate students enrolled in public and private universities in Vietnam. The findings indicated that students' inclination to adopt ChatGPT (referred to as adoption intention or AI) was influenced by their perception of its user-friendliness (PEU). However, the perceived usefulness (PU) of ChatGPT did not have a direct impact on students' adoption intention; instead, it had an indirect influence through personalisation (with a positive effect) and interactivity (with a negative effect). Importantly, there was no significant indirect effect of PU on AI mediated by perceived trust and perceived intelligence. This study is one of the initial empirical inquiries into ChatGPT adoption within an Asian context, providing valuable insights in this emerging area of research. As the use of ChatGPT by students becomes increasingly inevitable, educational institutions should carefully consider integrating it into the assessment process. It is crucial to design assessments that encourage responsible usage of ChatGPT, preserving students' critical thinking abilities and creativity in their assessment writing. Moving forward, educators will play a pivotal role by offering clear guidelines and instructions that set out the appropriate and ethical use of artificial intelligence tools in the assessments. [ABSTRACT FROM AUTHOR]

Knowledge selection plays a crucial role in knowledge-driven dialogue generation methods, directly influencing the accuracy, relevance, and coherence of generated responses. Existing research often overlooks the handling of disparities between dialogue statements and external knowledge, leading to inappropriate knowledge representation in dialogue generation. To overcome this limitation, this paper proposes an innovative Multi-hop Reasoning Knowledge Selection Module (KMRKSM). Initially, multi-relational graphs containing rich composite operations are encoded to capture graph-aware representations of concepts and relationships. Subsequently, the multi-hop reasoning module dynamically infers along multiple relational paths, aggregating triple evidence to generate knowledge subgraphs closely related to dialogue history. Finally, these generated knowledge subgraphs are combined with dialogue history features and synthesized into comprehensive knowledge features by a decoder. Through automated and manual evaluations, the exceptional performance of KMRKSM in selecting appropriate knowledge is validated. This module efficiently selects knowledge matching the dialogue context through multi-hop reasoning, significantly enhancing the appropriateness of knowledge representation and providing technical support for achieving more natural and human-like dialogue systems. [ABSTRACT FROM AUTHOR]

Fitts' law, a predictive model for motor task completion time, is widely utilized in human–computer interaction (HCI) research. While its formulas in two dimensions have achieved consensus over the decades, research diverges on its application in three dimensions. This paper synthesizes practical applications across touchscreens, virtual reality (VR), pedals, handheld devices, etc., with a specific emphasis on enhancing interaction experiences for vulnerable populations. This review studies Fitts' law's applicability in diverse interaction scenarios, highlighting design considerations for touchscreens and handheld/foot-held devices. This article underscores the need for future research to explore three-dimensional applications and consider user age, with potential expansions into medical and sports domains. This systematic review aims to empower designers in crafting more ergonomic products and improving HCI experiences. [ABSTRACT FROM AUTHOR]

Featured Application: This research has important applications in areas such as smart furniture and human-computer interaction. It will bring people a more efficient and comfortable living experience as well as a new smart experience.  Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set. [ABSTRACT FROM AUTHOR]

This study explores the development and pilot testing of an exergame designed for the rehabilitation of individuals with upper limb deficits. While traditional physiotherapy is effective, it often fails to fully engage patients due to its repetitive nature. This research integrates a novel exergame into physiotherapy regimens, aiming to enhance patient motivation through a gaming experience that complements conventional sessions. The exergame is structured around a narrative-driven adventure, with exercises embedded in gameplay that mirror adjustable physiotherapy routines. It utilizes the Myo armband, a wearable electromyography device, to capture muscle activity and movement. The system, part of a web-based platform, is easily deployable in various settings, including home environments. Comprehensive evaluations with health professionals and neurological patients indicate that the exergame significantly improves patient engagement. This study not only demonstrates the potential of exergames in enhancing traditional therapy but also underscores the importance of patient-centered therapeutic tools. [ABSTRACT FROM AUTHOR]

Background: Nursing students often face anxiety and cognitive overload, leading to high attrition rates; meanwhile, traditional pastoral interventions have proved insufficient. A novel, digitally enhanced learning environment named The Wellbeing in Student Education (WISE) Room, was introduced as an alternative at a UK university. Aims: This study investigated the impact of The WISE Room, a 4m × 4m digital platform containing touch-interactive walls, co-created with students and academic staff, designed to promote insight through immersive experiences beyond traditional learning settings. Methods: A mixed-methods approach was adopted, collecting data from nursing students, healthcare staff and academic faculty. Data were collected at three stages, each corresponding with the iterative development of The WISE Room, including an evaluation of the intervention. Results: Feedback from 240 participants was positive. In Phase 2 of the study, 93% of the 79 students and 21 staff who visited the room and completed a survey were satisfied with the room's suitability in three key areas: emotional wellbeing, academic readiness, and placement preparedness. Conclusion: The WISE Room has proved effective in supporting nursing students' wellbeing, and educational and placement preparation requirements. Its multisensory, digital and experiential nature is beneficial for students throughout their learner journey. Additional trials and content development will be required to enhance the learning and teaching experience further. [ABSTRACT FROM AUTHOR]

3D shape learning is an important research topic in computer vision, in which the datasets play a critical role. However, most of the existing 3D datasets use voxels, point clouds, mesh, and B-rep, which are not parametric and feature-based. Thus they can not support the generation of real-world engineering computer-aided design (CAD) models with complicated shape features. Furthermore, they are based on 3D geometry results without human-computer interaction (HCI) history. This work is the first to provide a full parametric and feature-based CAD dataset with a selection mechanism to support HCI in 3D learning. First, unlike existing datasets, mainly composed of simple features (typical sketch and extrude), we devise complicated engineering features, such as fillet, chamfer, mirror, pocket, groove, and revolve. Second, different from the monotonous combination of features, we invent a select mechanism to mimic how human focuses on and selects a particular topological entity. The proposed mechanism establishes the relationships among complicated engineering features, which fully express the design intention and design knowledge of human CAD engineers. Therefore, it can process advanced 3D features for real-world engineering shapes. The experiments show that the proposed dataset outperforms existing CAD datasets in both reconstruction and generation tasks. In quantitative experiment, the proposed dataset demonstrates better prediction accuracy than other parametric datasets. Furthermore, CAD models generated from the proposed dataset comply with semantics of the human CAD engineers and can be edited and redesigned via mainstream industrial CAD software. [ABSTRACT FROM AUTHOR]

In this preliminary investigation, a discerning evaluation reveals that users face pervasive challenges accessing e-resources within university libraries. A social presence interface design for e-resources is introduced, recognizing the need for user support through effective communication with librarians and assistance for swift information navigation and system function. This research presents a meticulous usability assessment grounded in heuristic evaluation, engaging six expert evaluators comprising specialists in human-computer interaction and librarians. The primary objective is to ascertain the intuitiveness of the interface design and identify potential issues through expert scrutiny. The study unveiled compelling insights by employing a tailored usability heuristic questionnaire encompassing 39 questions aligned with the developed design. Notably, 75.54% of evaluators agreed unanimously regarding the design’s adherence to heuristic principles, indicating their concurrence with the established criteria. Additionally, evaluators provided valuable commentary and feedback focusing on aspects related to icons, labels, and the efficacy of the help function. This comprehensive evaluation informs and enhances the ongoing discourse surrounding optimal user interface design for e-resources in university library settings. [ABSTRACT FROM AUTHOR]

Intangible cultural heritage (ICH) represents living cultural expressions and practices that are part of the heritage of a community, and their preservation and transmission are considered highly important. Various methods and tools have been applied so far for the digitization and dissemination of ICH content including a wide range of technologies. Mobile augmented reality is a promising solution along this path that enables the overlap of digital and real-world information in an engaging and efficient manner. Despite the widespread use of AR in cultural heritage, there are not many studies regarding the user experience, the learning outcomes, and the way in which users observe and interact with the virtual content. This paper presents a mobile augmented reality installation that re-enacts the stages of leather tanning process, adopting a novel approach that augments 3D content upon a physical scale model of an old tannery. This approach pursues to transmit the cultural value of traditional craftmanship to visitors of the building and associate its architectural elements to its history and use. A user evaluation was conducted aiming to measure the users' engagement, learning, and experience using the installation. The encouraging results led to a follow-up study about the impact of the physical scale model on the experience. Two variations of the experience have been studied, one with a physical scale model and one with a digital-only version in a between-subject design. The results of the two studies provide evidence that the proposed approach generated a positive user experience and evident learning gain and was considered easy to use, highlighting its potential to be widely adopted in buildings with architectural value. [ABSTRACT FROM AUTHOR]

Taking into account the profound impact of technology on modern education, especially during the covid19 pandemic, increasing academic interest has focused towards the design and application of such tools on different learning contexts. A specific area of Human–Computer Interaction, called affordance theory, focuses on the perception, design and use of different technologies by educators and learners in learning contexts. This paper explores the impact of affordances in the process of creative problem in the context of playful educational robotics, with an intension of informing the design of future educational experiences around the field. The study capitalizes upon previous affordance propositions and frameworks in order to establish an affordance-based framework in the scope of playful educational robotics contexts, through the adoption of a qualitative research methodology, which was considered more appropriate as an exploratory tool. As part of the qualitative analysis, the study is mapping different types of affordances, related to such technologies, as well as an iterative creative problem-solving process that stems from learners' interactions with robotic artifacts, like the CreaCube playful robotics activity, which is presented in this study. [ABSTRACT FROM AUTHOR]

Human pose estimation has important applications in medical diagnosis (such as early diagnosis of autism in children and assisting with the diagnosis of Parkinson's disease), human-computer interaction, animation, and other fields. Currently, many human pose estimation algorithms are based on deep learning. However, most research focuses only on increasing the depth and width of the network model. This approach overlooks that merely enlarging the network's depth and width results in excessive parameterization, without enhancing the model's effective receptive field or its ability to extract multi-scale features. Hence, this paper constructs a network model, named MS-HRNet (Multi-Scale High-Resolution Network), for human pose estimation. Specifically, we propose a more concise and efficient version of HRNet framework as the backbone network of MS-HRNet. This addresses the challenges of HRNet complex structure and large number of parameters that cause training difficulties, and its inadequacy in handling multi-scale information. Additionally, we designed a multi-scale convolutional kernel parallel module named MSBlock (Multi-Scale Block) as the basic block of MS-HRNet. By introducing coordinate attention modules and ASFF (Adaptive Spatial Feature Fusion) modules, the model's ability to extract information is effectively increased, and the issue of feature conflict during the fusion of features with different resolutions is resolved, with only a small increase in the number of model parameters. To evaluate the effectiveness of the proposed model, we conducted comparison experiment and ablation experiments using popular human pose estimation datasets, including COCO2017 and MPII, against multiple existing human pose estimation models.On the COCO 2017 dataset, the number of MS-HRNet parameters are decreased by 41% than the baseline model HRNet, the computational complexity by 59%, and the detection accuracies(mAP) are increased by 2.4 point. [ABSTRACT FROM AUTHOR]

Improving the quality of life for people with vision impairments has been an important goal in the research and design of assistive devices for several decades. This paper seeks to further that goal by introducing a novel assistive technology platform that leverages real-time 3D spatial audio to promote safe and efficient navigation for people who are blind or visually impaired (PVI). The presented platform, EchoSee, uses modern 3D scanning technology on a mobile device to construct a live, digital 3D map of a user's environment as they move about their surroundings. Spatialized, virtual audio sources (i.e., virtual speakers) are dynamically placed within the digital 3D scan of the world, providing the navigator with a real-time 3D stereo audio "soundscape." The digital 3D map, and its resultant soundscape, are continuously updated as the user moves about their environment. The generated soundscape is played back through headphones connected to the navigator's device. This paper details (1) the underlying technical components and how they were integrated to produce the mobile application that generates a dynamic soundscape on a consumer mobile device, (2) a methodology for analyzing navigation performance with the application, (3) the design and execution of a user study investigating the effectiveness of the presented system, and (4) a discussion of the results of that study along with a proposed future study and possible improvements. Altogether, this paper presents a novel software platform aimed at assisting individuals with vision impairments to navigate and understand spaces safely, efficiently, and independently and the results of a feasibility study analyzing the viability of the approach. [ABSTRACT FROM AUTHOR]

This study investigated how different learning tasks influence students' collaborative interactions in immersive Virtual Reality (iVR). A set of chemistry learning activities was designed with iVR, and 35 pairs of undergraduate students went through the activities. Videos of students' interactions were analysed to identify patterns in students' physical, conceptual, and social interactions. When students were manipulating conceptually familiar virtual objects (several water molecules), they perceived the tasks as a simple extension of prior knowledge and did not attempt to explore the 3D visualisation much. They did not move around to take different perspectives, and conceptual discussions were brief. Their prior power relations (leader–follower) carried over in iVR environments. In contrast, when conceptually unfamiliar chemical structures (protein enzyme) were displayed, students perceived the tasks as complex, demanding a new mode of learning. They spontaneously moved around to explore and appreciate the 3D visualisation of iVR. Walking to different positions to observe the virtual objects from multiple angles, students engaged in more collaborative, exploratory conceptual discussions. As the perceived complexity of learning tasks or virtual objects triggers different collaborative interactions amongst students, careful considerations need to be placed on the design of iVR tasks to encourage productive collaborative learning. [ABSTRACT FROM AUTHOR]

Robots and smart technologies are becoming part of everyday life and private households. While this automation of mundane tasks typically creates pragmatic benefits (e.g., efficiency, accuracy, time savings), it may also come with psychological losses, such as in meaning, competence, or responsibility. Depending on the domain (e.g., AI-assisted surgery, automated decision making), especially the user's felt responsibility could have far-reaching consequences. The present research systematically explores such effects, building a more structured base for responsible automation in human–computer interaction (HCI). Based on a framework of seven dimensions, study 1 (N = 122) evaluates users' reflections on automating five mundane tasks (e.g., gardening, paperwork) and identifies reasons for or against automation in different domains (e.g., creativity, care work, decision making). Study 2 (N = 57) provides deeper insights into effects of automation on responsibility perceptions. Based on the example of a vacuum cleaner robot, an experimental laboratory study contrasted a non-robotic manual vacuum cleaner to a robot, whereby the user's perceptions of device agency (higher for the robot) and own competence (higher for the manual device) were central mediators for the perceived responsibility for the result. We position our findings as part of a broader idea of responsible design and automation from a user-centered design perspective. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCI) are systems able to respond to a user's intent based on physiological information acquired directly from his/her brain. BCI-based games employ those physiological data to control or alter the game world, providing not only the possibility of greater immersion for the player, but also allowing virtually any person, regardless of physical disabilities, to play the game, allowing many research possibilities for the field of Human-Computer Interaction (HCI). However, being an interdisciplinary field, research with BCI-based games is often conducted by researchers from different areas, making it difficult to organize and compare studies involving those games. In this context, this article presents a model-based taxonomy for games controlled by electroencephalography (EEG), a specific case of non-invasive modality for BCI systems. The taxonomy, called CoDIS, was constructed after surveying the literature and considering four aspects of EEG-controlled games, i.e., Concept, Design, Implementation and Study, each with different dimensions and attributes to represent a total of 48 characteristics of such games. Each of these dimensions was then described considering the current state of the field, and the usefulness of the taxonomy was demonstrated based on examples from the literature. The taxonomy may be used by researchers, designers and developers to classify, identify, implement and compare EEG-based games based on the dimensions of the taxonomy. [ABSTRACT FROM AUTHOR]

The automotive glazing projection display pertains to projecting information onto unique glazing materials in a car through projection equipment, a new direction of Human-Computer Interaction (HCI) in the intelligent cockpit. The core issue in glazing projection is determining what information should be projected onto the car's glazing and the area to which it should be projected. To address this issue, we conducted a focus group comprising 12 people. We distributed 1082 questionnaires to investigate future autonomous driving contexts, user information demands based on glazing projection, and regional preferences for displaying such information. We selected five contexts and nine high-rated features to design a driver-specific display interface. We conducted eye-movement experiments to investigate the effect of the projection interface on the driver's gaze behaviors under different display areas and subsequently modified the display areas. This study confirms the feasibility of the glazing projection display and provides theoretical support for HCI development in the intelligent cockpit. [ABSTRACT FROM AUTHOR]

Cheating is a major concern for any gaming environment, but effective solutions are still far from being found. Previous research has overlooked the social complexity of current video games and the role that technology may have in determining the cheating practices. In this article, we explore how cheating unfolds within Call of Duty: Warzone through a digital ethnography, analyzing the perspectives of both amateur players and video game streamers and how technology shapes the cheating phenomenon. We highlight that players' perception of cheating depends on the "social role" of the cheater. Moreover, the technological sophistication of cheats makes them uncertain in the players' eyes, contributing to the spreading of mutual suspicion and surveillance practices. Based on these findings, we offer a novel understanding of cheating as a sort of black box that only a few individuals are able to decipher, and several design considerations addressing the cheating practices in contemporary games. [ABSTRACT FROM AUTHOR]

Side channels are unintended pathways within target systems that leak internal target information. Side-channel sensing (SCS) is the process of exploiting side channels to extract embedded target information. SCS is well established within the cybersecurity (CYB) domain, and has recently been proposed for medical diagnostics and monitoring (MDM). Remaining unrecognised is its applicability to human–computer interaction (HCI), among other domains (Misc). This article analyses literature demonstrating SCS examples across the MDM, HCI, Misc, and CYB domains. Despite their diversity, established fields of advanced sensing and signal processing underlie each example, enabling the unification of these currently otherwise isolated domains. Identified themes are collating under a proposed domain-agnostic SCS framework. This SCS framework enables a formalised and systematic approach to studying, detecting, and exploiting of side channels both within and between domains. Opportunities exist for modelling SCS as data structures, allowing for computation irrespective of domain. Future methodologies can take such data structures to enable cross- and intra-domain transferability of extraction techniques, perform side-channel leakage detection, and discover new side channels within target systems. [ABSTRACT FROM AUTHOR]

With the advent of the popular use of artificial intelligence (AI), the higher education (HE) sector is now facing a new challenge regarding how to exploit the educational potentials of Human-computer interaction (HCI). Developing students’ AI literacy is now attracting the attention of the HE and HCI research community. This paper aims to explore HE students’ perceptions of efficient and critical use of AI tools, and to systematically map the potential components of AI literacy as a new 21st century skill for HE students. This study applied a qualitative, exploratory approach in the form of semistructured interviews with HE students. Results indicate that the participants primarily use ChatGPT for tasks such as brainstorming, topic selection, searching for information, and translation. While many find it useful for creating and reformatting texts, some encountered challenges, including generality in responses, outdated information, and issues during exams. Students highlighted its effectiveness in various academic tasks, from writing essays and CVs to language learning and transcription. Instructors’ perspectives on ChatGPT varied, with some advocating for its integration, while others expressed concerns about job security and misinformation. The implications of the study call for a more systematic introduction and discussion around AI literacy in educational settings. [ABSTRACT FROM AUTHOR]

This document discusses the use of artificial intelligence (AI), specifically ChatGPT, in English Language Arts (ELA) education. It highlights the potential of AI to support students with disabilities and enhance their literacy skills. The document emphasizes the importance of explicit instruction on how to use AI as a tool and acknowledges that AI is not a substitute for human thinking and learning. It also provides guidelines for instructors on how to incorporate AI tools into their teaching and emphasizes the importance of ethical considerations and responsible use. Additionally, a research paper explores the use of ChatGPT in educational settings and its impact on reading comprehension exercises. [Extracted from the article]