Search or ask a question

How does emotional intelligence affect the design and development of chatbots?

Sentiment analysis

Emotional intelligence

Personalization

Best insight from top research papers

Emotional intelligence significantly influences the design and development of chatbots. Research has shown that incorporating emotional intelligence features in chatbots can enhance user satisfaction and engagement . Chatbots with emotional intelligence capabilities can understand and respond appropriately to complex human emotions, leading to more empathetic interactions and a positive user experience . Studies have highlighted the importance of features like personalization, responsiveness, and human intervention in emotionally intelligent chatbots for children, emphasizing the need for emotional support in various situations . Additionally, the use of Artificial Neural Networks (ANN) and supervised learning in training chatbots to identify users' emotional states can further improve their effectiveness in providing relevant suggestions based on emotional cues .

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Book Chapter•DOI Design of a Chatbot for Four- to Ten-Year-Old Children Based on Emotional Intelligence Qasem Abu Al-Haija 27 Sep 2022	Emotional intelligence influences chatbot design by enabling features like personalization, responsiveness, and human intervention, enhancing the chatbot's ability to support children in coping with emotional challenges.
Open access•Posted Content•DOI Testing emotional understanding of ChatGPT: Interview with ChatGPT Marianna Mazza 21 Mar 2023	Emotional intelligence influences chatbot design by enhancing understanding and appropriate responses to human emotions, aiming for engaging interactions and positive user experiences.
Proceedings Article•DOI Emotion Based Chatbot Using Deep Learning Nurlaila Binti Rosli, Alexander Chai Min Jie, Lo Woon Chze, Nelson Chai Zhen Faan, Yek Nai Liang - Show less +4 more 01 Dec 2022	Emotional intelligence impacts chatbot design by enabling emotion detection through text input, enhancing user interaction and providing tailored responses, as proposed in the research.
Proceedings Article•DOI Emotion Based Chatbot Using Deep Learning 01 Dec 2022	Emotional intelligence influences chatbot design by enabling emotion detection through user text, enhancing user interaction by providing tailored responses, as demonstrated in the proposed emotion-based chatbot using deep learning.
Open access•Journal Article•DOI Emotionally Intelligent Chatbots: A Systematic Literature Review Ghazala Bilquise, Samar Ibrahim, Khaled Shaalan - Show less +2 more 26 Sep 2022-Human behavior and emerging technologies 2 Citations	Emotional intelligence enhances chatbot design by enabling accurate emotion detection and generation of emotionally relevant responses, crucial for improving user satisfaction in human-machine interactions.

My columns

Related Questions

What is artificial intelligence chatbots?4 answersArtificial intelligence chatbots are programs designed to simulate human conversation using natural language processing (NLP) and neural networks, aiding users through digital interactions. These chatbots can understand user queries in various formats like voice, text, and sentiments, providing immediate responses based on pre-training. They have been applied in diverse fields such as healthcare, education, and customer service, showing potential in promoting health and well-being through personalized interactions. While AI chatbots have demonstrated efficiency in managing routine tasks and processing data, they are seen as supplements rather than substitutes for human professionals in healthcare due to the lack of empathy and intuition inherent in machines. Despite their potential to revolutionize online health information-seeking behavior and combat misinformation, challenges like system accuracy and security remain, necessitating continuous training and updates.

How AI chatbots are (re)shaping the way consumers think, feel, and act?5 answersAI chatbots are significantly impacting consumer behavior by influencing perceptions and interactions. Studies show that anthropomorphic cues in chatbots enhance consumer attitudes and intentions to use them, especially in hedonic-centered businesses. Furthermore, the conversational properties of AI chatbots, such as turn-taking and grounding, enhance perceived humanness, leading to more intimate consumer-brand relationships and favorable brand outcomes. Additionally, the quality of interaction and information provided by AI chatbots positively influences consumer trust and purchase intentions, especially among customers familiar with the products. Moreover, well-designed AI chatbots can enhance consumers' willingness to disclose personal information, with different interaction styles catering to promotion-focused and prevention-focused individuals. These findings collectively demonstrate how AI chatbots are reshaping consumer perceptions, emotions, and behaviors in various contexts.

How does the use of chatbots impact the design thinking process?4 answersThe use of chatbots in the design thinking process has been found to have a positive impact on efficiency and creativity. Integrating AI tools, such as ChatGPT, in the Define and Ideate stages of design thinking can improve the user interface (UI). ChatGPT has been utilized to generate personas, simulate interviews, create design ideas, simulate usage scenarios, and evaluate user experience, effectively performing the tasks assigned to it. In a study comparing student groups interacting with chatbots, target customers, and no users, the chatbot group exhibited better design concept scores and more significant transitions of engineering design behaviors. Additionally, the GPT-3 Large Language Model (LLM) has shown potential in extracting useful knowledge from human dialogues during co-design sessions, which can be embedded in a conversational agent to support humans in design activities.

How does emotional influence impact the adoption of AI technology in various industries?4 answersEmotional influence has a significant impact on the adoption of AI technology in various industries. Emotional AI tools are being increasingly adopted in the workplace, with vendors promoting their benefits for organizations. However, workers perceive emotion AI as a deep privacy violation and express concerns about the privacy of their sensitive emotional information. Emotional AI may also function to enforce workers' compliance with emotional labor expectations, leading workers to engage in emotional labor as a mechanism to preserve privacy over their emotions. Additionally, emotional intelligence dimensions have been found to influence personal self-efficacy, which in turn drives technology adoption. Overall, emotional influence plays a crucial role in shaping the adoption of AI technology, impacting workers' privacy, emotional labor, and self-efficacy in various industries.

How does the use of AI chatbots affect customer engagement?5 answersThe use of AI chatbots has a significant impact on customer engagement. Chatbots can replicate human guidance, improving user engagement and efficacy in internet-based self-help interventions. They can also enhance customer trust in AI chatbots for hotel services, with empathy response being the strongest influence on interaction. AI chatbots are utilized by firms to engage with their consumer base, provide real-time help, and attract customers to make purchases. Customized interaction with AI chatbots can improve positive customer experience, and the use of anthropomorphic AI chatbots positively influences customer experience. Overall, AI chatbots play a crucial role in enhancing customer engagement by providing personalized assistance, building trust, and creating positive experiences.

Chatbots applied to designing ICt architectures?3 answersChatbots have been applied to designing ICT architectures. The integration of chatbots with IoT systems has been explored to address the shortcomings of existing IoT systems and improve their efficiency and intelligence. In the context of product configuration, chatbots have been developed to simplify the complexity of current product configurators and provide a natural language interface for customization. Reference architectures have been proposed for building applications with chatbot components, including commercial platforms like IBM Watson Assistant and Google DialogFlow. Chatbots have also been studied as examples of intentional and intelligent systems, with the modeling and analysis of different types of chatbots using process architecture and actor models. The main components and architectures of chatbots have been outlined, providing a starting point for learning about them in-depth.

See what other people are reading

Dos Santos C, Gatti M. Deep convolutional neural networks for sentiment analysis of short texts DOI

Dos Santos C, Gatti M. utilized deep convolutional neural networks for sentiment analysis of short texts. This approach is crucial in the field of natural language processing (NLP) due to the increasing importance of sentiment analysis in understanding subjective information from text data. The use of deep learning neural networks, such as convolutional neural networks (CNN) and long short-term memory (LSTM), has shown promising results in sentiment categorization. Additionally, the study by Zhan Shi, Chongjun Fan, highlighted the advantages of Bayesian and deep neural networks in short text sentiment classification, emphasizing the effectiveness of these algorithms in text representation for sentiment analysis tasks. Furthermore, the work by Raed Khalid, Pardeep Singh demonstrated the potential of using S-BERT pre-trained embeddings in combination with a CNN model for sentiment analysis, outperforming traditional machine learning approaches and word embedding models.Dos Santos C, Gatti M. Deep convolutional neural networks for sentiment analysis of short texts

Dos Santos C, Gatti M. proposed the use of deep convolutional neural networks (CNNs) for sentiment analysis of short texts. This approach leverages the power of deep learning in natural language processing (NLP). The study by Raed Khalid and Pardeep Singh also highlighted the effectiveness of CNNs in sentiment analysis, achieving high accuracy by combining S-BERT pre-trained embeddings with a CNN model. Additionally, research by Zhan Shi and Chongjun Fan emphasized the advantages of Bayesian and deep neural networks in short text sentiment classification, showcasing high classification accuracy. These findings collectively support the notion that deep CNNs can be a valuable tool for analyzing sentiments in short texts, offering promising results for various applications in NLP.How useful and reliable are personality tests?

Personality tests are valuable tools for organizations in selecting suitable employees, enhancing productivity, and avoiding negligent hiring claims. They are increasingly integrated into recruitment processes, with over 45% of American companies utilizing them. However, concerns arise regarding the faking effect on self-report personality tests, particularly in fields like medical student selection. Despite their predictive validity across various job roles and performance criteria, the selection decision method used can impact issues like adverse impact and differential hiring rates. To ensure the reliability and usefulness of personality tests, they should provide incremental validity over other tools, be tailored to specific job criteria, and consider the potential for faking effects in certain contexts.What are new trends in federated learning in medtech?

Recent trends in federated learning in medical technology focus on enhancing privacy protection and performance. One key trend involves personalized frameworks that consider the heterogeneity of medical data across different institutions, leading to improved generalization and personalization. Another trend addresses challenges like imbalanced datasets and intermittent clients in decentralized healthcare systems, utilizing data augmentation methods and scalable frameworks for better model training. Additionally, a novel approach involves using Federated Learning-based Electronic Health Record sharing schemes to preserve patient data privacy, employing decentralized models and secure data storage techniques like Private InterPlanetary File Systems (IPFS). These trends highlight the ongoing efforts to optimize federated learning for medical applications while safeguarding sensitive health data.What are the most effective strategies for integrating community genetics into primary care settings?

Effective strategies for integrating community genetics into primary care settings involve collaborative efforts and innovative approaches. One key strategy is to incorporate genetic testing into routine medical care through population-based testing, which can help overcome barriers at individual, provider, clinic, and societal levels. Additionally, integrating genetic counselors into primary care teams has shown promise in facilitating access to genetic services and enhancing patient care. Furthermore, implementing a community-empowered co-design process involving hospital stakeholders, community partners, and researchers can help address uncertainties and make informed decisions about the implementation of genetic services, ensuring inclusivity and respect for local communities. By combining these approaches, primary care settings can effectively integrate community genetics, improve patient outcomes, and advance the field of genetic testing.How do personalized packaging preferences vary across different demographic groups, such as age, gender, and income level?

Personalized packaging preferences vary across different demographic groups. Research indicates that female consumers exhibit distinct preferences in packaging design assessments. Age also plays a role, with individuals over 35 years showing varying preferences in packaging design ratings. Income levels further influence preferences, as those earning IDR 3,000,000-5,000,000 have different ratings for specific packaging designs. Additionally, the importance of personalization in packaging is highlighted, showing that tailored packaging can lead to increased consumer satisfaction, repeat purchases, and a willingness to pay extra. Artificial intelligence is leveraged to create personalized packaging systems that cater to diverse consumer needs and brand characteristics, offering various design options and modification functions.How does proper communication help the student's development?

Proper communication plays a crucial role in students' development by enhancing their skills and fostering positive relationships. Effective communication between teachers and students is essential for conveying knowledge, motivating students, and creating a conducive learning environment. Teachers need to possess strong communication skills to engage students, provide emotional support, and respect their ideas, thus promoting a culture of open dialogue and mutual respect. Additionally, the development of students' communication skills, including logical, understandable, and polite communication, is a primary task for teachers, emphasizing the importance of acquiring communicative competence through various educational games and activities. Humanizing pedagogical communication further contributes to harmoniously influencing students' personality development within the educational process. In essence, effective communication nurtures students' socioemotional skills, fosters learning engagement, and cultivates a supportive educational environment conducive to holistic development.How does leaders demonstrate commitment to ethical practices within an organization?

Leaders demonstrate commitment to ethical practices within an organization by embodying ethical values, engaging in ethical discussions, setting clear ethical standards, and utilizing incentives and punishments to foster compliance. Ethical leaders are characterized by fairness, honesty, kindness, and principled behavior, which positively impact employees' emotional intelligence and organizational commitment. They promote normatively appropriate conduct through personal actions, interpersonal relationships, and communication, reinforcing ethical behavior among followers. Ethical leadership is linked to follower affective and normative commitment through perceived organizational support, with follower psychological empowerment moderating this relationship. By demonstrating integrity, trust, respect, and making moral choices, ethical leaders create a culture of ethical behavior that influences organizational outcomes positively.Incomplete utterances were very common, reflecting the overlapping turns and co-construction of meaning that characterized many of the interactions.?

Incomplete utterances play a significant role in interactions, facilitating overlapping turns and co-construction of meaning. They serve as a resource for seeking information, initiating repair, and shaping collaborative productions. In Japanese conversations, co-participant completion involves mobilizing resources to complete another's utterance, highlighting locally emergent structures and unprojected features of turn construction. In educational settings, incomplete utterances are used to solicit displays of knowledge from students, casting them as learners and evidencing prior learning. Additionally, approaches like semantic segmentation have been proposed to enhance incomplete utterance rewriting tasks, achieving superior performance and faster inference times compared to traditional methods. Overall, incomplete utterances are prevalent and essential in various contexts, fostering interactive dynamics and mutual understanding.How moderate to high levels of emotional intelligence among senior high school students?

Moderate to high levels of emotional intelligence among senior high school students have been observed in various studies. The research by Dr. Geetha C found that 41.5% of secondary school students had moderate emotional intelligence, while 22% exhibited high emotional intelligence. Additionally, a study by M. Kumar indicated that the emotional intelligence of higher secondary school students was average, with females showing better emotional intelligence than males. Furthermore, a study in Ibadan, Nigeria, highlighted that self-esteem significantly influenced emotional intelligence among senior secondary students, with those having high self-esteem reporting higher emotional intelligence levels. These findings suggest that emotional intelligence levels among senior high school students can vary, with a notable percentage displaying moderate to high emotional intelligence capabilities.What are the primary objectives of dating app builders in utilizing recommender systems for marketing purposes?

Dating app builders primarily aim to enhance user engagement and satisfaction by utilizing recommender systems for marketing purposes. These systems, such as collaborative filtering algorithms, focus on predicting user preferences based on past behaviors to offer personalized recommendations. Additionally, incorporating reinforcement learning (RL) into these models allows for the integration of direct user feedback through reward signals, leading to a more tailored user experience. Moreover, the concept of multistakeholder recommendation is considered, where the recommender system itself is viewed as a stakeholder, emphasizing the importance of addressing the needs of various stakeholders, including the system, over time to optimize recommendations effectively. Ultimately, the primary objectives revolve around improving user satisfaction, engagement, and overall platform performance through personalized and inclusive recommendations.