A Framework for Automation in Psychotherapy.

This comprehensive literature review delves into the multifaceted realm of customer engagement through the lens of Conversational Marketing and Artificial Intelligence (AI). In an era characterized by digital transformation, businesses are increasingly reliant on innovative strategies to engage customers effectively. The study aims to provide an in-depth exploration of the existing body of research about Conversational Marketing and AI, with a focus on their combined impact on customer engagement.
 The literature review begins by examining the current state of research in Conversational Marketing and AI, identifying key findings and emerging trends. It then extends its focus to the practical implications of these technologies, delving into their application in enhancing customer engagement. The unique challenges and opportunities posed by Conversational Marketing and AI are analysed, paying special attention to the cultural and linguistic factors that shape the customer engagement landscape.
 The study not only offers valuable insights for businesses seeking to optimize their customer engagement strategies but also provides a foundation for policymakers and researchers interested in the ethical, regulatory, and future research considerations surrounding Conversational Marketing and AI. By shedding light on the dynamic interplay between technology and customer engagement, this literature review contributes to a deeper understanding of the evolving customer-business relationship in an increasingly digital world.

Dual humanness and trust in conversational AI: A person-centered approach

BackgroundThe rapid evolution of conversational and generative artificial intelligence (AI) has led to the increased deployment of AI tools in healthcare settings. While these conversational AI tools promise efficiency and expanded access to healthcare services, there are growing concerns ethically, practically and in terms of inclusivity. This study aimed to identify activities which reduce bias in conversational AI and make their designs and implementation more equitable.MethodsA qualitative research approach was employed to develop an analytical framework based on the content analysis of 17 guidelines about AI use in clinical settings. A stakeholder consultation was subsequently conducted with a total of 33 ethnically diverse community members, AI designers, industry experts and relevant health professionals to further develop a roadmap for equitable design and implementation of conversational AI in healthcare. Framework analysis was conducted on the interview data.ResultsA 10-stage roadmap was developed to outline activities relevant to equitable conversational AI design and implementation phases: 1) Conception and planning, 2) Diversity and collaboration, 3) Preliminary research, 4) Co-production, 5) Safety measures, 6) Preliminary testing, 7) Healthcare integration, 8) Service evaluation and auditing, 9) Maintenance, and 10) Termination.DiscussionWe have made specific recommendations to increase conversational AI’s equity as part of healthcare services. These emphasise the importance of a collaborative approach and the involvement of patient groups in navigating the rapid evolution of conversational AI technologies. Further research must assess the impact of recommended activities on chatbots’ fairness and their ability to reduce health inequalities.

BACKGROUND Conversational artificial intelligence (CAI) is emerging as a promising digital technology for mental health care. CAI apps, such as psychotherapeutic chatbots, are available in app stores, but their use raises ethical concerns. OBJECTIVE We aimed to provide a comprehensive overview of ethical considerations surrounding CAI as a therapist for individuals with mental health issues. METHODS We conducted a systematic search across PubMed, Embase, APA PsycINFO, Web of Science, Scopus, the Philosopher’s Index, and ACM Digital Library databases. Our search comprised 3 elements: embodied artificial intelligence, ethics, and mental health. We defined CAI as a conversational agent that interacts with a person and uses artificial intelligence to formulate output. We included articles discussing the ethical challenges of CAI functioning in the role of a therapist for individuals with mental health issues. We added additional articles through snowball searching. We included articles in English or Dutch. All types of articles were considered except abstracts of symposia. Screening for eligibility was done by 2 independent researchers (MRM and TS or AvB). An initial charting form was created based on the expected considerations and revised and complemented during the charting process. The ethical challenges were divided into themes. When a concern occurred in more than 2 articles, we identified it as a distinct theme. RESULTS We included 101 articles, of which 95% (n=96) were published in 2018 or later. Most were reviews (n=22, 21.8%) followed by commentaries (n=17, 16.8%). The following 10 themes were distinguished: (1) safety and harm (discussed in 52/101, 51.5% of articles); the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) explicability, transparency, and trust (n=26, 25.7%), including topics such as the effects of “black box” algorithms on trust; (3) responsibility and accountability (n=31, 30.7%); (4) empathy and humanness (n=29, 28.7%); (5) justice (n=41, 40.6%), including themes such as health inequalities due to differences in digital literacy; (6) anthropomorphization and deception (n=24, 23.8%); (7) autonomy (n=12, 11.9%); (8) effectiveness (n=38, 37.6%); (9) privacy and confidentiality (n=62, 61.4%); and (10) concerns for health care workers’ jobs (n=16, 15.8%). Other themes were discussed in 9.9% (n=10) of the identified articles. CONCLUSIONS Our scoping review has comprehensively covered ethical aspects of CAI in mental health care. While certain themes remain underexplored and stakeholders’ perspectives are insufficiently represented, this study highlights critical areas for further research. These include evaluating the risks and benefits of CAI in comparison to human therapists, determining its appropriate roles in therapeutic contexts and its impact on care access, and addressing accountability. Addressing these gaps can inform normative analysis and guide the development of ethical guidelines for responsible CAI use in mental health care.

Rapid technological advancements make it easier than ever for young children to ‘talk to’ artificial intelligence (AI). Conversational AI models spanning education and entertainment include those specifically designed for early childhood education and care, as well as those not designed for young children but easily accessible by them. It is therefore crucial to critically analyse the ethical implications for children's well-being when a conversation with AI is just a click away. This colloquium flags the ‘empathy gap’ that characterises AI systems that are designed to mimic empathy, explaining the risks of erratic or inadequate responses for child well-being. It discusses key social and technical concerns, tracing how conversational AI may be unable to adequately respond to young children's emotional needs and the limits of natural language processing due to AI's operation within predefined contexts determined by training data. While proficient at recognising patterns and data associations, conversational AI can falter when confronted with unconventional speech patterns, imaginative scenarios or the playful, non-literal language that is typical of children's communication. In addition, societal prejudices can infiltrate AI training data or influence the output of conversational AI, potentially undermining young children's rights to safe, non-discriminatory environments. This colloquium therefore underscores the ethical imperative of safeguarding children and responsible child-centred design. It offers a set of practical considerations for policies, practices and critical ethical reflection on conversational AI for the field of early childhood education and care, emphasising the need for transparent communication, continual evaluation and robust guard rails to prioritise children's well-being.

Conversational artificial intelligence (CAI) is increasingly used in various counseling settings to deliver psychotherapy, provide psychoeducational content, and offer support like companionship or emotional aid. Research has shown that CAI has the potential to effectively address mental health issues when its associated risks are handled with great caution. It can provide mental health support to a wider population than conventional face-to-face therapy, and at a faster response rate and more affordable cost. Despite CAI's many advantages in mental health support, potential users may differ in their willingness to adopt and engage with CAI to support their own mental health. This study focused specifically on dispositional trust in AI and attachment styles, and examined how they are associated with individuals' intentions to adopt CAI for mental health support. A cross-sectional survey of 239 American adults was conducted. Participants were first assessed on their attachment style, then presented with a vignette about CAI use, after which their dispositional trust and subsequent adoption intentions toward CAI counseling were surveyed. Participants had not previously used CAI for digital counseling for mental health support. Dispositional trust in artificial intelligence emerged as a critical predictor of CAI adoption intentions (P<.001), while attachment anxiety (P=.04), rather than avoidance (P=.09), was found to be positively associated with the intention to adopt CAI counseling after controlling for age and gender. These findings indicated higher dispositional trust might lead to stronger adoption intention, and higher attachment anxiety might also be associated with greater CAI counseling adoption. Further research into users' attachment styles and dispositional trust is needed to understand individual differences in CAI counseling adoption for enhancing the safety and effectiveness of CAI-driven counseling services and tailoring interventions. Open Science Framework; https://osf.io/c2xqd.

Conversational Artificial Intelligence has disrupted higher education by fundamentally altering its landscape. Fuelled by natural language processing and machine learning this technology has gained widespread adoption particularly since the release of ChatGPT in November 2022. As universities embrace digital transformation, assessment practices must evolve to align with the capabilities of Artificial Intelligence-driven chatbots and virtual assistants. This paper explores how conversational artificial intelligence impacts higher education, in particular, student assessment. A fundamental shift in assessment and evaluation of student competencies is necessary to not only consider knowledge retention but also critical thinking, communication, and adaptability skills. A review of the literature was conducted to understand how assignments should change due to the emergence of this disruptive technology. Conversational Artificial Intelligence and its application within the higher education context is uncertain, with disparate practices—in terms of ethical consideration and understanding—across the sector. A case study was conducted in which MSc Management students undertaking a specific module were tasked to use three Artificial Intelligence tools in their report writing of a business, to verify the sources and content provided by the Artificial Intelligence tool, and to critically evaluate the process as well as the output received for each prompt. The paper proposes a collaborative approach to navigate the ethical implementation and utilization of conversational Artificial Intelligence in higher education, advocating for the co-creation of guidelines through forums like Knowledge Cafés, stressing the need to rethink student assignments and its assessment and the adoption of artificial intelligence technologies by students for assignments.

The purpose of this study was to design a curriculum of artificial intelligence (AI) application for secondary schools. The learning objective of the curriculum was to allow students to learn the application of conversational AI on a block-based programming platform. Moreover, the empirical study actually implemented the curriculum in the formal learning of a secondary school for a period of six weeks. The study evaluated the learning performance of students who were taught with the cycle of experiential learning in one class, while also evaluating the learning performance of students who were taught with the conventional instruction, which was called the cycle of doing projects. Two factors, learning approach and gender, were taken into account. The results showed that females’ learning effectiveness was significantly better than that of males regardless of whether they used experiential learning or the conventional projects approach. Most of the males tended to be distracted from the conversational AI curriculum because they misbehaved during the conversational AI process. In particular, in their performance using the Voice User Interface with the conventional learning approach, the females outperformed the males significantly. The results of two-way ANCOVA revealed a significant interaction between gender and learning approach on computational thinking concepts. Females with the conventional learning approach of doing projects had the best computational thinking concepts in comparison with the other groups.

Abstract: This chapter delves into the realm of Conversational AI, an innovative fusion of Artificial Intelligence (AI) and Natural Language Processing (NLP) that is transforming the way humans interact with machines. By enabling machines to understand, process, and generate human language in a contextually relevant manner, Conversational AI facilitates seamless, intuitive, and meaningful dialogues between humans and technology. From the foundational technologies like advanced language models and speech recognition to the design principles guiding dialogue management and context handling, this chapter explores the intricacies of building systems capable of human-like conversation. It further examines the wide array of applications spanning customer service, voice assistants, and real-time language translation, illustrating the versatility and transformative potential of Conversational AI. Despite its advancements, the chapter also addresses the challenges faced, including dealing with linguistic ambiguity, ethical considerations, and adapting to language diversity. Looking ahead, it outlines the future directions of Conversational AI, emphasizing the importance of continuous innovation and ethical considerations. This chapter aims to provide a comprehensive overview of Conversational AI, offering insights into its development, applications, challenges, and future prospects, paving the way for a future where AI can communicate as naturally as humans do. Keywords: Conversational AI,Natural Language Processing (NLP),Language Models (GPT, BERT),Speech Recognition,Text-to-Speech (TTS),Speech-to-Text (STT),Dialogue Management,Intent Recognition,Entity Extraction,Context Handling,Ethical Considerations in AI,AI in Customer Service,Voice Assistants,Real-Time Language Translation and Future of Conversational AI.

Artificial intelligence (AI) is predicted to revolutionize most industries in the next few years. This has resulted in a surge in research interest in AI. However, there is a lack of research in the adoption of conversational AI and factors influencing its acceptance. This chapter presents qualitative research exploring perceptions and acceptance of Vietnamese people to conversational AI. Participants of various careers, such as doctors, educators, and content creators, participated in in-depth interviews to share their experiences. Findings show that people are generally more receptive towards using conversational AI in their personal lives, but very skeptical towards the application of it in their work, due to price value, the uncertainty of its performance, and anthropomorphism. People in creative industries appear to be more inclined to use conversational AI if it has a higher level of anthropomorphism. Based on these findings, suggestions for conversational AI businesses and entrepreneurs are proposed.

As artificial intelligence (AI) continues to advance, its integration into education has generated growing interest in conversational AI as a tool to support student learning. Conversational AI systems facilitate natural language interactions between AI and students, serving diverse pedagogical roles, such as tutoring students, providing feedback, and offering learning companionship. However, most existing research has focused on conversational AI in instructor or tutor roles, which may limit students’ opportunities for independent exploration and deep engagement with learning content. Moreover, while prior work highlights the potential of conversational AI to enhance learning through personalization, engagement, and social presence, less is known about how their specific communicative features—such as tone style and emoji use—shape students’ learning experiences. To address these gaps, this study investigates conversational teachable agents (i.e., agents positioned as “students” whom learners teach) and examines how variations in their tone (positive vs. neutral) and emoji use (present vs. absent) influence students’ math learning. The findings reveal that a positive tone and the use of emojis promote students’ affective engagement during interactions with the agents, while a neutral tone and the omission of emoji facilitate deeper cognitive engagement. Furthermore, students’ cognitive engagement significantly predicted their application of procedural and conceptual knowledge during the teaching process. This study underscores the importance of communicative features in educational conversational AI and provides actionable insights for designing more engaging and cognitively supportive teachable agents.

BACKGROUND Conversational artificial intelligence (AI) allows for engaging interactions, however, its acceptability, barriers, and enablers to support patients with atrial fibrillation (AF) are unknown. OBJECTIVE This work stems from the Coordinating Health care with AI–supported Technology for patients with AF (CHAT-AF) trial and aims to explore patient perspectives on receiving support from a conversational AI support program. METHODS Patients with AF recruited for a randomized controlled trial who received the intervention were approached for semistructured interviews using purposive sampling. The 6-month intervention consisted of fully automated conversational AI phone calls (with speech recognition and natural language processing) that assessed patient health and provided self-management support and education. Interviews were recorded, transcribed, and thematically analyzed. RESULTS We conducted 30 interviews (mean age 65.4, SD 11.9 years; 21/30, 70% male). Four themes were identified: (1) interaction with a voice-based conversational AI program (human-like interactions, restriction to prespecified responses, trustworthiness of hospital-delivered conversational AI); (2) engagement is influenced by the personalization of content, delivery mode, and frequency (tailoring to own health context, interest in novel information regarding health, overwhelmed with large volumes of information, flexibility provided by multichannel delivery); (3) improving access to AF care and information (continuity in support, enhancing access to health-related information); (4) empowering patients to better self-manage their AF (encouraging healthy habits through frequent reminders, reassurance from rhythm-monitoring devices). CONCLUSIONS Although conversational AI was described as an engaging way to receive education and self-management support, improvements such as enhanced dialogue flexibility to allow for more naturally flowing conversations and tailoring to patient health context were also mentioned. CLINICALTRIAL Australian New Zealand Clinical Trials Registry ACTRN12621000174886; https://tinyurl.com/3nn7tk72 INTERNATIONAL REGISTERED REPORT RR2-10.2196/34470

This multi-component output brings together two projects that explore our relationship with Artificial Intelligence (AI) technologies. ‘Women Reclaiming AI’ aims to rewrite and reimagine the cultural myths of AI by developing a feminist AI voice assistant, and ‘The Infinite Guide’ is a speculative artwork powered by a conversational AI that explores cultural attitudes towards AI and digital immortality. Both projects manifest as websites, and contextual information comprises video documentation and audience data.

Media use–social capital research has studied traditional and social media use and associated social capital. Still, little is known about whether social capital would be cultivated or damaged by the use of conversational artificial intelligence (AI). This study explores the associations between conversational AI use and various measures of social capital using a territory-wide survey of an online representative sample in Hong Kong ( n = 1022). The results showed that conversational AI users ( n = 398) were more likely to have more offline and online bonding and bridging social capital, social trust, and civic participation than non-users ( n = 624). For the conversational AI users, intensity and frequency of conversational AI use were the positive predictors of the social capital measures. The findings demonstrated larger effect sizes for online bonding and bridging social capital than offline bonding and bridging social capital.

This comprehensive article explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on modern marketing operations. The research delves into key areas where AI and ML are revolutionizing marketing strategies, including hyper-personalization, predictive analytics, and conversational AI. Through an analysis of recent developments and case studies, the article demonstrates how AI-driven personalization significantly enhances customer engagement and relevance, with some implementations showing up to 25% increase in revenue and 15% improvement in customer retention rates. The article also examines the role of predictive analytics in shifting marketing strategies from reactive to proactive approaches, enabling more accurate forecasting of customer behavior, campaign performance, and market trends. Furthermore, the evolution of chatbots and conversational AI is explored, highlighting their capacity to automate lead qualification, scale customer engagement, and gather real-time insights without increasing manual input. The integration of AI in marketing operations is shown to improve campaign management efficiency, enhance personalization capabilities, and facilitate future-focused campaign development. However, the research also addresses the challenges and ethical considerations associated with AI integration in marketing, including data privacy concerns, skill gaps, and the need to balance automation with human creativity. This article provides a comprehensive overview of how AI and ML are reshaping the marketing landscape, offering valuable insights for marketers, researchers, and business leaders navigating this technological revolution.