Human Therapist Research Articles

Exploring accessibility, user experience and engagement of digital media among older patients with depression: a pilot and observational screening study protocol of the DiGA4Aged study

IntroductionThe prevalence of mental health problems is increasing worldwide, particularly in the vulnerable group of older people. The limited availability of therapists, long wait periods and increasing shortage of healthcare...

Gender differences in preferences for mental health apps in the general population – a choice-based conjoint analysis from Germany

BackgroundMen and women differ in the mental health issues they typically face. This study aims to describe gender differences in preferences for mental health treatment options and specifically tries to identify participants who prefer AI-based therapy over traditional face-to-face therapy.MethodA nationally representative sample of 2,108 participants (53% female) aged 18 to 74 years completed a choice-based conjoint analysis (CBCA). Within the CBCA, participants evaluated twenty choice sets, each describing three treatment variants in terms of provider, content, costs, and waiting time.ResultsCosts (relative importance [RI] = 55%) emerged as the most critical factor when choosing between treatment options, followed by provider (RI = 31%), content (RI = 10%), and waiting time (RI = 4%). Small yet statistically significant differences were observed between women and men. Women placed greater importance on the provider, while men placed greater importance on cost and waiting time. Age and previous experience with psychotherapy and with mental health apps were systematically related to individual preferences but did not alter gender effects. Only a minority (approximately 8%) of participants preferred AI-based treatment to traditional therapy.ConclusionsOverall, affordable mental health treatments performed by human therapists are consistently favored by both men and women. AI-driven mental health apps should align with user preferences to address psychologist shortages. However, it is uncertain whether they alone can meet the rising demand, highlighting the need for alternative solutions.

A Novel Cognitive Behavioral Therapy-Based Generative AI Tool (Socrates 2.0) to Facilitate Socratic Dialogue: Protocol for a Mixed Methods Feasibility Study.

Digital mental health tools, designed to augment traditional mental health treatments, are becoming increasingly important due to a wide range of barriers to accessing mental health care, including a growing shortage of clinicians. Most existing tools use rule-based algorithms, often leading to interactions that feel unnatural compared with human therapists. Large language models (LLMs) offer a solution for the development of more natural, engaging digital tools. In this paper, we detail the development of Socrates 2.0, which was designed to engage users in Socratic dialogue surrounding unrealistic or unhelpful beliefs, a core technique in cognitive behavioral therapies. The multiagent LLM-based tool features an artificial intelligence (AI) therapist, Socrates, which receives automated feedback from an AI supervisor and an AI rater. The combination of multiple agents appeared to help address common LLM issues such as looping, and it improved the overall dialogue experience. Initial user feedback from individuals with lived experiences of mental health problems as well as cognitive behavioral therapists has been positive. Moreover, tests in approximately 500 scenarios showed that Socrates 2.0 engaged in harmful responses in under 1% of cases, with the AI supervisor promptly correcting the dialogue each time. However, formal feasibility studies with potential end users are needed. This mixed methods study examines the feasibility of Socrates 2.0. On the basis of the initial data, we devised a formal feasibility study of Socrates 2.0 to gather qualitative and quantitative data about users' and clinicians' experience of interacting with the tool. Using a mixed method approach, the goal is to gather feasibility and acceptability data from 100 users and 50 clinicians to inform the eventual implementation of generative AI tools, such as Socrates 2.0, in mental health treatment. We designed this study to better understand how users and clinicians interact with the tool, including the frequency, length, and time of interactions, users' satisfaction with the tool overall, quality of each dialogue and individual responses, as well as ways in which the tool should be improved before it is used in efficacy trials. Descriptive and inferential analyses will be performed on data from validated usability measures. Thematic analysis will be performed on the qualitative data. Recruitment will begin in February 2024 and is expected to conclude by February 2025. As of September 25, 2024, overall, 55 participants have been recruited. The development of Socrates 2.0 and the outlined feasibility study are important first steps in applying generative AI to mental health treatment delivery and lay the foundation for formal feasibility studies. DERR1-10.2196/58195.

An Overview of Tools and Technologies for Anxiety and Depression Management Using AI

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including “artificial intelligence”, “applications”, “mental health”, “anxiety”, “LLMs” and “depression”. Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for overreliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings.

Comparing perceived empathy and intervention strategies of an AI chatbot and human psychotherapists in online mental health support

AbstractBackgroundGiven the growing potential of artificial intelligence (AI) to enhance therapeutic interventions and work with a large number of people, it is crucial to understand AI's differences, advantages and limitations compared with human therapists.MethodologyThis study compared an AI chatbot's and human psychotherapists' capabilities in responding to mental health enquiries in an online forum. One hundred and fifty questions from a Reddit forum, where qualified therapists provide mental health support, were selected. Each question received two responses: one from a human therapist and one generated by AI. These 300 responses were coded and compared based on empathy indices and psychological intervention types.ResultsThe results indicated that AI scored significantly higher in perspective‐taking (V = 12,957, p < .001, r = .53) and empathic concern (V = 17,400, p < .001, r = .60). AI was more likely to use supportive interventions (42.2% vs. 21.8%) and slightly more likely to aim for insight‐driven change (6.41% vs. 4.57%). In contrast, human therapists were more inclined to provide advice and information (47.84% vs. 39.81%), explore dysfunctional patterns (19.95% vs. 10.29%) and ask clarifying questions (4.09% vs. 0.97%). A chi‐squared test confirmed significant differences between the intervention types used by AI and human therapists (χ2[8, N = 300] = 67.80, p < .001).DiscussionThese findings highlight AI's potential for basic perceived empathic support, especially in administrative tasks and therapist training. However, the study's scope is limited to single interactions, without the consideration of the nuanced communication available to human therapists through speech, facial expressions and body language.

Chatbots, Robots, and the Ethics of Automating Psychotherapy

Recent developments in artificial intelligence—AI--have caused considerable discussion among both philosophers of technology and psychotherapists. In particular, the question of whether or not new forms of AI will complement or even replace traditional psychotherapists has emerged as a major contemporary debate. This debate is not entirely new, as it has its origins in the Turing Test of 1950, and an early psychotherapy chatbot named Eliza, developed in 1966 at MIT. However, recent developments in AI technology, coupled with long waiting lists and variable access to psychotherapists have raised the question of machine psychotherapists in an urgent manner. Already, there are psychotherapy apps that one can download onto a standard smartphone and use in lieu of a human psychotherapist. In the near future, this simulacrum of a human therapist may be enhanced by the use of android therapists, programed to duplicate the knowledge and behavior of human therapists instantly, appealing to convenience or self-gratification through technology. This raises a host of ethical questions: can such beings be equally effective, and if so, ought we to reason in a consequentialist manner in their favor so as to increase accessibility and reduce costs through technology? Would there be a psychological difference between automated and potentially anthropomorphic therapy and genuine human therapy, if only a subtle one? Even if a chatbot or robot therapist is transparently such, is there an element of emotional manipulation and potential dishonesty in this interaction? Can the security of clients and their data be thus secured? I will argue that key aspects of chatbot psychotherapy present major ethical and clinical challenges in these areas, although transparent forms of it should not be legally banned. Keywords: chatbots, technological momentum, automation, psychotherapy, trust, anthropomorphism, data

Human-Human vs Human-AI Therapy: An Empirical Study

In many nations, demand for mental health services currently outstrips supply, especially in the area of talk-based psychological interventions. Within this context, chatbots (software applications designed to simulate conversations with human users) are increasingly explored as potential adjuncts to traditional mental healthcare service delivery with a view to improving accessibility and reducing waiting times. However, the effectiveness and acceptability of such chatbots remains under-researched. This study evaluates mental health professionals’ perceptions of Pi, a relational Artificial Intelligence (AI) chatbot, in the early stages of the psychotherapeutic process (problem exploration). We asked 63 therapists to assess therapy transcripts between a human client and Pi (human-AI) versus traditional therapy transcripts between therapists and clients (human-human). Therapists were unable to reliably discriminate between human-AI and human-human therapy transcripts. Therapists were accurate only 53.9% of the time, no better than chance, and rated the human-AI transcripts as higher quality on average. This study has potentially profound implications for the treatment of mental health problems, adding tentative support for the use of relational AI chatbots in providing initial assistance for mild to moderate psychological issues, especially when access to human therapists is constrained.

The Machine Speaks: Conversational AI and the Importance of Effort to Relationships of Meaning.

The focus of debates about conversational artificial intelligence (CAI) has largely been on social and ethical concerns that arise when we speak to machines-what is gained and what is lost when we replace our human interlocutors, including our human therapists, with AI. In this viewpoint, we focus instead on a distinct and growing phenomenon: letting machines speak for us. What is at stake when we replace our own efforts at interpersonal engagement with CAI? The purpose of these technologies is, in part, to remove effort, but effort has enormous value, and in some cases, even intrinsic value. This is true in many realms, but especially in interpersonal relationships. To make an effort for someone, irrespective of what that effort amounts to, often conveys value and meaning in itself. We elaborate on the meaning, worth, and significance that may be lost when we relinquish effort in our interpersonal engagements as well as on the opportunities for self-understanding and growth that we may forsake.

Does a lack of emotions make chatbots unfit to be psychotherapists?

Mental health chatbots (MHCBs) designed to support individuals in coping with mental health issues are rapidly advancing. Currently, these MHCBs are predominantly used in commercial rather than clinical contexts, but this might change soon. The question is whether this use is ethically desirable. This paper addresses a critical yet understudied concern: assuming that MHCBs cannot have genuine emotions, how this assumption may affect psychotherapy, andconsequentlythe quality of treatment outcomes. We argue that if MHCBs lack emotions, they cannot have genuine (affective) empathy or utilise countertransference. Consequently, this gives reason to worry that MHCBs are (a) more liable to harm and (b) less likely to benefit patients than human therapists. We discuss some responses to this worry and conclude that further empirical research is necessary to determine whether these worries are valid. We conclude that, even if these worries are valid, it does not mean that we should never use MHCBs. By discussing the broader ethical debate on the clinical use of chatbots, we point towards how further research can help us establish ethical boundaries for how we should use mental health chatbots.

Improving the social communication skills of preschoolers with autism spectrum disorder: robot-based intervention on Picture Exchange Communication System use

Objectives Children diagnosed with autism spectrum disorder (ASD) often exhibit challenges in social communication. Picture Exchange Communication System (PECS) is effective in enhancing the social skills of children with ASD, and interventions using social robots are potentially beneficial training tools. However, it is uncertain whether robots can facilitate more effective training for children with ASD compared to human therapists. Methods In the present study, we compared the effectiveness of robot-based and human-based interventions on enhancing the social communication behaviors in young children diagnosed with ASD. The feasibility and efficacy of integrating the PECS in robotic systems were evaluated using a single-case design. Eight children with ASD were randomly assigned to either a robot-based group (N = 4) or a human-based group (N = 4). Participants in the two groups underwent phases I–III of the PECS protocol facilitated by either a human therapist or a robot therapist. Changes in social communication behaviors were assessed in each child after intervention using a single-case design. Results Half of the participants in the robot-based intervention group showed significant improvement in both verbal and nonverbal communication behaviors after a two-month PECS intervention. A higher proportion of children in the robot-based intervention group showed significantly enhanced social communication skills encompassing both verbal and nonverbal behaviors than in the human-based intervention group. Conclusions This study demonstrates the effectiveness of robot-based PECS intervention in enhancing verbal and nonverbal communication behaviors in young children with ASD. These findings present novel intervention tools for early intervention educators engaged in supporting children with ASD.

AI, automation and psychotherapy – A proposed model for losses and gains in the automated therapeutic encounter

ABSTRACT With artificial intelligence (AI) and automation embedded in most aspects of life from healthcare to transportation, this article conceptualizes how increasing automation will benefit or inhibit the therapeutic encounter. Five levels of a model are outlined, from the human analogue to the fully autonomous virtual (AI) therapist. The levels are referenced against key elements of the therapeutic encounter known to contribute to an effective therapeutic outcome, from the working alliance, to collaborative elements, to a confidential setting. Losses and gains are discussed through these and other aspects, such as the neurobiological, and the capability to notice what is not there in the process as well as what is, around the client’s aim of change or awareness. The objective is not to pit human therapists against non-human systems, but to propose a human-centred framework for the integration of AI into clinical reality, by introducing a taxonomy model of human–machine interaction (HMI), to see how each contributes, and to evaluate areas of risk and harm. Questions are raised around ethics, therapeutic process and crucially the very nature of what it is to be in a therapeutic encounter for a human client together with a human or AI therapist.

Enhanced Muscle Activation Using Robotic Assistance Within the Electromechanical Delay: Implications for Rehabilitation?

Robotic rehabilitation has been shown to match the effects of conventional physical therapy on motor function for patients with neurological diseases. Rehabilitation robots have the potential to reduce therapists' workload in time-intensive training programs as well as perform actions that are not replicable by human therapists. We investigated the effects of one such modality that cannot be achieved by a human therapist: assistance and resistance within the electromechanical delay between muscle activation and muscle contraction during arm extension. We found increased muscle activation when providing robotic assistance within this electromechanical delay. Assistance provided within this delay moves the participant's arm quicker than their own muscle and increases the subsequent peak voluntary muscle activation compared to normal arm extension by 68.97 ± 80.05 % (SE = 0.021; p = 0.007 ). This is surprising since all previous literature shows that muscle activation either decreases or does not change when participants receive robotic assistance. As a consequence, traditional robotic rehabilitation incrementally reduces assistance as the patient improves to maintain levels of muscle activation which is suggested to be important for neuronal repair. The present result may enable therapists to no longer have to choose between providing assistance or increasing muscle activation. Instead, therapists may be able to provide assistance while also increasing muscle activation.

AI Chatbots And Psychotherapy: A Match Made In Heaven?

Dear Editor, Artificial Intelligence (AI) is revolutionizing psychotherapy by combating its inaccessibility.1 AI chatbots and conversational agents are among the most promising integrations in psychiatry and psychology. By harnessing the power of machine learning and artificial intelligence (Figure I), AI chatbots and conversational agents can provide a level of interaction and support previously unattainable in the landscape of mental health. In this letter, I will explore some of their numerous benefits. Chatbots have evolved from early examples such as ELIZA, a programmable natural language processing program, and PARRY, a program that mimicked the behaviour of a human with schizophrenia, to modern ones such as Woebot, Tess, and Replika, which offer cognitive behavioural therapy, sessions, and self-understanding, respectively. Some chatbots such as Kaspar and Nao are also embodied in physical robots, which help children with autism spectrum disorder (ASD) learn social skills and facial recognition. AI chatbots can address the challenges of mental health care, such as the shortage of professionals2, especially in underdeveloped countries; the stigma of seeking help, which is prevalent in many cultures; the cost of therapy, which can be prohibitive for many people; and the limitations of human therapists, who can have variable attention spans, attention-to-detail, and memory capacities. AI chatbots can also offer benefits such as accessibility, anonymity, personalization, and effectiveness. Studies have shown that AI chatbots can improve the outcomes and experiences of mental health patients in various ways, such as enhancing the performance of those with ASD with robotic "therapists"1, increasing their disclosure of sensitive information to chatbots2, and reducing their distress after talking about their problems with conversational agents3. The benefits of AI chatbots for mental health are clear. However, they also face many challenges that limit their usability and reliability. They may lack the emotional skills of human therapists and may not be accountable for their diagnoses and recommendations. They may also not be based on sound mental health knowledge and evidence, as technology companies may not have expertise and experience in this field. Therefore, technology companies and mental health professionals must collaborate and adopt a rational approach to develop and evaluate chatbots. Only then can chatbots realize their full potential and complement existing mental health services safely and ethically.

The future of automated capture of social kinesic signals for psychiatric purposes

This article considers how computer vision can be enlisted for biomedical applications, specifically the measurement, data analytics and treatment of psychiatric disorders. Often, youngsters are too afraid or embarrassed to disclose their emotional and mental problems to human therapists. An AI system can be utilized not only to collect data in a non-threatening ongoing manner and record patient's temporal psychophysiological state but also to analyze and output the periodic results, it may be an efficient and effective means for therapists to plan treatments. We report on various tools for analyzing social kinesic signals for emotional and physiological states. Only one, AVATAR (and its predecessor SPECIES), both records a patient's state and also outputs an analysis that flags problem areas for therapists. In this way, automated tools can augment human observation and judgment.

Digital cognitive behavioral therapy for insomnia on depression and anxiety: a systematic review and meta-analysis

Despite research into the development of digital cognitive behavioral therapy for insomnia (dCBT-I), research into the outcomes of dCBT-I on insomnia and the associated clinical conditions of depression and anxiety have been limited. The PubMed, PsycINFO (Ovid), Embase, and Cochrane databases were searched for randomized controlled trials (RCTs) on adult patients with insomnia also having reported measures of depressive or anxiety symptoms. In total, 2504 articles were identified after duplicate removal, and 22 RCTs were included in the final meta-analysis. At the post-treatment assessment, the dCBT-I group had a small to moderate effect in alleviating depressive (standardized mean difference (SMD) = −0.42; 95% CI: −0.56, −0.28; p < 0.001; k = 21) and anxiety symptoms (SMD = −0.29; 95% CI: −0.40, −0.19; p < 0.001; k = 18), but had a large effect on sleep outcome measures (SMD = −0.76; 95% CI: −0.95, −0.57; p < 0.001; k = 22). When considering treatment adherence, the treatment effects of those in the high adherent groups identified a more robust outcome, showing greater effect sizes than those in the low adherent groups for depression, anxiety, and sleep outcomes. Furthermore, additional subgroup analysis on studies that have used the fully automated dCBT-I treatment without the support of human therapists reported significant treatment effects for depression, anxiety, and sleep outcomes. The results demonstrated that digital intervention for insomnia yielded significant effects on alleviating depressive and anxiety symptoms as well as insomnia symptoms. Specifically, the study demonstrated significant effects on the above symptoms when considering treatment adherence and implementing fully automated dCBT-I.

Analysis of the therapeutic interaction provided by a humanoid robot serving stroke survivors as a therapeutic assistant for arm rehabilitation.

Objective: To characterize a socially active humanoid robot's therapeutic interaction as a therapeutic assistant when providing arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis or arm ability training (AAT) for mild arm paresis) to stroke survivors when using the digital therapeutic system Evidence-Based Robot-Assistant in Neurorehabilitation (E-BRAiN) and to compare it to human therapists' interaction. Methods: Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., ABT [n = 9] or AAT [n = 8]) using E-BRAiN over a course of nine sessions and twenty-one other stroke survivors receiving arm rehabilitation sessions (i.e., ABT [n = 6] or AAT [n = 15]) in a conventional 1:1 therapist-patient setting. Analysis of therapeutic interaction: Therapy sessions were videotaped, and all therapeutic interactions (information provision, feedback, and bond-related interaction) were documented offline both in terms of their frequency of occurrence and time used for the respective type of interaction using the instrument THER-I-ACT. Statistical analyses: The therapeutic interaction of the humanoid robot, supervising staff/therapists, and helpers on day 1 is reported as mean across subjects for each type of therapy (i.e., ABT and AAT) as descriptive statistics. Effects of time (day 1 vs. day 9) on the humanoid robot interaction were analyzed by repeated-measures analysis of variance (rmANOVA) together with the between-subject factor type of therapy (ABT vs. AAT). The between-subject effect of the agent (humanoid robot vs. human therapist; day 1) was analyzed together with the factor therapy (ABT vs. AAT) by ANOVA. Main results and interpretation: The overall pattern of the therapeutic interaction by the humanoid robot was comprehensive and varied considerably with the type of therapy (as clinically indicated and intended), largely comparable to human therapists' interaction, and adapted according to needs for interaction over time. Even substantially long robot-assisted therapy sessions seemed acceptable to stroke survivors and promoted engaged patients' training behavior. Conclusion: Humanoid robot interaction as implemented in the digital system E-BRAiN matches the human therapeutic interaction and its modification across therapies well and promotes engaged training behavior by patients. These characteristics support its clinical use as a therapeutic assistant and, hence, its application to support specific and intensive restorative training for stroke survivors.

Feasibility, coverage, and inter-rater reliability of the assessment of therapeutic interaction by a humanoid robot providing arm rehabilitation to stroke survivors using the instrument THER-I-ACT.

Objective: The instrument THERapy-related InterACTion (THER-I-ACT) was developed to document therapeutic interactions comprehensively in the human therapist-patient setting. Here, we investigate whether the instrument can also reliably be used to characterise therapeutic interactions when a digital system with a humanoid robot as a therapeutic assistant is used. Methods: Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis [n = 9] or arm ability training (AAT) for mild arm paresis [n = 8]) using the digital therapy system E-BRAiN over a course of nine sessions. Analysis of the therapeutic interaction: A total of 34 therapy sessions were videotaped. All therapeutic interactions provided by the humanoid robot during the first and the last (9th) session of daily training were documented both in terms of their frequency and time used for that type of interaction using THER-I-ACT. Any additional therapeutic interaction spontaneously given by the supervising staff or a human helper providing physical assistance (ABT only) was also documented. All ratings were performed by two trained independent raters. Statistical analyses: Intraclass correlation coefficients (ICCs) were calculated for the frequency of occurrence and time used for each category of interaction observed. Results: Therapeutic interactions could comprehensively be documented and were observed across the dimensions provision of information, feedback, and bond-related interactions. ICCs for therapeutic interaction category assessments from 34 therapy sessions by two independent raters were high (ICC ≥0.90) for almost all categories of the therapeutic interaction observed, both for the occurrence frequency and time used for categories of therapeutic interactions, and both for the therapeutic interaction performed by the robot and, even though much less frequently observed, additional spontaneous therapeutic interactions by the supervisory staff and a helper being present. The ICC was similarly high for an overall subjective rating of the concentration and engagement of patients (0.87). Conclusion: Therapeutic interactions can comprehensively and reliably be documented by trained raters using the instrument THER-I-ACT not only in the traditional patient-therapist setting, as previously shown, but also in a digital therapy setting with a humanoid robot as the therapeutic agent and for more complex therapeutic settings with more than one therapeutic agent being present.

Utilizing an Emotional Robot Capable of Lip-Syncing in Robot-Assisted Speech Therapy Sessions for Children with Language Disorders.

This study scrutinizes the impacts of utilizing a socially assistive robot, the RASA robot, during speech therapy sessions for children with language disorders. Two capabilities were developed for the robotic platform to enhance children-robot interactions during speech therapy interventions: facial expression communication (containing recognition and expression) and lip-syncing. Facial expression recognition was conducted by training several well-known CNN architectures on one of the most extensive facial expressions databases, the AffectNet database, and then modifying them using the transfer learning strategy performed on the CK+ dataset. The robot's lip-syncing capability was designed in two steps. The first step was concerned with designing precise schemes of the articulatory elements needed during the pronunciation of the Persian phonemes (i.e., consonants and vowels). The second step included developing an algorithm to pronounce words by disassembling them into their components (including consonants and vowels) and then morphing them into each other successively. To pursue the study's primary goal, two comparable groups of children with language disorders were considered, the intervention and control groups. The intervention group attended therapy sessions in which the robot acted as the therapist's assistant, while the control group only communicated with the human therapist. The study's first purpose was to compare the children's engagement while playing a mimic game with the affective robot and the therapist, conducted via video coding. The second objective was to assess the efficacy of the robot's presence in the speech therapy sessions alongside the therapist, accomplished by administering the Persian Test of Language Development, Persian TOLD. According to the first scenario, playing with the affective robot is more engaging than playing with the therapist. Furthermore, the statistical analysis of the study's results indicates that participating in robot-assisted speech therapy (RAST) sessions enhances children with language disorders' achievements in comparison with taking part in conventional speech therapy interventions.

Computer-Assisted CBT May Help Alleviate Depression

Computer-Assisted CBT May Help Alleviate Depression

Popularity of Mental Health Chatbots Grows

Popularity of Mental Health Chatbots Grows