Artificial Intelligence In Psychology Research Articles

The Role of Artificial Intelligence in Psychology

The Role of Artificial Intelligence in Psychology

Advancing personalized human-robot interaction in the smart world through emotional AI in entertainment robots

The rise of innovative methods has increased the implementation of robotics into daily existence. Improving human-robot interactions (HRI) to create customised relationships is essential to promote consumer acceptability and pleasure in this setting. This article investigates using behavioural artificial intelligence in the modern environment framework to customise HRI. A higher degree of involvement and camaraderie will be attained by giving entertaining robots (enbots) the capacity to sense, comprehend, and react to our feelings. Using psychological artificial intelligence methods, such machines can modify their actions, appearances, and replies based on personal desires and psychological conditions, resulting in increasingly customised and engaging customer interactions. Nevertheless, even such enbots remain unable to identify humans' feelings. This study proposes a method for adjusting enbot features based on an employee's psychological condition to accomplish this. The technique uses Electroencephalography (EEG) technologies to digitise and interpret human feelings. The enbot's settings will alter immediately to sustain the individual's feelings within a desired span, boosting mutual trust and respect between the two entities.

Psychological AI: A critical analysis of capabilities, limitations, and ramifications

As artificial intelligence (AI) continues to advance, machine learning and natural language processing (NLP) offer new possibilities in various areas of psychology, such as therapy, diagnostics, treatment planning, demographic profiling, sentiment analysis, and consumer psychology. However, AI research in psychology is still lacking. This study aims to explore the specific applications of AI in the field of psychology. Firstly, the article delves into the efficacy and cost-effectiveness of chatbots in delivering cognitive behavioral therapy (CBT). Next, the article explores the current state of AI in detecting mental health disorders, highlighting the use of digital footprints as a diagnostic tool. Furthermore, the research investigates how AI can assist medical professionals in developing focused treatment plans. It analyzes studies that aim to optimize treatment strategies using AI technologies. The article also examines the influence of demographic characteristics on our understanding and application of psychology. Lastly, the article touches on the use of AI in analyzing sentiment to understand and predict consumer behavior. Ethical implications of using AI-driven psychological assessments are also discussed. It concludes with a summary of key findings and offers a glimpse into potential future developments in this field.

Artificial intelligence (AI) in psychology: a commentary on AI’s emerging role and the ensuing conversation

This brief commentary explores the opportunities and challenges presented by the increasing prevalence of artificial intelligence in the field of psychology in South Africa. Artificial intelligence has the potential to revolutionise teaching and learning, research, content production, and professional services, but it also presents some challenges to academic and professional psychology in South Africa. While some generative artificial intelligence can produce written work, such as assignments, literature reviews, and theses, they currently cannot replace human reasoning and the critical thinking abilities required to argue a particular point (at this stage). Artificial intelligence chatbots can also act as teaching assistants and even provide complex psychological interventions such as cognitive-behavioural therapy. In research and publication, artificial intelligence can increase efficiency and provide new insights and perspectives by detecting patterns and relationships that may have been overlooked by human researchers. However, the use of artificial intelligence raises ethical concerns, particularly around ownership and authorship of artificial intelligence–generated content, potential biases, and errors. The commentary concludes that as artificial intelligence technology continues to evolve, and with the human–artificial intelligence partnership continuing to unfold, it is important to recognise the risks associated with its use in academic writing and ensure that psychology students develop appropriate research skills.

Psychological AI: Designing Algorithms Informed by Human Psychology

Psychological artificial intelligence (AI) applies insights from psychology to design computer algorithms. Its core domain is decision-making under uncertainty, that is, ill-defined situations that can change in unexpected ways rather than well-defined, stable problems, such as chess and Go. Psychological theories about heuristic processes under uncertainty can provide possible insights. I provide two illustrations. The first shows how recency—the human tendency to rely on the most recent information and ignore base rates—can be built into a simple algorithm that predicts the flu substantially better than did Google Flu Trends’s big-data algorithms. The second uses a result from memory research—the paradoxical effect that making numbers less precise increases recall—in the design of algorithms that predict recidivism. These case studies provide an existence proof that psychological AI can help design efficient and transparent algorithms.

Uses of Artificial Intelligence in Psychology

Machine learning has a new landscape for humanity in the area of artificial intelligence (AI). Artificial intelligence (AI) approaches have recently been developed to support mental health professionals, primarily psychiatrists, psychologists, and clinicians, with decision-making based on patients' historical data (e.g., clinical history, behavioral data, social media use, etc.). This article reviews developments in artificial intelligence (AI) technologies and their current and potential applications in clinical psychological practice. Issues associated with AI in the context of clinical practice, the potential risk for job loss among mental health professionals, and other ramifications associated with the advancement of AI technology are discussed. The advancement of AI technologies and their application in psychological practice have important implications that can be expected to transform the mental health care field. Psychologists and other mental health care professionals have an essential part to play in the development, evaluation, and ethical use of AI technologies.

Preparing Psychologists and Social Workers for the Daily Use of AI

A daily use of Artificial Intelligence (AI) is becoming a fact in many fields today, and two of them are psychology and social work. At the same time as AI systems are used for predicting psychological treatments and for decisions in social welfare, higher education has few AI courses for these professions. Moreover, there are several examples in these fields where AI can make unethical decisions that need to be corrected by humans. To better understand the possibilities and challenges of AI in psychology and social work, professional users of AI services need a tailored education on how the underlying technology works. The aim of this paper is to present a project concept for the design and evaluation of a novel course in AI for professional development in psychology and social work. For the design and development of the course the guiding research question should be: What are the strengths and challenges with contemporary AI techniques regarding prediction, adaptivity and decision systems? The suggested AI course should be given as a technology enhanced online training to enable the idea of anytime and anywhere for full-time working participants. Course content and activities are divided into the four separate sections of: 1) The history of AI structured around the 'Three waves of AI', with o focus on the current third wave. 2) A section with a focus on AI techniques for prediction and adaptivity. Underlying techniques such as machine learning, neural networks, and deep learning will be conceptually described and discussed, but not on a detailed level. 3) An elaborated discussion on the relevance, usefulness and trust, and the at the difference between AI-based decision systems and AI-based decision support systems. 4) Finally, the fourth section should comprise the ethical aspects of AI, and discuss transparency and Explainable AI. An innovative approach of the project is to use a neuroscientific assessment of the education to understand how the education changes brain function relevant to evaluate AI based decision. This should be complemented with a qualitative evaluation based on semi-structured interviews.

Preparing Psychologists and Social Workers for the Daily Use of AI

A daily use of Artificial Intelligence (AI) is becoming a fact in many fields today, and two of them are psychology and social work. At the same time as AI systems are used for predicting psychological treatments and for decisions in social welfare, higher education has few AI courses for these professions. Moreover, there are several examples in these fields where AI can make unethical decisions that need to be corrected by humans. To better understand the possibilities and challenges of AI in psychology and social work, professional users of AI services need a tailored education on how the underlying technology works. The aim of this paper is to present a project concept for the design and evaluation of a novel course in AI for professional development in psychology and social work. For the design and development of the course the guiding research question should be: What are the strengths and challenges with contemporary AI techniques regarding prediction, adaptivity and decision systems? The suggested AI course should be given as a technology enhanced online training to enable the idea of anytime and anywhere for full-time working participants. Course content and activities are divided into the four separate sections of: 1) The history of AI structured around the 'Three waves of AI', with o focus on the current third wave. 2) A section with a focus on AI techniques for prediction and adaptivity. Underlying techniques such as machine learning, neural networks, and deep learning will be conceptually described and discussed, but not on a detailed level. 3) An elaborated discussion on the relevance, usefulness and trust, and the at the difference between AI-based decision systems and AI-based decision support systems. 4) Finally, the fourth section should comprise the ethical aspects of AI, and discuss transparency and Explainable AI. An innovative approach of the project is to use a neuroscientific assessment of the education to understand how the education changes brain function relevant to evaluate AI based decision. This should be complemented with a qualitative evaluation based on semi-structured interviews.

The Ethics of Psychological Artificial Intelligence: Clinical Considerations

Psychological artificial intelligence has a growing research base but often overlooks ethical considerations. Drawing from a review of the literature, the experiences of three counselor educators, and an industry insider, this article names six issues relevant to psychological artificial intelligence. The clinical implications of each issue are discussed, some at the national level, some at the practice level. Suggestions for the counseling profession, with an emphasis on prevention, are offered.

Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence?

Psychologists with their expertise in statistics and regarding human perception and behavior can contribute valuable insights to the development of innovative and useful artificial intelligence (AI) systems. Therefore, we need to raise attention and curiosity for AI and foster the willingness to engage with it among psychology students. This requires identifying approaches to integrate a general understanding of AI technology into formal psychological training and education. This study investigated to what extent psychology students currently accept and use AI and what affects their perception and usage. Therefore, an AI acceptance model based on established technology acceptance models was developed and tested in a sample of 218 psychology students. An acceptable fit with the data was found for an adapted version. Perceived usefulness and ease of use were most predictive for the students’ attitude towards AI; attitude itself, as well as perceived usefulness, social norm, and perceived knowledge, were predictors for the intention to use AI. In summary, we identified relevant factors for designing AI training approaches in psychology curricula. In this way, possible restraints regarding the use of AI can be reduced and its beneficial opportunities exploited in psychological contexts.

Optimizing Students' Mental Health and Academic Performance: AI-Enhanced Life Crafting.

One in three university students experiences mental health problems during their study. A similar percentage leaves higher education without obtaining the degree for which they enrolled. Research suggests that both mental health problems and academic underperformance could be caused by students lacking control and purpose while they are adjusting to tertiary education. Currently, universities are not designed to cater to all the personal needs and mental health problems of large numbers of students at the start of their studies. Within the literature aimed at preventing mental health problems among students (e.g., anxiety or depression), digital forms of therapy recently have been suggested as potentially scalable solutions to address these problems. Integrative psychological artificial intelligence (AI) in the form of a chatbot, for example, shows great potential as an evidence-based solution. At the same time, within the literature aimed at improving academic performance, the online life-crafting intervention in which students write about values and passions, goals, and goal-attainment plans has shown to improve the academic performance and retention rates of students. Because the life-crafting intervention is delivered through the curriculum and doesn’t bear the stigma that is associated with therapy, it can reach larger populations of students. But life-crafting lacks the means for follow-up or the interactiveness that online AI-guided therapy can offer. In this narrative review, we propose to integrate the current literature on chatbot interventions aimed at the mental health of students with research about a life-crafting intervention that uses an inclusive curriculum-wide approach. When a chatbot asks students to prioritize both academic as well as social and health-related goals and provides personalized follow-up coaching, this can prevent -often interrelated- academic and mental health problems. Right on-time delivery, and personalized follow-up questions enhance the effects of both -originally separated- intervention types. Research on this new combination of interventions should use design principles that increase user-friendliness and monitor the technology acceptance of its participants.

Psychological Artificial Intelligence Service, Tess: Delivering On-demand Support to Patients and Their Caregivers: Technical Report.

This technical report highlights how one mental health chatbot, or psychological artificial intelligence service named Tess, has been customized to deliver on-demand support for caregiving professionals, patients, and family caregivers at a non-profit organization. This low-cost, user friendly, and highly customizable service allows emotional support to be scaled to thousands of people at a single time. The following report describes the phased approach to implementing Tess in order to reach staff, caregivers, and patients across Canada and the United States.

Using Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial.

BackgroundStudents in need of mental health care face many barriers including cost, location, availability, and stigma. Studies show that computer-assisted therapy and 1 conversational chatbot delivering cognitive behavioral therapy (CBT) offer a less-intensive and more cost-effective alternative for treating depression and anxiety. Although CBT is one of the most effective treatment methods, applying an integrative approach has been linked to equally effective posttreatment improvement. Integrative psychological artificial intelligence (AI) offers a scalable solution as the demand for affordable, convenient, lasting, and secure support grows.ObjectiveThis study aimed to assess the feasibility and efficacy of using an integrative psychological AI, Tess, to reduce self-identified symptoms of depression and anxiety in college students.MethodsIn this randomized controlled trial, 75 participants were recruited from 15 universities across the United States. All participants completed Web-based surveys, including the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and Positive and Negative Affect Scale (PANAS) at baseline and 2 to 4 weeks later (T2). The 2 test groups consisted of 50 participants in total and were randomized to receive unlimited access to Tess for either 2 weeks (n=24) or 4 weeks (n=26). The information-only control group participants (n=24) received an electronic link to the National Institute of Mental Health’s (NIMH) eBook on depression among college students and were only granted access to Tess after completion of the study.ResultsA sample of 74 participants completed this study with 0% attrition from the test group and less than 1% attrition from the control group (1/24). The average age of participants was 22.9 years, with 70% of participants being female (52/74), mostly Asian (37/74, 51%), and white (32/74, 41%). Group 1 received unlimited access to Tess, with daily check-ins for 2 weeks. Group 2 received unlimited access to Tess with biweekly check-ins for 4 weeks. The information-only control group was provided with an electronic link to the NIMH’s eBook. Multivariate analysis of covariance was conducted. We used an alpha level of .05 for all statistical tests. Results revealed a statistically significant difference between the control group and group 1, such that group 1 reported a significant reduction in symptoms of depression as measured by the PHQ-9 (P=.03), whereas those in the control group did not. A statistically significant difference was found between the control group and both test groups 1 and 2 for symptoms of anxiety as measured by the GAD-7. Group 1 (P=.045) and group 2 (P=.02) reported a significant reduction in symptoms of anxiety, whereas the control group did not. A statistically significant difference was found on the PANAS between the control group and group 1 (P=.03) and suggests that Tess did impact scores.ConclusionsThis study offers evidence that AI can serve as a cost-effective and accessible therapeutic agent. Although not designed to appropriate the role of a trained therapist, integrative psychological AI emerges as a feasible option for delivering support.Trial RegistrationInternational Standard Randomized Controlled Trial Number: ISRCTN61214172; https://doi.org/10.1186/ISRCTN61214172.

Behaviorismens överdrivna död: En liten anmärkning till psykologins historia

Kristjánsson, M. (2004). The exaggerated death of behaviorism. Nordisk Psykologi, 56 (1) 40–49. Introspectionism in psychology was founded on a particular aspect of mentalism, namely the claim that the mind is predominantly a conscious private world, observable directly only by the possessor of that mind. Behaviorism was defined in opposition to introspectionism. In recent years, it has been widely claimed that mentalism was resurrected in the 1950s and 60s with the rise of cognitive psychology and cognitive science, after decades of behavioristic repression. Nevertheless, introspective psychology was not restored. Neither was the paradigm case of the mental, intentionality, because intentionality has always been, and continues to be, an integral, even if problematic, part of psychology. Advocates for artificial intelligence in psychology hoped to explain and naturalize intentionality, not to ultimately found psychology or cognitive science on mentalism.

Artificial Intelligence in Psychology

(1992). Artificial Intelligence in Psychology. Journal of Research on Computing in Education: Vol. 25, No. 1, pp. 134-136.

Review of Artificial Intelligence in Psychology: Interdisciplinary Essays.

Review of Artificial Intelligence in Psychology: Interdisciplinary Essays.