Testing Creativity of ChatGPT in Psychology: Interview with ChatGPT

Uludag, Kadir

doi:10.2139/ssrn.4390872

Cited by 21 publications

(11 citation statements)

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“…It underscores the proficiency of these AI models in accurately identifying mental health conditions, aligning their diagnoses with established clinical practices, and suggesting evidence-based treatment approaches. 41 This not only demonstrates LLMs’ reliability in mental health assessments but also highlights their potential to provide valuable support and guidance to individuals seeking information or coping with depression. This finding also has implications for clinical decision support, whereby LLMs can assist healthcare professionals in their initial assessments and treatment planning.…”

Section: Discussionmentioning

confidence: 82%

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public

Elyoseph,

Levkovich,

Shinan-Altman

2024

Fam Med Com Health

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BackgroundArtificial intelligence (AI) has rapidly permeated various sectors, including healthcare, highlighting its potential to facilitate mental health assessments. This study explores the underexplored domain of AI’s role in evaluating prognosis and long-term outcomes in depressive disorders, offering insights into how AI large language models (LLMs) compare with human perspectives.MethodsUsing case vignettes, we conducted a comparative analysis involving different LLMs (ChatGPT-3.5, ChatGPT-4, Claude and Bard), mental health professionals (general practitioners, psychiatrists, clinical psychologists and mental health nurses), and the general public that reported previously. We evaluate the LLMs ability to generate prognosis, anticipated outcomes with and without professional intervention, and envisioned long-term positive and negative consequences for individuals with depression.ResultsIn most of the examined cases, the four LLMs consistently identified depression as the primary diagnosis and recommended a combined treatment of psychotherapy and antidepressant medication. ChatGPT-3.5 exhibited a significantly pessimistic prognosis distinct from other LLMs, professionals and the public. ChatGPT-4, Claude and Bard aligned closely with mental health professionals and the general public perspectives, all of whom anticipated no improvement or worsening without professional help. Regarding long-term outcomes, ChatGPT 3.5, Claude and Bard consistently projected significantly fewer negative long-term consequences of treatment than ChatGPT-4.ConclusionsThis study underscores the potential of AI to complement the expertise of mental health professionals and promote a collaborative paradigm in mental healthcare. The observation that three of the four LLMs closely mirrored the anticipations of mental health experts in scenarios involving treatment underscores the technology’s prospective value in offering professional clinical forecasts. The pessimistic outlook presented by ChatGPT 3.5 is concerning, as it could potentially diminish patients’ drive to initiate or continue depression therapy. In summary, although LLMs show potential in enhancing healthcare services, their utilisation requires thorough verification and a seamless integration with human judgement and skills.

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Section: Discussionmentioning

confidence: 82%

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public

Elyoseph,

Levkovich,

Shinan-Altman

2024

Fam Med Com Health

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“…A qualitative approach was used, by interviewing ChatGPT. Due to the novelty of the subject, there is still a small number of studies which applied and employed ChatGPT as an interviewee instead of a human subject [27,28]. For example, Iskender [27] asked questions in the field of tourism industry and education and reported that his study originality lies in using ChatGPT as an interviewee.…”

Section: Approach Usedmentioning

confidence: 99%

“…For example, Iskender [27] asked questions in the field of tourism industry and education and reported that his study originality lies in using ChatGPT as an interviewee. Another study [28] interviewed ChatGPT in order to test its creativity in psychology, and the researcher reported that the output was similar in quality to that generated by human experts in certain fields. GPT-3.5 is one of the most advanced large language models [17].…”

Section: Approach Usedmentioning

confidence: 99%

Generative Artificial Intelligence in Higher Education: Exploring Ways of Harnessing Pedagogical Practices with the Assistance of ChatGPT

Nikolopoulou

2024

IJCE

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There is a growing interest in using generative artificial intelligence (AI) for educational purposes within the higher education environments, while AI applications (such as ChatGPT) can transform traditional teaching and learning methods. ChatGPT is an advanced AI tool that generates new content and human-like responses. The purpose of this paper is to use ChatGPT as a research assistant in order to explore ways AI can be harnessed to enhance pedagogical practices in higher education. This is a qualitative study, in which the output-responses generated by ChatGPT provided a starting point for the investigation. AI can be harnessed to enhance pedagogical practices in higher education in various ways including personalized learning, automated assessment and feedback generation, virtual assistants and chatbots, content creation, resource recommendation, time management, language translation and support, research assistance, simulations and virtual labs. Other educational affordances that can strengthen the teaching and learning experience regard collaboration and communication, accessibility and inclusivity, as well as AI literacy. When implementing AI tools such as ChatGPT in higher education, ethical considerations (e.g., data privacy, transparency, accessibility, cultural sensitivity), potential misuses and concerns need to also be addressed. Although ChatGPT can aid the generation of content-ideas for further exploration, it is a complementary-supportive tool, and its output necessitates human evaluation and review. The integration of ChatGPT and other AI tools in the higher educational process/practices has implications for educators, students, design of curricula, and university policy makers.

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“…However, both are dwarfed by GPT-4, released in 2023, which is estimated to use closer to 100 trillion parameters, being trained on a data set representing a non-trivial proportion of all recorded human knowledge [23]. It is because of this that modern LLMs appear to be capable of mimicking and, in many cases, that the quality of their outputs exceed all but the highest achievements in human cognition including inference [24] and creativity [25,26]. Some (e.g., [27,28]) have even tried to make the case that a GAI such as ChatGPT-4 is now at least close to being an AGI (artificial general intelligence), using measures of human intelligence and creativity as evidence.…”

Section: The Distinctive Nature Of Generative Aismentioning

confidence: 99%

The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

Dron

2023

Digital

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This paper analyzes the ways that the widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. Methodologically, the paper applies a theoretical model and grounded argument to present a case that GAIs are different in kind from all previous technologies. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans’ participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique, performed creatively or idiosyncratically). Education may be seen as a technological process for developing these soft and hard techniques in humans to participate in the technologies, and thus the collective intelligence, of our cultures. Unlike all earlier technologies, by embodying that collective intelligence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain; the very things that technologies enabled us to do can now be done by the technologies themselves. Because they replace things that learners have to do in order to learn and that teachers must do in order to teach, the consequences for what, how, and even whether learning occurs are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs. Its distinctive contributions include a novel means of understanding the distinctive differences between GAIs and all other technologies, a characterization of the nature of generative AIs as collectives (forms of collective intelligence), reasons to avoid the use of GAIs to replace teachers, and a theoretically grounded framework to guide adoption of generative AIs in education.

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Testing Creativity of ChatGPT in Psychology: Interview with ChatGPT

Cited by 21 publications

References 0 publications

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public

Generative Artificial Intelligence in Higher Education: Exploring Ways of Harnessing Pedagogical Practices with the Assistance of ChatGPT

The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

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