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      ChatGPT: Literate or intelligent about UN sustainable development goals?

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          Abstract

          Generative AI tools, such as ChatGPT, are progressively transforming numerous sectors, demonstrating a capacity to impact human life dramatically. This research seeks to evaluate the UN Sustainable Development Goals (SDGs) literacy of ChatGPT, which is crucial for diverse stakeholders involved in SDG-related policies. Experimental outcomes from two widely used Sustainability Assessment tests–the UN SDG Fitness Test and Sustainability Literacy Test (SULITEST) ‐ suggest that ChatGPT exhibits high SDG literacy, yet its comprehensive SDG intelligence needs further exploration. The Fitness Test gauges eight vital competencies across introductory, intermediate, and advanced levels. Accurate mapping of these to the test questions is essential for partial evaluation of SDG intelligence. To assess SDG intelligence, the questions from both tests were mapped to 17 SDGs and eight cross-cutting SDG core competencies, but both test questionnaires were found to be insufficient. SULITEST could satisfactorily map only 5 out of 8 competencies, whereas the Fitness Test managed to map 6 out of 8. Regarding the coverage of the Fitness Test and SULITEST, their mapping to the 17 SDGs, both tests fell short. Most SDGs were underrepresented in both instruments, with certain SDGs not represented at all. Consequently, both tools proved ineffective in assessing SDG intelligence through SDG coverage. The study recommends future versions of ChatGPT to enhance competencies such as collaboration, critical thinking, systems thinking, and others to achieve the SDGs. It concludes that while AI models like ChatGPT hold considerable potential in sustainable development, their usage must be approached carefully, considering current limitations and ethical implications.

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          Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

          We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making.
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            ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns

            ChatGPT is an artificial intelligence (AI)-based conversational large language model (LLM). The potential applications of LLMs in health care education, research, and practice could be promising if the associated valid concerns are proactively examined and addressed. The current systematic review aimed to investigate the utility of ChatGPT in health care education, research, and practice and to highlight its potential limitations. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar (published research or preprints) that examined ChatGPT in the context of health care education, research, or practice. A total of 60 records were eligible for inclusion. Benefits of ChatGPT were cited in 51/60 (85.0%) records and included: (1) improved scientific writing and enhancing research equity and versatility; (2) utility in health care research (efficient analysis of datasets, code generation, literature reviews, saving time to focus on experimental design, and drug discovery and development); (3) benefits in health care practice (streamlining the workflow, cost saving, documentation, personalized medicine, and improved health literacy); and (4) benefits in health care education including improved personalized learning and the focus on critical thinking and problem-based learning. Concerns regarding ChatGPT use were stated in 58/60 (96.7%) records including ethical, copyright, transparency, and legal issues, the risk of bias, plagiarism, lack of originality, inaccurate content with risk of hallucination, limited knowledge, incorrect citations, cybersecurity issues, and risk of infodemics. The promising applications of ChatGPT can induce paradigm shifts in health care education, research, and practice. However, the embrace of this AI chatbot should be conducted with extreme caution considering its potential limitations. As it currently stands, ChatGPT does not qualify to be listed as an author in scientific articles unless the ICMJE/COPE guidelines are revised or amended. An initiative involving all stakeholders in health care education, research, and practice is urgently needed. This will help to set a code of ethics to guide the responsible use of ChatGPT among other LLMs in health care and academia.
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              Key competencies in sustainability: a reference framework for academic program development

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                Author and article information

                Contributors
                Role: Writing – original draftRole: Writing – review & editing
                Role: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Writing – original draftRole: Writing – review & editing
                Role: Writing – original draftRole: Writing – review & editing
                Role: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                24 April 2024
                2024
                : 19
                : 4
                : e0297521
                Affiliations
                [1 ] Amrita School of Business, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
                [2 ] Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
                [3 ] Amrita School of Business, Amaravati, Andhra Pradesh, India
                [4 ] Fortune Institute of International Business, New Delhi, India
                Babes-Bolyai University, Cluj-Napoca, ROMANIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-0851-9742
                Article
                PONE-D-23-19769
                10.1371/journal.pone.0297521
                11042716
                38656952
                39a422be-f825-453a-a25c-a4a88af3ea92
                © 2024 Raman et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 June 2023
                : 5 January 2024
                Page count
                Figures: 5, Tables: 9, Pages: 27
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Psychology
                Psychometrics
                Intelligence Tests
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                Psychology
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                Diagnostic Medicine
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                Socioeconomic Aspects of Health
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                Public and Occupational Health
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                Custom metadata
                The anonymized, relevant data are within the paper and its Supporting Information files. Data for SULITEST is available at https://en.sulitest.org/. The Data SDG Fitness Test is available at https://www.unsdglearn.org/microlearning/sdg-fitness-test/.

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