Chatbots and AI in Education (AIEd) tools: The good, the bad, and the ugly

Journal of Applied Learning & Teaching, Journal Year: 2023, Volume and Issue: 6(2)

Published: Sept. 26, 2023

As the application of Artificial Intelligence (AI) continues to permeate various sectors, educational landscape is no exception. Several AI in education (AIEd) applications, like chatbots, present an intriguing array opportunities and challenges. This paper provides in-depth exploration use role research, focusing on benefits (the good) potential pitfalls bad ugly) associated with deployment chatbots other AIEDs. The explored include personalised learning, facilitation administrative tasks, enriched research capabilities, provision a platform for collaboration. These advantages are balanced against downsides, such as job displacement, misinformation, plagiarism, erosion human connection. Ethical considerations, particularly concerning data privacy, bias reinforcement, digital divide, also examined. Conclusions drawn from this analysis stress importance striking balance between capabilities elements education, well developing comprehensive ethical frameworks contexts.

Language: Английский

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Assessing the Effectiveness of ChatGPT in Delivering Mental Health Support: A Qualitative Study

Journal of Multidisciplinary Healthcare, Journal Year: 2024, Volume and Issue: Volume 17, P. 461 - 471

Published: Jan. 1, 2024

Background: Artificial Intelligence (AI) applications are widely researched for their potential in effectively improving the healthcare operations and disease management. However, research trend shows that these also have significant negative implications on service delivery. Purpose: To assess use of ChatGPT mental health support. Methods: Due to novelty unfamiliarity technology, a quasi-experimental design was chosen this study. Outpatients from public hospital were included sample. A two-week experiment followed by semi-structured interviews conducted which participants used Semi-structured with 24 individuals conditions. Results: Eight positive factors (psychoeducation, emotional support, goal setting motivation, referral resource information, self-assessment monitoring, cognitive behavioral therapy, crisis interventions, psychotherapeutic exercises) four (ethical legal considerations, accuracy reliability, limited assessment capabilities, cultural linguistic considerations) associated Conclusion: It is important carefully consider ethical, accuracy, challenges develop appropriate strategies mitigate them order ensure safe effective AI-based like Keywords: ChatGPT, artificial intelligence, mentally-ill patients, anxiety

Language: Английский

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From “online brains” to “online lives”: understanding the individualized impacts of Internet use across psychological, cognitive and social dimensions

World Psychiatry, Journal Year: 2024, Volume and Issue: 23(2), P. 176 - 190

Published: May 10, 2024

In response to the mass adoption and extensive usage of Internet-enabled devices across world, a major review published in this journal 2019 examined impact Internet on human cognition, discussing concepts ideas behind "online brain". Since then, online world has become further entwined with fabric society, extent which we use such technologies continued grow. Furthermore, research evidence ways affects mind advanced considerably. paper, sought draw upon latest data from large-scale epidemiological studies systematic reviews, along randomized controlled trials qualitative recently emerging topic, order now provide multi-dimensional overview impacts psychological, cognitive societal outcomes. Within this, detail empirical how effects differ according various factors as age, gender, types. We also new examining more experiential aspects individuals' lives, understand specifics their interactions Internet, lifestyle, determine benefits or drawbacks time. Additionally, explore nascent but intriguing areas culturomics, artificial intelligence, virtual reality, augmented reality are changing our understanding can interact brain behavior. Overall, importance taking an individualized approach mental health, cognition social functioning is clear. emphasize need for guidelines, policies initiatives around make full available neuroscientific, behavioral levels presented herein.

Language: Английский

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Large Language Models for Chatbot Health Advice Studies

JAMA Network Open, Journal Year: 2025, Volume and Issue: 8(2), P. e2457879 - e2457879

Published: Feb. 4, 2025

Importance There is much interest in the clinical integration of large language models (LLMs) health care. Many studies have assessed ability LLMs to provide advice, but quality their reporting uncertain. Objective To perform a systematic review examine variability among peer-reviewed evaluating performance generative artificial intelligence (AI)–driven chatbots for summarizing evidence and providing advice inform development Chatbot Assessment Reporting Tool (CHART). Evidence Review A search MEDLINE via Ovid, Embase Elsevier, Web Science from inception October 27, 2023, was conducted with help sciences librarian yield 7752 articles. Two reviewers screened articles by title abstract followed full-text identify primary accuracy AI-driven (chatbot studies). then performed data extraction 137 eligible studies. Findings total were included. Studies examined topics surgery (55 [40.1%]), medicine (51 [37.2%]), care (13 [9.5%]). focused on treatment (91 [66.4%]), diagnosis (60 [43.8%]), or disease prevention (29 [21.2%]). Most (136 [99.3%]) evaluated inaccessible, closed-source did not enough information version LLM under evaluation. All lacked sufficient description characteristics, including temperature, token length, fine-tuning availability, layers, other details. describe prompt engineering phase study. The date querying reported 54 (39.4%) (89 [65.0%]) used subjective means define successful chatbot, while less than one-third addressed ethical, regulatory, patient safety implications LLMs. Conclusions Relevance In this chatbot studies, heterogeneous may CHART standards. Ethical, considerations are crucial as grows

Language: Английский

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Evaluating Large Language Models for the National Premedical Exam in India: Comparative Analysis of GPT-3.5, GPT-4, and Bard

JMIR Medical Education, Journal Year: 2023, Volume and Issue: 10, P. e51523 - e51523

Published: Oct. 30, 2023

Background Large language models (LLMs) have revolutionized natural processing with their ability to generate human-like text through extensive training on large data sets. These models, including Generative Pre-trained Transformers (GPT)-3.5 (OpenAI), GPT-4 and Bard (Google LLC), find applications beyond processing, attracting interest from academia industry. Students are actively leveraging LLMs enhance learning experiences prepare for high-stakes exams, such as the National Eligibility cum Entrance Test (NEET) in India. Objective This comparative analysis aims evaluate performance of GPT-3.5, GPT-4, answering NEET-2023 questions. Methods In this paper, we evaluated 3 mainstream LLMs, namely Google Bard, questions related exam. The NEET were provided these artificial intelligence responses recorded compared against correct answers official answer key. Consensus was used all models. Results It evident that passed entrance test flying colors (300/700, 42.9%), showcasing exceptional performance. On other hand, GPT-3.5 managed meet qualifying criteria, but a substantially lower score (145/700, 20.7%). However, (115/700, 16.4%) failed criteria did not pass test. demonstrated consistent superiority over subjects. Specifically, achieved accuracy rates 73% (29/40) physics, 44% (16/36) chemistry, 51% (50/99) biology. Conversely, attained an rate 45% (18/40) 33% (13/26) 34% (34/99) consensus metric showed matching between well had higher incidences being correct, at 0.56 0.57, respectively, which stood 0.42. When considered together, reached highest 0.59. Conclusions study’s findings provide valuable insights into emerged most accurate model, highlighting its potential educational applications. Cross-checking across may result confusion (as duos or trio) tend agree only little half responses. Using one will consensus. results underscore suitability exams positive impact education. Additionally, study establishes benchmark evaluating enhancing LLMs’ tasks, promoting responsible informed use diverse environments.

Language: Английский

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A Promising Start and Not a Panacea: ChatGPT's Early Impact and Potential in Medical Science and Biomedical Engineering Research

Annals of Biomedical Engineering, Journal Year: 2023, Volume and Issue: 52(5), P. 1131 - 1135

Published: Aug. 4, 2023

Language: Английский

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ChatGPT: perspectives from human–computer interaction and psychology

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: June 18, 2024

The release of GPT-4 has garnered widespread attention across various fields, signaling the impending adoption and application Large Language Models (LLMs). However, previous research predominantly focused on technical principles ChatGPT its social impact, overlooking effects human–computer interaction user psychology. This paper explores multifaceted impacts interaction, psychology, society through a literature review. author investigates ChatGPT’s foundation, including Transformer architecture RLHF (Reinforcement Learning from Human Feedback) process, enabling it to generate human-like responses. In terms studies significant improvements GPT models bring conversational interfaces. analysis extends psychological impacts, weighing potential mimic human empathy support learning against risks reduced interpersonal connections. commercial domains, discusses applications in customer service services, highlighting efficiency challenges such as privacy issues. Finally, offers predictions recommendations for future development directions impact relationships.

Language: Английский

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Large Language Models for Mental Health Applications: A Systematic Review (Preprint)

JMIR Mental Health, Journal Year: 2024, Volume and Issue: 11, P. e57400 - e57400

Published: Sept. 3, 2024

Background Large language models (LLMs) are advanced artificial neural networks trained on extensive datasets to accurately understand and generate natural language. While they have received much attention demonstrated potential in digital health, their application mental particularly clinical settings, has generated considerable debate. Objective This systematic review aims critically assess the use of LLMs specifically focusing applicability efficacy early screening, interventions, settings. By systematically collating assessing evidence from current studies, our work analyzes models, methodologies, data sources, outcomes, thereby highlighting challenges present, prospects for use. Methods Adhering PRISMA (Preferred Reporting Items Systematic Reviews Meta-Analyses) guidelines, this searched 5 open-access databases: MEDLINE (accessed by PubMed), IEEE Xplore, Scopus, JMIR, ACM Digital Library. Keywords used were (mental health OR illness disorder psychiatry) AND (large models). study included articles published between January 1, 2017, April 30, 2024, excluded languages other than English. Results In total, 40 evaluated, including 15 (38%) conditions suicidal ideation detection through text analysis, 7 (18%) as conversational agents, 18 (45%) applications evaluations health. show good effectiveness detecting issues providing accessible, destigmatized eHealth services. However, assessments also indicate that risks associated with might surpass benefits. These include inconsistencies text; production hallucinations; absence a comprehensive, benchmarked ethical framework. Conclusions examines inherent risks. The identifies several issues: lack multilingual annotated experts, concerns regarding accuracy reliability content, interpretability due “black box” nature LLMs, ongoing dilemmas. clear, framework; privacy issues; overreliance both physicians patients, which could compromise traditional medical practices. As result, should not be considered substitutes professional rapid development underscores valuable aids, emphasizing need continued research area. Trial Registration PROSPERO CRD42024508617; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=508617

Language: Английский

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The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis

JMIR Mental Health, Journal Year: 2024, Volume and Issue: 11, P. e56569 - e56569

Published: April 27, 2024

Abstract Large language model (LLM)–powered services are gaining popularity in various applications due to their exceptional performance many tasks, such as sentiment analysis and answering questions. Recently, research has been exploring potential use digital health contexts, particularly the mental domain. However, implementing LLM-enhanced conversational artificial intelligence (CAI) presents significant ethical, technical, clinical challenges. In this viewpoint paper, we discuss 2 challenges that affect of CAI for individuals with issues, focusing on case patients depression: tendency humanize lack contextualized robustness. Our approach is interdisciplinary, relying considerations from philosophy, psychology, computer science. We argue humanization hinges reflection what it means simulate “human-like” features LLMs role these systems should play interactions humans. Further, ensuring contextualization robustness requires considering specificities production depression, well its evolution over time. Finally, provide a series recommendations foster responsible design deployment therapeutic support depression.

Language: Английский

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Healthcare professionals and the public sentiment analysis of ChatGPT in clinical practice

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 7, 2025

To explore the attitudes of healthcare professionals and public on applying ChatGPT in clinical practice. The successful application practice depends technical performance critically perceptions non-healthcare healthcare. This study has a qualitative design based artificial intelligence. was divided into five steps: data collection, cleaning, validation relevance, sentiment analysis, content analysis using K-means algorithm. comprised 3130 comments amounting to 1,593,650 words. dictionary method showed positive negative emotions such as anger, disgust, fear, sadness, surprise, good, happy emotions. Healthcare prioritized ChatGPT's efficiency but raised ethical accountability concerns, while valued its accessibility emotional support expressed worries about privacy misinformation. Bridging these perspectives by improving reliability, safeguarding privacy, clearly defining role is essential for practical integration

Language: Английский

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