Value‐sensitive design of chatbots in environmental education: Supporting identity, connectedness, well‐being and sustainability

British Journal of Educational Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

While offering the potential to support learning interactions, emerging AI applications like Large Language Models (LLMs) come with ethical concerns. Grounding technology design in human values can address ethics and ensure adoption. To this end, we apply Value‐Sensitive Design—involving empirical, conceptual technical investigations—to centre development evaluation of LLM‐based chatbots within a high school environmental science curriculum. Representing multiple perspectives expertise, help students refine their causal models climate change's impact on local marine ecosystems, communities individuals. We first perform an empirical investigation leveraging participatory explore that motivate educators engage chatbots. Then, conceptualize emerge from by grounding them research design, values, human‐AI interactions education. Findings illuminate considerations for students' identity development, well‐being, human–chatbot relationships sustainability. further map onto principles illustrate how these guide Our demonstrates conduct contextual, value‐sensitive inquiries emergent technologies educational settings. Practitioner notes What is already known about topic Generative artificial intelligence (GenAI) not only learning, but also raise concerns such as transparency, trust accountability. Value‐sensitive (VSD) presents systematic approach centring design. paper adds VSD education identify central supporting learning. investigations several stages GenAI development: conceptualization, evaluation. Implications practice and/or policy Identity human–AI sustainability are key designing Using stakeholders' generate metrics promote adoption engagement.

Language: Английский

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A Survey on the Use of Large Language Models (LLMs) in Fake News

Future Internet, Journal Year: 2024, Volume and Issue: 16(8), P. 298 - 298

Published: Aug. 19, 2024

The proliferation of fake news and profiles on social media platforms poses significant threats to information integrity societal trust. Traditional detection methods, including rule-based approaches, metadata analysis, human fact-checking, have been employed combat disinformation, but these methods often fall short in the face increasingly sophisticated content. This review article explores emerging role Large Language Models (LLMs) enhancing profiles. We provide a comprehensive overview nature spread followed by an examination existing methodologies. delves into capabilities LLMs generating both profiles, highlighting their dual as tool for disinformation powerful means detection. discuss various applications text classification, verification, contextual demonstrating how models surpass traditional accuracy efficiency. Additionally, covers LLM-based through profile attribute network behavior pattern recognition. Through comparative we showcase advantages over conventional techniques present case studies that illustrate practical applications. Despite potential, challenges such computational demands ethical concerns, which more detail. concludes with future directions research development detection, underscoring importance continued innovation safeguard authenticity online information.

Language: Английский

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An Evaluation of ChatGPT for Nutrient Content Estimation from Meal Photographs

Nutrients, Journal Year: 2025, Volume and Issue: 17(4), P. 607 - 607

Published: Feb. 7, 2025

Background/Objectives: Advances in artificial intelligence now allow combined use of large language and vision models; however, there has been limited evaluation their potential dietary assessment. This study aimed to evaluate the accuracy ChatGPT-4 estimating nutritional content commonly consumed meals using meal photographs derived from national survey data. Methods: Meal (n = 114) were uploaded ChatGPT it was asked identify foods each meal, estimate weight, nutrient for 16 nutrients comparison with known values precision, paired t-tests, Wilcoxon signed rank test, percentage difference, Spearman correlation (rs). Seven dietitians also estimated energy, protein, carbohydrate thirty-eight intraclass (ICC). Results: Comparing actual meals, showed good precision (93.0%) correctly identifying photographs. There agreement weight (p 0.221) small but poor medium < 0.001) meals. 10 0.05). Percentage difference >10% 13 nutrients, underestimating 11 nutrients. Correlations adequate or all rs ranging 0.29 0.83. When comparing dietitians, ICC ranged 0.31 0.67 across Conclusions: performed well foods, weights portion sizes, ranking according content, poorly sizes providing accurate estimates content.

Language: Английский

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Large language models (LLM) in computational social science: prospects, current state, and challenges

Social Network Analysis and Mining, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 9, 2025

Language: Английский

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Bridging LMS and Generative AI: Dynamic Course Content Integration (DCCI) for Connecting LLMs to Course Content – The Ask ME Assistant

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

The integration of Large Language Models (LLMs) with Learning Management Systems (LMSs) has the potential to enhance task automation and accessibility in education. However, hallucination where LLMs generate inaccurate or misleading information remains a significant challenge. This study introduces Dynamic Course Content Integration (DCCI) mechanism, which dynamically retrieves integrates course content curriculum from Canvas LMS into LLM-powered assistant, Ask ME. By employing prompt engineering structure retrieved within LLM’s context window, DCCI ensures accuracy, relevance, contextual alignment, mitigating hallucination. To evaluate DCCI’s effectiveness, ME’s usability, broader student perceptions AI education, mixed-methods approach was employed, incorporating user satisfaction ratings structured survey. Results pilot indicate high (4.614/5), students recognizing ability provide timely contextually relevant responses for both administrative course-related inquiries. Additionally, majority agreed that reduced platform-switching, improving engagement, comprehension. AI’s role reducing classroom hesitation fostering self-directed learning intellectual curiosity also highlighted. Despite these benefits positive perception tools, concerns emerged regarding over-reliance on AI, accuracy limitations, ethical issues such as plagiarism student-teacher interaction. These findings emphasize need strategic implementation, safeguards, pedagogical framework prioritizes human-AI collaboration over substitution. contributes AI-enhanced education by demonstrating how context-aware retrieval mechanisms like improve LLM reliability educational engagement while ensuring responsible integration.

Language: Английский

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ChatGPT's Role in Healthcare: What Does Twitter Tell Us About Public Sentiment? (Preprint)

Published: July 18, 2024

The advent of AI-based large language models (LLMs) in 2022 has given rise to a plethora discussions within the academic community. discourse is multifaceted, with enthusiastic users lauding sophisticated chatbots' potential assist writing tasks. Nevertheless, critics have warned that cultural and ethical implications relying on LLMs may be too costly bear. literature spans multiple fields often focuses overlapping themes, such as their appropriate integration, analytical performance, practical benefits for users. However, there notable gap examining nature these discussions. societal impact ultimately depends users' opinions, technophiles luddites shape trajectory technological adoption. To address this, our study assessed public opinion perception most popular LLM available: ChatGPT. In current work, we aimed understand how opinions sentiment shared by general contrast among researchers other field experts gain broader view future direction healthcare. We utilized Academic Twitter API retrieve tweets search terms “ChatGPT AND (health OR healthcare hospital physician nurse nursing patient)”. This data collection process was executed period between December 1st, 2022, day after ChatGPT became publicly available, March 20th, 2023. Our analysis consisted three phases: 1) Human-labeled tweet classification; 2) Algorithm-based 3) Structural Topic Model distinctly group content. Using an innovative approach integrates Syuzhet package GPT-3.5, achieved 84% accuracy classification. Further investigation using structural topic modeling revealed eight distinct topics covering both optimistic concerned perspectives. results indicated predominantly positive towards integration healthcare, especially areas patient care decision making. concerns were raised mental health support communication. highlights significant transform while also addressing challenges. It further contributes ongoing scholarly concerning advantages disadvantages domain.

Language: Английский

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Did You Tell a Deadly Lie? Evaluating Large Language Models for Health Misinformation Identification

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 391 - 405

Published: Nov. 26, 2024

Language: Английский

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ChatGPT’s Role in Healthcare: What Does X/Twitter Tell Us About Public Sentiment? (Preprint)

JMIR Infodemiology, Journal Year: 2024, Volume and Issue: unknown

Published: July 18, 2024

Language: Английский

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