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Temas: inteligencia artificial

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This research assesses the effectiveness of state-of-the-art large language models (LLMs), including ChatGPT, Llama, Aya, Jais, and ACEGPT, in the task of Arabic automated essay scoring (AES) using the AR-AES dataset. It explores various evaluation methodologies, including zero-shot, few-shot in context learning, and fine-tuning, and examines the influence of instruction-following capabilities through the inclusion of marking guidelines within the prompts. A mixed-language prompting strategy, integrating English prompts with Arabic content, was implemented to improve model comprehension and performance. Among the models tested, ACEGPT demonstrated the strongest performance across the...

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To address the complexity and high costs of developing listening tests for test-takers of varying proficiency levels, this study investigates the capabilities of an OpenAI's large language model, ChatGPT 4, in developing listening assessments. Employing prompt engineering and fine-tuning of prompts, the study specifically focuses on creating listening scripts and test items using ChatGPT 4 for test-takers across a spectrum of proficiency levels (academic, low, intermediate, and advanced). For comparability, the 24 topics of these scripts were selected from topics found in academic listening tests. We conducted two types of analyses to evaluate the quality of the output. First, we performed...

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Metodología: revisión bibliográfica

Temas: inteligencia artificial

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The adoption of Generative AI (GenAI) and Large Language Models (LLMs) in healthcare education has accelerated rapidly since 2023, yet the evidence base for their use in Scenario-Based Learning (SBL) remains fragmented. This systematic review synthesises empirical research on GenAI applications across scenario-based, case-based, problem-based, and simulation-based learning in healthcare education. Following the PRISMA 2020 guidelines, five databases were searched on 9 November 2025 for peer-reviewed studies published from January 2023 onwards. Of 1151 initial records, 23 studies met the inclusion criteria. Quality was assessed using the Mixed Methods Appraisal Tool (MMAT). Thematic...

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Generative Large Language Models (LLMs) demonstrate impressive results in different writing tasks and have already attracted much attention from researchers and practitioners. However, there is limited research to investigate the capability of generative LLMs for reflective writing. To this end, in the present study, we have extensively reviewed the existing literature and selected 9 representative prompting strategies for ChatGPT – the chatbot based on state-of-art generative LLMs to generate a diverse set of reflective responses, which are combined with student-written reflections. Next, those responses were evaluated by experienced teaching staff following a theory-aligned...

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Generative Artificial Intelligence (GenAI) chatbots continue to amaze users worldwide with their rapid improvements. These tools possess vast general knowledge and can thus be used in various fields, including education. However, before rolling out these models in pedagogical applications, it is fundamental to understand whether the information provided is reliable and if any of the currently available chatbots are best suited for domain-specific tasks. The objective of this study is to thoroughly investigate these aspects in a specific domain, fermentation, with the overarching goal of providing guidelines to students and teachers to select the best GenAI assistant. To achieve this goal,...

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In the realm of education, crafting appropriate questions for examinations is a meticulous and time-consuming task that is crucial for assessing students' understanding of the subject matter. This paper explores the potential of leveraging large language models (LLMs) to automate question generation in the educational domain. Specifically, we focus on generating educational questions from contexts extracted from school-level textbooks. Our study aims to prompt LLMs such as GPT-4 Turbo, GPT-3.5 Turbo, Llama-2-70B, Llama-3.1-405B, and Gemini Pro to generate a complete set of questions for each context, potentially streamlining the question generation process for educators. We performed a...

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Several reports in education have called for transforming physics learning environments by promoting sensemaking of real-world scenarios in light of curricular ideas. Recent advancements in Generative-Artificial Intelligence have garnered increasing traction in educators' community by virtue of its potential to transform STEM learning. In this exploratory study, we adopt a mixed-methods approach in comparatively examining student- and AI-generated responses to two different formats of a physics problem through the theoretical lenses of sensemaking and mechanistic reasoning. The student data is derived from think-aloud interviews of introductory students and the AI data comes from ChatGPT's...

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Student course evaluations contain rich qualitative feedback in the form of comments written in response to open-ended questions. However, this qualitative data, which may be more nuanced and detailed than quantitative ratings, is often unexamined in both administrative and research settings due to the labor-intensive nature of manual analysis. We investigate whether large language models (LLMs), including BERT, RoBERTa, and OpenAI model variants, can accurately replicate human judgments of sentiment in these comments. We compare masked and generative language models, using both naïve and fine-tuned approaches, to analyze a curated dataset of 1000 de-identified course evaluation responses....

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Intelligent tutoring systems have demonstrated effectiveness in teaching formal propositional logic proofs, but their reliance on template-based explanations limits their ability to provide personalized student feedback. While large language models (LLMs) offer promising capabilities for dynamic feedback generation, they risk producing hallucinations or pedagogically unsound explanations. We evaluated the stepwise accuracy of LLMs in constructing multi-step symbolic logic proofs, comparing six prompting techniques across four state-of-the-art LLMs on 358 propositional logic problems. Results show that DeepSeek-V3 achieved superior performance with upto 86.7 % accuracy on stepwise proof...

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Advancements in generative AI, such as large language models (LLMs), may serve as a potential solution to the burdensome task of essay grading often faced by language education teachers. Yet, the validity and reliability of leveraging LLMs for automatic essay scoring (AES) in language education is not well understood. To address this, we evaluated the cross-sectional and longitudinal validity and reliability of four prominent LLMs, Google's PaLM 2, Anthropic's Claude 2, and OpenAI's GPT-3.5 and GPT-4, for the AES of English language learners' writing. 119 essays taken from an English language placement test were assessed twice by each LLM, on two separate occasions, as well as by a pair of...

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