grandes modelos de lenguaje

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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,...

Can large language models meet the challenge of generating school-level questions?

Subhankar Maity, Aniket Deroy, Sudeshna Sarkar (2025)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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...

Student and AI responses to physics problems examined through the lenses of sensemaking and mechanistic reasoning

Amogh Sirnoorkar, Dean Zollman, James T. Laverty (2024)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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...

LLM sentiment quantification reveals selective alignment with human course-evaluation raters

Joyce W. Lacy, Chi Nnoka, Zachary Jock (2026)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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....

The promise and limits of LLMs in constructing proofs and hints for logic problems in intelligent tutoring systems

Sutapa Dey Tithi, Arun Kumar Ramesh, Clara DiMarco (2025)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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...

Large language models and automated essay scoring of English language learner writing: Insights into validity and reliability

Austin Pack, Alex Barrett, Juan Escalante (2024)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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...

When the algorithm enters the classroom: A critical integrative review of large language models, nursing education structural gaps, and the reconstitution of professional identity

Yanjie Sun, Hongzhou Li, Xiaohui Tao (2026)

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Tipo: artículo

Metodología: revisión bibliográfica

Temas: inteligencia artificial

Resumen:

Large language models (LLMs) are being integrated into nursing education at a pace that outstrips the field's capacity to evaluate their consequences. Existing syntheses have mapped what LLMs can do in nursing contexts. Among those identified in this review, none has examined how their integration affects the professional development processes through which nursing expertise is formed. This critical integrative review addresses that gap.Following Whittemore and Knafl’s (2005) five-stage framework, we systematically searched four databases (PubMed/MEDLINE, Web of Science, Scopus, CINAHL) for studies published between January 2017 and December 2025, yielding 489 included studies from 47...

Evaluating the capability of large language models in characterising relational feedback: A comparative analysis of prompting strategies

Wei Dai, Yixin Cheng, Ahmad Ari Aldino (2025)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Relational feedback is increasingly recognised for its crucial role in enhancing student-instructor relationships and promoting the assimilation of feedback. Despite its significance, no studies have tried to develop automated methods to analyse written feedback for properties of relational feedback to promote its use at scale and assist feedback providers with their relational feedback practices. This automated analysis of relational feedback can be performed as a classification task. However, traditional machine and deep learning methods for text classification typically require extensive human labelling and pose a significant challenge for educators and researchers lacking machine...

Performance of ChatGPT on the US fundamentals of engineering exam: Comprehensive assessment of proficiency and potential implications for professional environmental engineering practice

Vinay Pursnani, Yusuf Sermet, Musa Kurt (2023)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

In recent years, advancements in artificial intelligence (AI) have led to the development of large language models like GPT-4, demonstrating potential applications in various fields, including education. This study investigates the feasibility and effectiveness of using ChatGPT, a GPT-4 based model, in achieving satisfactory performance on the Fundamentals of Engineering (FE) Environmental Exam. This study further shows a significant improvement in the model's accuracy when answering FE exam questions through noninvasive prompt modifications, substantiating the utility of prompt modification as a viable approach to enhance AI performance in educational contexts. Furthermore, the findings...

Exploring the design of LLM-powered question generation for deaf and hard of hearing learners

Si Chen, Shuxu Huffman, Qingxiaoyang Zhu (2026)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Deaf and Hard of Hearing (DHH) learners face unique learning challenges, often due to a lack of customized educational materials that address their specific needs. This study explores the potential of Large Language Models (LLMs) to generate personalized quiz questions to enhance the video-based learning experiences of DHH students. We iteratively designed, developed, and evaluated a prototype that combines LLMs to generate questions with the characteristics of the DHH learners in focus. The two unique question generation strategies include Visual Questions, which identify video segments where visual information might be misinterpreted, and Emotion Questions, which highlight moments where...