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  • grandes modelos de lenguaje

Bibliografía: grandes modelos de lenguaje

Nº de publicaciones: 102
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Large language models for education: An open-source paradigm for automated Q&A in the graduate classroom
Ryann M. Perez, Marie Shimogawa, Yanan Chang (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar ✕

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

Large Language Models (LLMs) offer scalable educational support, but face barriers regarding accuracy, cost, and learning depth. To interrogate these limitations, we developed the Teaching Assistant for Specialized Knowledge (TAsk), a retrieval-augmented generation enabled and educator curated pipeline. In this nine-week pilot study (N = 33 participants), we deployed TAsk in a graduate-level biological chemistry course. We compared TAsk against human expert teaching assistants (TAs) using blinded review process and analyzed inquiry depth. We observed three major findings related to potential pedagogical decisions and educational theory. First, TAsk delivered effective feedback that was...

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Evaluating the performance of ChatGPT and GPT-4o in coding classroom discourse data: A study of synchronous online mathematics instruction
Simin Xu, Xiaowei Huang, Chung Kwan Lo (2024)
Computers & Education: Artificial Intelligence
Detalles Cerrar ✕

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

High-quality instruction is essential to facilitating student learning, prompting many professional development (PD) programmes for teachers to focus on improving classroom dialogue. However, during PD programmes, analysing discourse data is time-consuming, delaying feedback on teachers' performance and potentially impairing the programmes' effectiveness. We therefore explored the use of ChatGPT (a fine-tuned GPT-3.5 series model) and GPT-4o to automate the coding of classroom discourse data. We equipped these AI tools with a codebook designed for mathematics discourse and academically productive talk. Our dataset consisted of over 400 authentic talk turns in Chinese from synchronous online...

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Level-specific feedback generation for scene descriptions via fine-tuning multimodal large language models
ZhiWei Xie, Tse-Tin Chan, Philip L.H. Yu (2025)
Computers & Education: Artificial Intelligence
Detalles Cerrar ✕

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

Scene description tasks effectively enhance students' English writing skills in contextual settings, facilitating the establishment of authentic situational connections. However, evaluating descriptive quality and providing accurate, level-appropriate feedback present significant challenges. Although Multimodal Large Language Models (MLLMs) have demonstrated strong capabilities in vision-language tasks, their generated feedback for scene description tasks often remains generic. It fails to account for students' educational stages. To address this limitation, we construct a novel level-specific feedback dataset for scene description tasks. This dataset is constructed using GPT-4o with...

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AI literacy for ethical use of chatbot: Will students accept AI ethics?
Yusuke Kajiwara, Kouhei Kawabata (2024)
Computers & Education: Artificial Intelligence
Detalles Cerrar ✕

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

In AI literacy education, there are few examples of education based on AI ethical principles, and limited knowledge exists regarding curriculum design that incorporates AI ethical principles and its effects. Therefore, in this study, we propose a curriculum that teaches the ethical use of large language models (LLM) such as ChatGPT and verify its impact on educational effectiveness and technology acceptance among students aged 12 to 24. The validation results show that the proposed curriculum particularly contributes to the understanding of LLM concepts and their ethical use in decision support. We also demonstrate that experience using ChatGPT influences the level of understanding of...

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Building AI companions that prioritise learning over performance
Hassan Khosravi, Dragan Gašević, Shazia Sadiq (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar ✕

Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

Texto completo

Generative AI, currently most visible in education through large language models (LLMs), is increasingly embedded in students’ everyday learning practices, supporting tasks such as writing, coding, reasoning, and analysis. Yet its educational value remains uncertain because systems designed to improve immediate task performance may also weaken the cognitive and metacognitive processes that support durable learning. This paper addresses this learning–performance paradox by asking how AI systems can be designed to support learning rather than merely produce better outputs. To address this, in this paper, we make three contributions. First, we synthesise current LLM-based study support with...

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Application of ChatGPT for automated problem reframing across academic domains
Hafsteinn Einarsson, Sigrún Helga Lund, Anna Helga Jónsdóttir (2024)
Computers & Education: Artificial Intelligence
Detalles Cerrar ✕

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

This paper explores the potential of large language models, specifically ChatGPT, to reframe problems from probability theory and statistics, making them accessible to students across diverse academic fields including biology, economics, law, and engineering. The aim of this study is to enhance interdisciplinary learning by rendering complex concepts more accessible, relevant, and engaging. We conducted a pilot study using ChatGPT to adapt problems across 17 disciplines, evaluated through expert review. Our results demonstrate the significant potential of ChatGPT in reshaping problems for diverse settings, preserving theoretical meaning in 77.1% of cases, and requiring no or only minor...

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Conversational AI as a catalyst for informal learning: An empirical large-scale study on LLM use in everyday learning
Nađa Terzimehić, Babette Bühler, Enkelejda Kasneci (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar ✕

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

Large language models have not only captivated the public imagination but have also sparked a profound rethinking of how we learn. In the third year following the breakthrough launch of ChatGPT, everyday informal learning has been transformed as these novel tools become easily and widely available. Who is embracing LLMs for self-directed learning, and who remains hesitant? What are their reasons for adoption or avoidance? What learning patterns emerge with this novel technological landscape? We present an in-depth analysis from a large-scale survey of 776 German participants, showcasing that 88% of our respondents already incorporate LLMs into their everyday learning routines for a wide...

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Is GPT-4 fair? An empirical analysis in automatic short answer grading
Luiz Rodrigues, Cleon Xavier, Newarney Costa (2025)
Computers & Education: Artificial Intelligence
Detalles Cerrar ✕

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

Short open-ended questions represent a central resource in formative and summative assessments both face-to-face and online settings, ranging from elementary to higher education. However, grading these questions remains challenging for instructors, raising attention to the field of Automatic Short Answer Grading (ASAG). While ASAG has yielded valuable contributions to learning analytics, it often faces generalizability issues. Accordingly, the rapid advancement in Large Language Models (LLMs) has motivated their adoption to empower ASAG systems. Despite that, previous research has not investigated whether LLMs are fair graders in the context of ASAG. Therefore, this paper presents an...

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Effects of adaptive feedback generated by a large language model: A case study in teacher education
Annette Kinder, Fiona J. Briese, Marius Jacobs (2025)
Computers & Education: Artificial Intelligence
Detalles Cerrar ✕

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

This study investigates the effects of adaptive feedback generated by large language models (LLMs), specifically ChatGPT, on performance in a written diagnostic reasoning task among German pre-service teachers (n = 269). Additionally, the study analyzed user evaluations of the feedback and feedback processing time. Diagnostic reasoning, a critical skill for making informed pedagogical decisions, was assessed through a writing task integrated into a teacher preparation course. Participants were randomly assigned to receive either adaptive feedback generated by ChatGPT or static feedback prepared in advance by a human expert, which was identical for all participants in that condition, before...

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Opening the blackbox of LLM-based automated essay scoring: Insights into feature weighting patterns and score validity
Manru Wang, Yihan Chen, Xiaoting Huang (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar ✕

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

Large language models (LLMs) are increasingly used for automated essay scoring, yet their underlying scoring mechanisms remain insufficiently understood. This study systematically compared the scoring behavior of three LLMs (Qwen, GPT, and Gemini) with human raters on English essays written by non-native learners. Sixteen textual features were analyzed to compare score alignment, feature weighting, subgroup consistency, and feature interactions. Results showed strong overall alignment but distinct feature weighting patterns between the LLMs and human raters. Specifically, the LLMs placed greater emphasis on grammatical accuracy, lexical sophistication, and syntactic complexity, indicating a...

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