modelos de lenguaje (LLM)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial grandes modelos de lenguaje andamiaje/scaffolding

Temas IA: modelos de lenguaje (LLM) prompts IA y aprendizaje

Resumen:

The integration of large language models (LLMs) into intelligent tutoring systems promises personalized learning, yet procedural domains such as primary mathematics remain challenging due to inconsistency and pedagogical opacity. LLM tutors may give unsolicited answers or show unstable scaffolding when students vary arithmetic, units, or rounding. This design science research develops and evaluates a rule-guided tutoring system for mathematical word problems in primary schools. We formalize a distinction between rule-guided scaffolding, in which tutoring is governed by a three-layer architecture (diagnosis → intent selection → constrained response generation), and ad-hoc scaffolding, where...

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 grandes modelos de lenguaje procesamiento del lenguaje natural

Temas IA: modelos de lenguaje (LLM) análisis de producción de IA estudio empírico

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....

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)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial ChatGPT grandes modelos de lenguaje

Temas IA: análisis de producción de IA chatGPT modelos de lenguaje (LLM)

Resumen:

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...

AI literacy and its implications for prompt engineering strategies

Nils Knoth, Antonia Tolzin, Andreas Janson (2024)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial alfabetización en IA grandes modelos de lenguaje

Temas IA: prompts modelos de lenguaje (LLM) IA y educación

Resumen:

Artificial intelligence technologies are rapidly advancing. As part of this development, large language models (LLMs) are increasingly being used when humans interact with systems based on artificial intelligence (AI), posing both new opportunities and challenges. When interacting with LLM-based AI system in a goal-directed manner, prompt engineering has evolved as a skill of formulating precise and well-structured instructions to elicit desired responses or information from the LLM, optimizing the effectiveness of the interaction. However, research on the perspectives of non-experts using LLM-based AI systems through prompt engineering and on how AI literacy affects prompting behavior is...

Level-specific feedback generation for scene descriptions via fine-tuning multimodal large language models

ZhiWei Xie, Tse-Tin Chan, Philip L.H. Yu (2025)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial expresión escrita feedback/retroalimentación

Temas IA: IA y enseñanza-aprendizaje de lenguas modelos de lenguaje (LLM) IA y evaluación

Resumen:

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...

AI-based learning content generation and learning pathway augmentation to increase learner engagement

Chaitali Diwan, Srinath Srinivasa, Gandharv Suri (2023)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial tecnología educativa creación de materiales

Temas IA: IA y creación de materiales IA y aprendizaje modelos de lenguaje (LLM)

Resumen:

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Retaining learner engagement is a major challenge in online learning environments, which is even more intensified with learning spaces increasingly built by combining resources from multiple independent sources. Narrative-centric learning experience has been found to improve learner engagement by several researchers. Towards this end, we propose an AI-based approach that generates auxiliary learning content called narrative fragments which are interspersed into the learning pathways to create interactive learning narratives. The proposed approach consists of the automatic generation of two types of narrative fragments– overviews of the learning pathway segments and reflection...

A scoping review of generative AI-powered agentic AI in education: Research landscape, agentic capabilities, and insights from the frontier agent paradigm, exemplified by OpenClaw

Ningxia Wang, Di Zou, Haoran Xie (2026)

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Tipo: artículo

Metodología: revisión bibliográfica

Temas: inteligencia artificial grandes modelos de lenguaje tecnología educativa

Temas IA: modelos de lenguaje (LLM) IA y educación revisión de bibliografía

Resumen:

Generative AI-powered agentic systems are increasingly proposed for educational applications. However, the research landscape remains fragmented, and the relationship between technical capabilities and pedagogical design remains poorly understood. To address this gap, this scoping review systematically mapped 474 studies published between January 2020 and May 2026. Guided by three research questions, we analysed publication characteristics, study designs, agent roles, AI models and architectures, six dimensions of agentic capability, and the extent to which educational theory was incorporated. The results show that the field has expanded rapidly, particularly since 2025. At the same time,...

Is GPT-4 fair? An empirical analysis in automatic short answer grading

Luiz Rodrigues, Cleon Xavier, Newarney Costa (2025)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial evaluación grandes modelos de lenguaje

Temas IA: IA y evaluación modelos de lenguaje (LLM) ética de la IA

Resumen:

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...

Effects of adaptive feedback generated by a large language model: A case study in teacher education

Annette Kinder, Fiona J. Briese, Marius Jacobs (2025)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial feedback/retroalimentación grandes modelos de lenguaje

Temas IA: IA y evaluación modelos de lenguaje (LLM) chatGPT

Resumen:

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...

Opening the blackbox of LLM-based automated essay scoring: Insights into feature weighting patterns and score validity

Manru Wang, Yihan Chen, Xiaoting Huang (2026)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial expresión escrita evaluación

Temas IA: IA y enseñanza-aprendizaje de lenguas IA y evaluación modelos de lenguaje (LLM)

Resumen:

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...