Bibliografía sobre IA y ELE

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Tipo: artículo

Metodología: estudio empírico

Temas:
ética de la IA estudio empírico

Resumen:

Predictive analytics in education holds promise for supporting student learning but poses risks of bias potentially disadvantaging certain groups based on demographic attributes such as gender. Prior work on fairness in educational prediction has largely focused on binary outcomes in higher education, leaving continuous predictions, such as standardized test scores, comparatively understudied, especially in early schooling where consequences can be long-lasting. Despite a few fairness metrics available for continuous outcomes, they often rely on mean differences, obscuring distributional disparities that may disadvantage specific students. Even fewer studies have attempted to address bias...

Same AI, different pathways: Unpacking mechanisms of AI-mediated learning across discipline-institution contexts

Qinjie Shen (2026)

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Tipo: artículo

Metodología: estudio empírico

Temas:
prompts IA y aprendizaje estudio empírico

Resumen:

Large language models (LLMs) are transforming higher education, yet their learning benefits likely depend on students’ AI literacy and how they prompt and verify AI outputs. This mixed-methods study investigates how AI literacy relates to prompting proficiency and verification behavior, and how these behaviors operate through two mechanisms, trust calibration and extraneous cognitive load, to predict coursework performance across two discipline–institution contexts in Thailand. The author surveyed Design students (n = 221) and Business students (n = 222) and analyzed the data using multigroup structural equation modeling, complemented by thematic analysis of semi-structured interviews....

LLMs do not grade essays like humans

Jerin George Mathew, Sumayya Taher, Anindita Kundu (2026)

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Tipo: artículo

Metodología: estudio empírico

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

Resumen:

Large language models have recently been proposed as tools for automated essay scoring, but their agreement with human grading remains unclear. In this work, we evaluate how LLM-generated scores compare with human grades and analyze the grading behavior of several models from the GPT and Llama families in an out-of-the-box setting, without task-specific training. Our results show that agreement between LLM and human scores remains relatively weak and varies with essay characteristics. In particular, compared to human raters, LLMs tend to assign higher scores to short or underdeveloped essays, while assigning lower scores to longer essays that contain minor grammatical or spelling errors. We...

Categorizing learning analytics models according to their goals and identifying their relevant components: A review of the learning analytics literature from 2011 to 2019

Benazir Quadir, Maiga Chang, Jie Chi Yang (2021)

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Tipo: artículo

Metodología: revisión bibliográfica

Temas:
IA y educación revisión de bibliografía

Resumen:

This study aimed to categorize learning analytics (LA) models and identify their relevant components by analyzing LA-related articles published between 2011 and 2019 in international journals. A total of 101 articles discussing various LA models were selected. These models were characterized according to their goals and components. A qualitative content analysis approach was used to develop a coding scheme for analyzing the aforementioned models. The results reveal that the studied LA models belong to five categories, namely performance, meta-cognitive, interactivity, communication, and data models. The majority of the selected LA-related articles were data models, followed by performance...

Machine learning based feedback on textual student answers in large courses

Jan Philip Bernius, Stephan Krusche, Bernd Bruegge (2022)

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Tipo: artículo

Metodología: estudio empírico

Temas:
IA y evaluación estudio empírico

Resumen:

Many engineering disciplines require problem-solving skills, which cannot be learned by memorization alone. Open-ended textual exercises allow students to acquire these skills. Students can learn from their mistakes when instructors provide individual feedback. However, grading these exercises is often a manual, repetitive, and time-consuming activity. The number of computer science students graduating per year has steadily increased over the last decade. This rise has led to large courses that cause a heavy workload for instructors, especially if they provide individual feedback to students. This article presents CoFee, a framework to generate and suggest computer-aided feedback for...

Developing a weather prediction project-based machine learning course in facilitating AI learning among high school students

Wen-Yen Lu, Szu-Chun Fan (2023)

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Tipo: artículo

Metodología: estudio empírico

Temas:
IA y educación estudio empírico

Resumen:

The rapid growth of artificial intelligence (AI) technology has changed lifestyles, work patterns, and educational approaches. However, courses that can guide students through the practical applications of AI technology are still scarce in K-12 education. This study aimed to develop a project-based machine learning (ML) course for the implementation of AI technology. The core idea of this course, which focused on the supervised learning of AI ML technology, was designed based on the project of weather prediction. Furthermore, data collection and status display were realized using various hardware devices such as Arduino and sensors, whereas ML algorithms were implemented in Python...

Application of ChatGPT for automated problem reframing across academic domains

Hafsteinn Einarsson, Sigrún Helga Lund, Anna Helga Jónsdóttir (2024)

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Tipo: artículo

Metodología: estudio empírico

Temas:
IA y creación de materiales chatGPT estudio empírico

Resumen:

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...

Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities

Jiahong Su, Davy Tsz Kit Ng, Samuel Kai Wah Chu (2023)

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Tipo: artículo

Metodología: revisión bibliográfica

Temas:
IA y educación revisión de bibliografía conceptos básicos

Resumen:

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Nowadays, Artificial Intelligence (AI) literacy has become an emerging topic in digital literacy education research. However, it is still under-explored in early childhood education (ECE) since the AI curriculum for young children has just been designed in recent years. A scoping review was conducted to examine the thematic and content analysis of 16 empirical papers from 2016 to 2022. This scoping reviews evaluate, synthesize, and display 16 studies on AI literacy in early childhood education, including curriculum design, AI tools, pedagogical approaches, research designs, assessment methods, and findings. The discussion of the AI literacy implementation in ECE contributes to...

The power of Deep Learning techniques for predicting student performance in Virtual Learning Environments: A systematic literature review

Bayan Alnasyan, Mohammed Basheri, Madini Alassafi (2024)

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Tipo: artículo

Metodología: revisión bibliográfica

Temas:
IA y educación revisión de bibliografía

Resumen:

With the advances in Artificial Intelligence (AI) and the increasing volume of online educational data, Deep Learning techniques have played a critical role in predicting student performance. Recent developments have assisted instructors in determining the strengths and weaknesses of student achievement. This understanding will benefit from adopting the necessary interventions to assist students in improving their performance, helping at-risk of failure students, and preventing dropout rates. The review analyzed 46 studies between 2019 and 2023 that apply one or more Deep Learning (DL) techniques, either single or in combination with Machine Learning (ML) or Ensemble Learning techniques....

Potential risks of generative artificial intelligence integration into K-12 education: A scoping review

Sisi Tao, Min Lan, Minjuan Wang (2026)

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Tipo: artículo

Metodología: revisión bibliográfica

Temas:
ética de la IA IA y educación revisión de bibliografía

Resumen:

The rapid integration of Generative AI (GenAI) into K-12 education has outpaced our understanding of its potential risks and unintended consequences. Existing reviews have prioritized higher education and technical promise, overlooking the potential developmental risks it poses to children and adolescents in K-12 settings. This scoping review synthesizes 22 empirical studies from K-12 contexts to map the types of risks and concerns reported in research, compile mitigation strategies, and identify priority gaps that could guide more developmentally informed future studies. Across the included studies, reported risks clustered into three domains: (a) risks to psychological wellbeing, with...