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  • Computers & Education: Artificial Intelligence

Revistas: Computers & Education: Artificial Intelligence

Nº de publicaciones: 569

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Addressing the use of generative AI in academic writing
Johan van Niekerk, Petrus M.J. Delport, Iain Sutherland (2025)
Computers & Education: Artificial Intelligence 8
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial escritura académica ChatGPT

Resumen:

Texto completo

The rise of generative AI has been a major disruptive force in academia. Academics are concerned about its impact on student learning. Students can use generative AI technologies, such as ChatGPT, to complete many academic tasks on their behalf. This could lead to poor academic outcomes as students use ChatGPT to complete assessments, rather than engaging with the learning material. One particularly vulnerable academic activity is academic writing. This paper reports the results of an active learning intervention where ChatGPT was used by students to write an academic paper. The resultant papers were then analysed and critiqued by students to highlight the weaknesses of such AI-produced...

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From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning
Eduard Pogorskiy, Jens F. Beckmann (2023)
Computers & Education: Artificial Intelligence 4
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial tecnología educativa enseñanza en línea e híbrida

Resumen:

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Compared to traditional classroom learning, success in online learning tends to depend more on the learner’s skill to self-regulate. Self-regulation is a complex meta-cognitive skill set that can be acquired. This study explores the effectiveness of a virtual learning assistant in terms of (a) developmental, (b) general compensatory, and (c) differential compensatory effects on learners’ self-regulatory skills in a sample of N = 157 online learners using an experimental intervention-control group design. Methods employed include behavioural trace data as well as self-reporting measures. Participants provided demographic information and responded to a 24-item self-regulation...

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How can we design for learning in an AI world?
Lucila Carvalho, Roberto Martinez-Maldonado, Yi-Shan Tsai (2022)
Computers & Education: Artificial Intelligence 3
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Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial diseño curricular alfabetización en IA

Resumen:

Texto completo

Fast improvements in computing power and Artificial Intelligence (AI) algorithms enable us to automate important decisions that shape our everyday lives, and drive workplace transformations. It is predicted that many people will find themselves unprepared to deal with high degrees of change and uncertainty, increasingly posed by AI in some sectors. A critical educational challenge involves figuring out how to support young generations to develop the capabilities that they will need to adapt to, and innovate in, a world with AI. This article argues that both educators and learners should be involved not only in learning but also in co-designing for learning in an AI world. Further, they...

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A machine learning framework for soft skills assessment: Leveraging serious games in higher education
Agostino Marengo, Alessandro Pagano, Vito Santamato (2025)
Computers & Education: Artificial Intelligence 9
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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial juegos evaluación

Resumen:

Texto completo

This study explores the use of serious games combined with machine learning techniques to evaluate student efficiency and determine appropriate degree programs. The main research question addressed is: How can predictive machine learning algorithms, applied to soft skills data collected through serious games, identify the most suitable study path for each student, thereby improving academic orientation and enhancing the likelihood of educational success? The research involved 211 university students from a single university in Italy, focusing on the application of predictive algorithms based on participants' soft skills and academic performance (GPA). The study employs logistic regression...

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Automatic proficiency scoring for early-stage writing
Michael Riis Andersen, Kristine Kabel, Jesper Bremholm (2023)
Computers & Education: Artificial Intelligence 5
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial expresión escrita evaluación

Resumen:

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In this work, we study the feasibility of using machine learning and natural language processing methods for assessing writing proficiency in Danish with respect to text construction, sentence construction, and use of modifiers. Our work is based on the analytical framework for scoring early writing proposed by Kabel et al. (2022), where each text is first annotated by a human expert according to a predefined coding scheme and subsequently scored using statistical Rasch modeling (Rasch, 1960). We investigate two different strategies for estimating these scores automatically: 1) we propose a system for identifying the central linguistic features automatically mimicking the role of the human...

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How reliable are large language models in analyzing the quality of written lesson plans? A mixed-methods study from a teacher internship program
Dennis Hauk, Nina Soujon (2026)
Computers & Education: Artificial Intelligence 10
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial grandes modelos de lenguaje formación de profesores

Resumen:

Texto completo

This study investigates the reliability of Large Language Models (LLMs) in evaluating the quality of written lesson plans from pre-service teachers. A total of 32 lesson plans, each ranging from 60 to 100 pages, were collected during a teacher internship program for civic education pre-service teachers. Using the ChatGPT-o1 reasoning model, we compared a human expert standard with LLM coding outcomes in a two-phase explanatory sequential mixed-methods design that combined quantitative reliability testing with a qualitative follow-up analysis to interpret inter-dimensional patterns of agreement. Quantitatively, overall reliability across six qualitative components of written lessons plans (...

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Improving EFL students’ cultural awareness: Reframing moral dilemmatic stories with ChatGPT
Yangsheng (Danson) Zheng, Nicola Stewart (2024)
Computers & Education: Artificial Intelligence 6
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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial competencia intercultural creación de materiales

Resumen:

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Classroom teachers are usually responsible for creating materials to meet students' needs and course requirements. The arrival of generative AI, such as ChatGPT, offers EFL teachers an opportunity to regularly collaborate with AI chatbots to create new source materials. The writing experiments in this study considered how to effectively and ethically work with generative AI to produce culturally appropriate EFL teaching materials. The experiments involved co-producing moral dilemmatic stories with ChatGPT to support a new Chinese EFL curriculum unit on “Morals and Virtues”. We draw on Lo's (2023a, 2023b) CLEAR framework for prompt engineering to generate appropriate stimulus materials. We...

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Navigating the ethical terrain of AI in education: A systematic review on framing responsible human-centered AI practices
Yao Fu, Zhenjie Weng (2024)
Computers & Education: Artificial Intelligence 7
Detalles Cerrar โœ•

Tipo: artículo

Metodología: revisión bibliográfica

Temas: inteligencia artificial ética

Resumen:

Texto completo

With the rapid development of artificial intelligence (AI) in recent years, there has been an increasing number of studies on integrating AI in various educational contexts, ranging from early childhood to higher education. Although systematic reviews have widely reported the effects of AI on teaching and learning, limited reviews have examined and defined responsible AI in education (AIED). To fill this gap, we conducted a convergent systematic mixed studies review to analyze key themes emerging from primary research. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we searched Scopus and Web of Science and identified 40 empirical studies...

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Reconceptualizing community of inquiry in the age of generative artificial intelligence
Shen Ba, Dragan Gaševiฤ‡, Cher-Ping Lim (2026)
Computers & Education: Artificial Intelligence 11
Detalles Cerrar โœ•

Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial enseñanza en línea e híbrida teorías de aprendizaje

Resumen:

Texto completo

The Community of Inquiry (CoI) framework is widely used to guide and explain learning in online and blended environments. However, generative artificial intelligence (GenAI) challenges a key assumption in CoI research and practice: that indicators of presence can be attributed primarily to human learners, instructors, and their interactions. GenAI can produce fluent discourse that resembles strong cognitive, social, or teaching presence. Yet such discourse may not reflect learners' thinking, relationships, or regulatory intentions. To date, research often frames GenAI as a tool, a dialogic partner, or a possible fourth presence. These views, however, do not fully capture GenAI's pervasive...

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Enhancing data analysis and programming skills through structured prompt training: The impact of generative AI in engineering education
Ashish Garg, K. Nisumba Soodhani, Ramkumar Rajendran (2025)
Computers & Education: Artificial Intelligence 8
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial pensamiento computacional educación superior

Resumen:

Texto completo

The advent of Generative Artificial Intelligence (GenAI) and large language models like LLama, Palm2, GPT, Gemini, and Claude has revolutionized education by generating human-like text and contextually relevant responses. Our research investigates the impact of structured prompt training on students' learning in data analysis and programming. We experimented with 157 first-year engineering students divided into three groups: a control group (internet access, no GenAI), an experimental group 1 (internet and GenAI without prompt training), and an experimental group 2 (internet and GenAI with prompt training). The prompt training session included techniques like few-shot prompting, chain...

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