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  • Computers & Education: Artificial Intelligence

Revistas: Computers & Education: Artificial Intelligence

Nº de publicaciones: 569

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Learning with machines: Toward a theory of epistemic co-agency
Anita Samuel (2026)
Computers & Education: Artificial Intelligence 10
Detalles Cerrar โœ•

Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial teorías de aprendizaje cognición y procesamiento

Resumen:

Texto completo

The rapid integration of generative AI (GenAI) systems into educational settings necessitates a reevaluation of how learners reason, construct, and assume responsibility for knowledge. Existing learning theories like constructivism, sociocultural theory, and connectivism presume human-centered epistemic agency and fail to account for the ways GenAI simulates reasoning, reframes arguments, and co-constructs meaning. This paper introduces the Epistemic Entanglement Framework, a theory-informed model that captures how learners engage with AI systems. Drawing on distributed cognition, sociomaterialism, and posthumanist theory, the framework proposes epistemic co-agency as a reflexive stance...

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Investigating algorithmic bias in student progress monitoring
Jamiu Adekunle Idowu, Adriano Soares Koshiyama, Philip Treleaven (2024)
Computers & Education: Artificial Intelligence 7
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial ética inclusión y equidad

Resumen:

Texto completo

This research investigates bias in AI algorithms used for monitoring student progress, specifically focusing on bias related to age, disability, and gender. The study is motivated by incidents such as the UK A-level grading controversy, which demonstrated the real-world implications of biased algorithms. Using the Open University Learning Analytics Dataset, the research evaluates fairness with metrics like ABROCA, Average Odds Difference, and Equality of Opportunity Difference. The analysis is structured into three experiments. The first experiment examines fairness as an attribute of the data sources and reveals that institutional data is the primary contributor to model discrimination,...

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Generative AI (GenAI) as a mindtool that supports generative learning (GL)
Nada Dabbagh, Helen Fake (2026)
Computers & Education: Artificial Intelligence 11
Detalles Cerrar โœ•

Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial teorías de aprendizaje cognición y procesamiento

Resumen:

Texto completo

Grounded in the learning sciences, Generative Learning (GL), is a learning method or process that encourages students to actively generate information and make connections between new and existing knowledge. This active participation in the learning process promotes a deeper understanding of the instructional material, fosters long-term retention of knowledge, and cultivates critical thinking skills and problem-solving abilities. The magic lies in the “generation” process, where learners actively make sense of the material rather than passively receiving information. In this paper, we argue that Generative Artificial Intelligence (GenAI) can be used as a Mindtool (knowledge representation...

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AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students' AI self-efficacy
Arne Bewersdorff, Marie Hornberger, Claudia Nerdel (2025)
Computers & Education: Artificial Intelligence 8
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial alfabetización en IA educación superior

Resumen:

Texto completo

This study investigates how cognitive, affective, and behavioral variables related to artificial intelligence (AI) build AI self-efficacy among university students. Based on these variables, we identify three meaningful student groups, which can guide educational initiatives. We recruited 1465 undergraduate and graduate students from the United States, the United Kingdom, and Germany and measured their AI self-efficacy, AI literacy, interest in AI, attitudes towards AI, and AI use. Using a path model, we examine the correlations and paths among these variables. Results reveal that AI usage and positive AI attitudes significantly predict interest in AI, which in turn and together with AI...

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AI analysis reveals top predictors of first grade success: Insights from multifactorial screening students’ early days of school
Pedro Bem-haja, Paulo Nossa, Andreia J. Ferreira (2025)
Computers & Education: Artificial Intelligence 8
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial educación primaria y secundaria analítica del aprendizaje

Resumen:

Texto completo

Early prediction of students' primary school academic performance is crucial for planning timely interventions for those expected to struggle. Random Forest, Gradient Boosting, and Extra Trees AI techniques were used to study 2549 first graders to predict end-of-year academic performance and identify critical predictors for targeted interventions. The Extra Trees model used EPIS (Empresários pela Inclusão Social) screening to yield high F1 scores above 99.7 %, accurately identifying passing and failing students. Out of 142 variables, the top ten strongest predictors of academic success were categorized into three areas: 1) teacher perceptions of cognitive function, particularly attention (...

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Exploring non-traditional learner motivations and characteristics in online learning: A learner profile study
Andrew Zamecnik, Vitomir Kovanoviฤ‡, Srecko Joksimovic (2022)
Computers & Education: Artificial Intelligence 3
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial enseñanza en línea e híbrida analítica del aprendizaje

Resumen:

Texto completo

The growth and uptake of educational technology has significantly reshaped the delivery of distance and online learning. With an unprecedented number of learners engaging with online modes of education, there is a growing need to understand the underlying student enrolment motivations, goals and learning behaviours evolving from a highly diverse student population. Research in learning analytics has advanced the use of digital data to understand student learning processes. However, there remains a limited understanding of how non-traditional learner characteristics, needs and motivational factors influence their learning behaviour and engagement strategies. Survey data from 232 students...

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Mechanisms of enhancing learning with unequal preparation: An experimental study on generative artificial intelligence use and proficiency in programming learning
Yuan Jin, Wei He, Jun Shen (2025)
Computers & Education: Artificial Intelligence 9
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial pensamiento computacional inclusión y equidad

Resumen:

Texto completo

Prior learning preparation, encompassing learners' prior knowledge, resources, and readiness for learning new material, plays a critical role in a new learning process. Unequal learning preparation commonly exists among learners with varying socioeconomic and academic backgrounds, and can further exacerbate the educational divide. By reducing learning cost and providing less prepared learners with personalized delivery of information and knowledge, Generative Artificial Intelligence (GenAI) may help mitigate disparities in learning preparation. In this study, we investigate the roles of learners' use of GenAI in their learning processes, considering varying levels of learners’ prior...

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What do university students know about Artificial Intelligence? Development and validation of an AI literacy test
Marie Hornberger, Arne Bewersdorff, Claudia Nerdel (2023)
Computers & Education: Artificial Intelligence 5
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial alfabetización en IA educación superior

Resumen:

Texto completo

Artificial Intelligence (AI) strongly influences our daily lives and work environments. To deal with the challenges as well as to pick up on the opportunities associated with AI, university students need to acquire a basic understanding of AI (so-called AI literacy). To design effective study programs that foster AI competencies, it is necessary to assess the state of students' AI literacy. While there already are some AI literacy tests available, many instruments focus on specific AI courses, rely primarily on self-assessment, or do not provide detailed psychometric information. This study aims to develop and validate an AI literacy test and to provide initial insights into the current...

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Undergraduate students’ learning outcomes with ChatGPT: A meta-analytic study
Fangfang Mo, Jing Huang, Yao Yang (2026)
Computers & Education: Artificial Intelligence 10
Detalles Cerrar โœ•

Tipo: artículo

Metodología: revisión bibliográfica

Temas: inteligencia artificial ChatGPT educación superior

Resumen:

Texto completo

ChatGPT has gained substantial attention in the field of higher education, particularly for its potential to enhance undergraduate students' learning outcomes. To better understand ChatGPT's impact, we conducted a meta-analysis evaluating the effects of ChatGPT applications on undergraduate students' learning outcomes, with data collected from studies published between January 1st, 2023, and May 31st, 2025. Our search across nine academic databases identified 5555 potential studies, of which 66 met the pre-defined inclusion criteria and were selected for meta-analysis. The meta-analysis incorporated 129 effect sizes, allowing us to estimate the overall impact of ChatGPT on undergraduate...

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Immersive multi-modal pedagogical conversational artificial intelligence for early childhood education: An exploratory case study in the wild
Sinem Aslan, Lenitra M. Durham, Nese Alyuz (2024)
Computers & Education: Artificial Intelligence 6
Detalles Cerrar โœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial educación infantil chatbots

Resumen:

Texto completo

Educational technology research has found that parents of young children widely share concerns about extended screen time, lack of physical activity, and lack of social interaction. Kid Space was developed to address these concerns by enabling multi-modal and immersive collaborative play-based learning. Kid Space utilizes multiple sensing technologies with an immersive physical space through a human-scale wall projection and incorporates a conversational AI agent to interact with children, understand individual progress, and personalize learning experiences in a blended physical and digital environment. To evaluate Kid Space in the wild, we conducted a multi-method user study involving a...

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