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  • 2026

Bibliografía: 2026

Nº de publicaciones: 477
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AI-mediated research agency formation in higher education: Autonomy, self-efficacy and innovation in early-career scientific training
Shanshan Han, Pingqing Liu (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar βœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

Generative AI is becoming part of the infrastructure of higher education research training, yet little is known about how AI-mediated environments shape early-career researchers’ agency. This study examines AI dependence, defined as reliance on intelligent tools as a primary source of cognitive support in judgement-intensive research tasks, and interprets its educational risk as epistemic delegation: the transfer of problem framing, methodological choice and interpretive authority from the researcher to the intelligent system. Drawing on an AI-mediated research agency formation framework, we examined whether research autonomy and research self-efficacy mediate the association between AI...

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Directive, metacognitive, or a blend of both? A comparison of AI-generated feedback types on student engagement, confidence, and outcomes
Omar Alsaiari, Nilufar Baghaei, Jason M. Lodge (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar βœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

Effective feedback is a central component of successful student learning, with extensive research examining how best to implement it in educational settings. Increasingly, feedback is being generated by artificial intelligence (AI), offering scalable and adaptive responses. Two widely studied approaches are directive feedback, which gives explicit explanations and reduces cognitive load to speed up learning, and metacognitive feedback which prompts learners to reflect, track their progress, and develop self-regulated learning (SRL) skills. While both approaches have clear theoretical advantages, their comparative effects on engagement, confidence, and quality of work remain underexplored....

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Measuring Acceptance of Age-Tiered AI Literacy Guidebooks: A Developmentally Informed Study of K-12 Students and Teachers
Li-Jen Wang, Tsung-Yen Chuang, Ying-Tien Wu (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar βœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

As generative artificial intelligence (AI) becomes increasingly integrated into K-12 education, there is a pressing need for AI literacy initiatives to incorporate learning resources that are both age-appropriate and ethically grounded. This study examined acceptance of the AI Learning Application Guidebook, developed in Elementary and Advanced editions for learners aged 9–12 and 13–18, respectively. The Elementary edition features scaffolded, platform-based, non-generative activities, whereas the Advanced edition includes authentic applications, ethical reasoning exercises, and supervised engagement with generative AI. The guidebooks were evaluated in Taiwanese classrooms by 831...

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Empowering university teachers in higher education: A generative AI-responsive competency framework
Daner Sun, Shen Ba, Yingying Cha (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar βœ•

Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

Texto completo

The integration of generative artificial intelligence (GenAI) into higher education necessitates a reconceptualization of teacher competencies, moving beyond technical proficiency to encompass pedagogical strategies for fostering critical, ethical, and developmentally appropriate student-AI collaboration. Existing competency frameworks, however, exhibit notable limitations in equipping university teachers with actionable guidance for designing GenAI-mediated learning experiences that cultivate their students’ higher-order thinking and subject knowledge. In response, this paper develops and proposes a GenAI-responsive competency framework for university teachers to supplement existing...

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How high-school pressure and autonomy support are linked to dual AI learning pathways: A cross-contextual SEM analysis
Shen Qinjie, Wynn Arunrugstichai (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar βœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

Generative AI tools are rapidly becoming embedded in students' everyday learning, yet students' AI use varies from constructive support to compulsive dependence. Drawing on Self-Determination Theory and the Hook model, this study proposes a dual-pathway framework examining how retrospective reports of high-school learning climate relate to university students’ GenAI-use patterns and perceived AI-supported learning quality through motivational and behavioral relations. Using cross-sectional survey data from university students with different high-school backgrounds and current university contexts (overall N = 508; China-HS group n = 305 and non-China-HS group n = 203; China-Uni n = 193 and...

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Teaching AI competencies: Experiences from coaching interdisciplinary teams to develop AI prototypes
Aidin Azamnouri, Dominik Hörauf, Brigitte Schönberger (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar βœ•

Tipo: artículo

Metodología: experiencia práctica

Temas: inteligencia artificial

Resumen:

Texto completo

Artificial Intelligence (AI) is changing and revolutionizing today’s economy and work life. A basic understanding of this technology is beneficial for even non-technical roles, highlighting the interdisciplinary nature of AI and its diverse applications. However, it is difficult to create significant, practically relevant learning experiences related to AI for students of different backgrounds, especially for students outside of computer science programs. To tackle this problem, we designed and evaluated an interdisciplinary, project-based course combined with creativity methods, where students from diverse study programs worked on an everyday challenge and tried to build AI prototypes to...

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Coauthorship integrity: Reconceptualising assessment validity for the age of generative artificial intelligence
Mohsen Ebrahimzadeh, Antonette Shibani, Simon Buckingham Shum (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar βœ•

Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

Texto completo

We consider how a future of pervasive human/AI coauthorship challenges current notions of validity and academic integrity. Specifically, we address widespread concerns that in non-proctored contexts, students are using generative artificial intelligence (GenAI) to submit texts they do not understand. Adopting an assessment validity lens, we show how GenAI undermines the integrity of multiple forms of validity evidence, leading us to propose Coauthorship Integrity as a new conceptual source of validity evidence for addressing these threats. Coauthorship Integrity is violated when students submit AI-generated content that they do not understand. To hold students accountable in this regard, a...

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A systematic review of the impact of GenAI on learning performance, AI hallucinations, and problem-solving in computer science education
Adedeji Adefisoye Adejumo, Solomon Sunday Oyelere, Ismaila Temitayo Sanusi (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar βœ•

Tipo: artículo

Metodología: revisión bibliográfica

Temas: inteligencia artificial

Resumen:

Texto completo

This systematic review synthesizes 64 empirical studies to examine how Generative AI (GenAI) shapes learning in Computer Science Education (CSE), particularly in programming, debugging, algorithmic reasoning, and computational problem-solving contexts. Grounded in Constructivist, Sociocultural, Cognitive Load, Adaptive Learning, and Metacognitive Learning theories, the review adopts an integrative perspective to analyze how GenAI-driven adaptivity, AI output qualities, hallucination dynamics, and cognitive–affective regulation influence learners’ interpretation, cognitive processing, and learning outcomes. Findings reveal a dual impact of GenAI in CSE. On the negative side, hallucinated or...

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From simulation to flight: Simulation-assisted drone learning with teacher-AI co-designed scaffolds for secondary students’ STEM knowledge and competencies
Richard Chung Yiu Yeung, Chi Ho Yeung, Daner Sun (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar βœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

A persistent challenge in drone-based STEM education is the scarcity of teacher-verified, interactive simulations that support both conceptual learning and competency development. Generative AI can accelerate content creation, but its outputs frequently lack pedagogical validity and contextual alignment. This study examines whether simulation-assisted drone instruction, anchored by teacher-AI co-designed simulations, yields superior learning outcomes compared to the same hands-on curriculum delivered without simulations. Using a quasi-experimental pretest–posttest design with 30 secondary students (aged 13 to 17), both groups completed identical drone tasks with the same instructor; however...

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Can students judge like experts? A large-scale study on the pedagogical quality of AI and human personalized formative feedback
Tanya Nazaretsky, Hagit Gabbay, Tanja Käser (2026)
Computers & Education: Artificial Intelligence
Detalles Cerrar βœ•

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

While feedback is essential for guiding student learning, providing timely and personalized guidance in large-scale educational settings remains a significant challenge. Generative AI offers a scalable solution, yet little is known about students’ perceptions of AI-generated feedback. In this paper, we aim to investigate how the identity of the feedback provider (human vs. AI) affects students’ ability to assess feedback quality and whether their judgments are biased. We propose a comprehensive rubric for assessing the pedagogical quality of formative feedback. We use it to compare the objective quality of AI-generated and human-crafted feedback (N = 979). Next, using data collected from...

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