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  • Claudia Nerdel

Bibliografía: Claudia Nerdel

Nº de publicaciones: 5
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Myths, mis- and preconceptions of artificial intelligence: A review of the literature
Arne Bewersdorff, Xiaoming Zhai, Jessica Roberts (2023)
Computers & Education: Artificial Intelligence
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Tipo: artículo

Metodología: revisión bibliográfica

Temas: inteligencia artificial

Resumen:

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Artificial Intelligence (AI) is prevalent in nearly every aspect of our lives. However, recent studies have found a significant amount of confusion and misunderstanding surrounding AI. To develop effective educational programs in the field of AI, it is vital to examine and understand learners' pre- and misconceptions as well as myths about AI. This study examined a corpus of 591 studies. 25 relevant studies were identified by applying the following eligibility criteria: English-written original empirical research on education and AI and reporting AI conceptions in a formal learning context. The review found studies from six continents, with the majority conducted in Europe and North...

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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
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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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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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Assessing student errors in experimentation using artificial intelligence and large language models: A comparative study with human raters
Arne Bewersdorff, Kathrin Seßler, Armin Baur (2023)
Computers & Education: Artificial Intelligence
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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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Identifying logical errors in complex, incomplete or even contradictory and overall heterogeneous data like students’ experimentation protocols is challenging. Recognizing the limitations of current evaluation methods, we investigate the potential of Large Language Models (LLMs) for automatically identifying student errors and streamlining teacher assessments. Our aim is to provide a foundation for productive, personalized feedback. Using a dataset of 65 student protocols, an Artificial Intelligence (AI) system based on the GPT-3.5 and GPT-4 series was developed and tested against human raters. Our results indicate varying levels of accuracy in error detection between the AI system and...

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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
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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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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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How AI literacy correlates with affective, behavioral, cognitive and contextual variables: A systematic review
Arne Bewersdorff, Claudia Nerdel, Xiaoming Zhai (2025)
Computers & Education: Artificial Intelligence
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Tipo: artículo

Metodología: revisión bibliográfica

Temas: inteligencia artificial

Resumen:

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This systematic review maps the empirical landscape of AI literacy by examining its correlations with a diverse array of affective, behavioral, cognitive and contextual variables. Building on the review of AI literacy scales by Lintner (2024), we analyzed 31 empirical studies that applied six of those AI literacy scales, covering 14 countries and a range of participant groups. Our findings reveal robust correlations of AI literacy with AI self-efficacy, positive AI attitudes, motivation, and digital competencies, and negative correlations with AI anxiety and negative AI attitudes. Personal factors such as age appear largely uncorrelated with AI literacy. The review reveals measurement...

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