Computers and Education Open最新文献

This paper explores issues surrounding children's participation in research relating to digital childhoods, with an emphasis on the process of obtaining ethics approvals and other necessary approvals from Australian education authorities (e.g. Departments of Education), along with associated issues and tensions. It will report on the patterns apparent in existing literature in terms of how much research is being undertaken in independent, government, or Catholic schools, as well as researchers’ experiences working across Australian states and territories. In addition to presenting experiential data, the paper will raise tension points and provocations relating to the challenges faced by researchers and the implications of these for conducting research with children, as well as for children's rights, agency, and voice.

Advances in technology have sparked a surge of interest in systematic group formation in educational contexts. The experimental study investigates group formation by extraversion distributions on group work outcomes, expected to influence group hierarchy. As an initial step in the experimental randomization process, an algorithmic group formation tool ensured an equal partitioning and aligned students into two experimental conditions with either consistent, homogeneous, or varied, heterogeneous, levels of extraversion. Over the course of one semester, a total of 114 students enrolled in several paralleled seminars, were surveyed on both subjective data (satisfaction with group work) and objective data (group performance) to evaluate the effect of the experimental intervention. The formation of extraversion at the group level contributed to the respective outcomes, emphasizing the value of collective social capital for both individuals and groups. Specifically, a homogeneous distribution of extraversion had a positive impact on group performance, as evident in improved grades on course group assignments and increased active participation in group meetings. Findings emphasize considering personality traits at group-level to enhance the success of groups.

Motivated by a holistic understanding of AI literacy, this work presents an interdisciplinary effort to make AI literacy measurable in a comprehensive way, considering generic and domain-specific AI literacy as well as AI ethics. While many AI literacy assessment tools have been developed in the last 2-3 years, mostly in the form of self-assessment scales and less frequently as knowledge-based assessments, previous approaches only accounted for one specific area of a comprehensive understanding of AI competence, namely cognitive aspects within generic AI literacy. Considering the demand for AI literacy development for different professional domains and reflecting on the concept of competence in a way that goes beyond mere cognitive aspects of conceptual knowledge, there is an urgent need for assessment methods that capture domain-specific AI literacy on each of the three competence dimensions of cognition, behavior, and attitude. In addition, competencies for AI ethics are becoming more apparent, which further calls for a comprehensive assessment of AI literacy for this very matter. This conceptual paper aims to provide a foundation upon which future AI literacy assessment instruments can be built and provides insights into what a framework for item development might look like that addresses both generic and domain-specific aspects of AI literacy as well as AI ethics literacy, and measures more than just knowledge-related aspects based on a holistic approach.

In the context of global integration and increasing reliance on Artificial Intelligence (AI) in education, evaluating the AI literacy of pre-service teachers is crucial. As future architects of educational systems, pre-service teachers must not only possess pedagogical expertise but also a strong foundation in AI literacy. This quantitative study examines AI literacy among 529 pre-service teachers in a Nigerian university, utilizing structural equation modeling (SEM) for comprehensive analysis. The research explores various dimensions of AI literacy, revealing that a profound understanding of AI significantly predicts positive outcomes in AI use, detection, ethics, creation, and problem-solving. However, no correlation exists between AI knowledge and emotion regulation or the assumption that active AI use enhances AI detection capabilities. The study identifies a trade-off between AI application and creation, emphasizing the ethical considerations intertwined with emotional and persuasive facets of AI use. It also supports the link between AI creation and problem-solving, emphasizing the foundational role of AI knowledge in shaping diverse aspects of AI literacy among pre-service teachers. The findings offer valuable insights for educators, administrators, policymakers, and researchers aiming to enhance AI literacy in pre-service teacher education programs.

Integrating artificial intelligence (AI) into teaching practices is increasingly vital for preparing students for a technology-centric future. This study examined the influence of a case-based AI professional development (PD) program on AI integration strategies and AI literacy among seven middle school science teachers. Employing three distinct case problems, from well-structured to ill-structured, the AI PD program aimed to stimulate teachers’ reflection on AI literacy development and encourage the construction of problem-solving and AI integration strategies within various pedagogical contexts. Analysis of video-recorded case discussions revealed that teachers primarily drew on personal experiences for collaborative problem-solving across the three cases. However, the complexity of the case problems influenced their approach to knowledge co-construction, and dealing with ill-structured problems promoted the application of new knowledge. Through analyzing the survey data, we found a marked increase in teachers’ AI literacy, particularly in the domain of knowing and understanding AI, suggesting a pivotal role for direct instruction that supports AI literacy growth. However, their application of this AI knowledge was limited during the case discussions, while other domains of teacher AI literacy were more frequently employed. The findings highlight the importance of combining direct instruction with case-based discussions in AI-related PD programs to bolster teacher AI literacy effectively. The research has implications for using a case-based learning approach during short-term PD initiatives and advocates the ongoing need for comprehensive AI literacy development to facilitate teachers’ AI integration in subject-specific teaching.

Intelligent voice assistants (IVAs) such as Alexa or Siri are voice-based Artificial Intelligence systems that help users with various everyday tasks using simple voice commands. However, users often only have a superficial understanding of how the Artificial Intelligence (AI) integrated into IVAs works, which leads to misunderstandings and potential risks of use. To promote self-determined interaction with IVAs, the development of specific AI-related skills, such as a comprehensive understanding of AI, is crucial. Based on learning psychology and media pedagogy principles, two online training modules were developed to deepen the understanding of AI concerning IVAs and enable self-determined interaction. A total of 99 participants took part in the training. The results show that the training promotes both understanding of AI and AI literacy. It also increases the intention to use IVAs, promotes a positive attitude, and enhances the willingness for self-determined interaction. In addition, the training contributes to a more realistic assessment of the IVAs' capabilities and reduces anthropomorphic perceptions. Overall, the study emphasizes the relevance of specific AI skills and shows how targeted training can contribute to improving these skills. Thus, the present work contributes to improving the availability of digital education programs.

The explosion of AI across all facets of society has given rise to the need for AI education across domains and levels. AI literacy has become an important concept in the current technological landscape, emphasizing the need for individuals to acquire the necessary knowledge and skills to engage with AI systems. This systematic review examined 47 articles published between 2019 and 2023, focusing on recent work to capture new insights and initiatives given the burgeoning of the literature on this topic. In the initial stage, we explored the dataset to identify the themes covered by the selected papers and the target population for AI literacy efforts. We identified that the articles broadly contributed to one of the following themes: a) conceptualizing AI literacy, b) prompting AI literacy efforts, and c) developing AI literacy assessment instruments. We also found that a range of populations, from pre-K students to adults in the workforce, were targeted. In the second stage, we conducted a thorough content analysis to synthesize six key constructs of AI literacy: Recognize, Know and Understand, Use and Apply, Evaluate, Create, and Navigate Ethically. We then applied this framework to categorize a range of empirical studies and identify the prevalence of each construct across the studies. We subsequently review assessment instruments developed for AI literacy and discuss them. The findings of this systematic review are relevant for formal education and workforce preparation and advancement, empowering individuals to leverage AI and drive innovation.

The use of Artificial Intelligence (AI) and Machine Learning algorithms is surging in education. One of these methods, called Reinforcement Learning (RL) may be considered more general and less rigid by changing its learning through interactions with the environment and specifically the inputs received as rewards and punishments. Given that education has shifted towards a constructivist approach and uses technology such as algorithms in its making (e.g., instructional design, delivery, assessment, and feedback), we are interested in taking stock of the effect RL may play in today's teaching and learning. We conduct a scoping review of the literature on RL in education. This work aims to open discussions on the pedagogical paradigm of RL and various types of bias introduced in teaching and learning.

This scoping literature review examines the application of Generative Artificial Intelligence (GenAI), a disruptive technology, in language teaching and learning. Since its launch in November 2022, GenAI has captured global attention with OpenAI's ChatGPT, powered by the generative pre-trained transformer-3 (GPT-3) large-language model. The emergence of GenAI holds immense implications across various domains, including language education. This review aims to provide an overview of the current state of research and identify research gaps and future directions in this emerging field. The review follows the PRISMA-ScR guidelines and includes eligible publications published between 2017 and July 2023. Four electronic databases were searched and 41 of the 224 initial papers were eventually selected for review. The findings reveal key terms related to GenAI in language education, the most researched language study and education levels, areas of research, attitudes towards GenAI, and the potential benefits and challenges of GenAI application. The review highlights several research gaps, including the need for more empirical studies to assess the effectiveness and impact of GenAI tools, discussion of ethical considerations, targeted interventions for specific language skills, and stakeholder engagement in responsible integration. Educators are encouraged to incorporate GenAI tools into their teaching practices while remaining vigilant about potential risks. Continuous professional development for educators is crucial to ensure informed decision-making and effective integration of GenAI tools. This scoping review contributes to the existing knowledge on the use of GenAI in language education and informs future research and practice in this disruptive and rapidly evolving field.

A common dilemma in educational game design is identifying the right balance between freedom and structure. Too much structure limits constructive learning and curiosity, while too much freedom diverts focus away from the educational content. How can we create a feeling of freedom while encouraging students to interact with learning material? We argue that a compelling, open-world action-adventure game with a branching storyline could provide just the right balance. To test this idea, we created a single-player custom map action-adventure game, Ulfberht's Sword, using the popular Minecraft game. Our objective was that the game would support prehistory education and we piloted it with 151 students in their history classrooms. To determine how effective our game was for delivering educational material, we collected and analyzed the students' log files in combination with students' Minecraft experience. Our structure discovery revealed four interaction types, (1) intellectuals, (2) wanderers, (3) explorers, and (4) achievers, which reflect the students' interaction with the educational content and perseverance in challenging situations. Ultimately, the game design engaged Minecraft-experienced player types and guided them toward primary educational materials. Less-experienced player types remained curious but would have benefited from more direct in-game scaffolding, demonstrating the importance of players' pre-existing gaming skills and thoughtful choice architecture. In conclusion, the player types provide insight into how we can best support different types of students in the open-world game environment. In turn, this information allows us to identify design challenges and suggest better ways to strike the structure-autonomy balance in realistically heterogeneous classrooms.