Computers and Education Open最新文献

Artificial intelligence (AI) literacy has recently emerged on the educational agenda raising expectations on teachers’ and teacher educators’ professional knowledge. This scoping review examines how the scientific literature conceptualises AI literacy in relation to teachers’ different forms of professional knowledge relevant for Teacher Education (TE). The search strategy included papers and proceedings from 2000 to 2023 related to AI literacy and TE as well as the intersection of AI and teaching. Thirty-four papers were included in the analysis. The Aristotelian concepts episteme (theoretical-scientific knowledge), techne (practical-productive knowledge), and phronesis (professional judgement) were used as a lens to capture implicit and explicit dimensions of teachers’ professional knowledge. Results indicate that AI literacy is a globally emerging research topic in education but almost absent in the context of TE. The literature covers many different topics and draws on different methodological approaches. Computer science and exploratory teaching approaches influence the type of epistemic, practical, and ethical knowledge. Currently, teachers’ professional knowledge is not broadly addressed or captured in the research. Questions of ethics are predominantly addressed as a matter of understanding technical configurations of data-driven AI technologies. Teachers’ practical knowledge tends to translate into the adoption of digital resources for teaching about AI or the integration of AI EdTech into teaching. By identifying several research gaps, particularly concerning teachers' practical and ethical knowledge, this paper adds to a more comprehensive understanding of AI literacy in teaching and can contribute to a more well-informed AI literacy education in TE as well as laying the ground for future research related to teachers’ professional knowledge.

Artificial intelligence (AI) education in K–12 schools is a global initiative, yet planning and executing AI education is challenging. The major frameworks are focused on identifying content and technical knowledge (AI literacy). Most of the current definitions of AI literacy for a non-technical audience are developed from an engineering perspective and may not be appropriate for K–12 education. Teacher perspectives are essential to making sense of this initiative. Literacy is about knowing (knowledge, what skills); competency is about applying the knowledge in a beneficial way (confidence, how well). They are strongly related. This study goes beyond knowledge (AI literacy), and its two main goals are to (i) define AI literacy and competency by adding the aspects of confidence and self-reflective mindsets, and (ii) propose a more comprehensive framework for K–12 AI education. These definitions are needed for this emerging and disruptive technology (e.g., ChatGPT and Sora, generative AI). We used the definitions and the basic curriculum design approaches as the analytical framework and teacher perspectives. Participants included 30 experienced AI teachers from 15 middle schools. We employed an iterative co-design cycle to discuss and revise the framework throughout four cycles. The definition of AI competency has five abilities that take confidence into account, and the proposed framework comprises five key components: technology, impact, ethics, collaboration, and self-reflection. We also identify five effective learning experiences to foster abilities and confidences, and suggest five future research directions: prompt engineering, data literacy, algorithmic literacy, self-reflective mindset, and empirical research.

This paper presents research on the connection and mutual influence between the socio-digital engagement of adolescents and their cognitive needs as well as attitudes towards learning. Within the socio-digital engagement of adolescents, using digital media for searching and processing information and life on social networks were considered. This research was conducted by examining the opinions of 14–19-year-old adolescents regarding their reading habits, using digital media, the Internet and social networks, their needs for cognition and their attitudes to learning. The results were obtained from 599 questionnaires consisting of 60 relevant questions distributed among a representative, focused sample of adolescents. The sample comprised seventh and eighth grade primary, grammar and high school pupils in the Republic of Serbia. The results of the research showed that there are statistically significant relationships between adolescents’ attitudes to their socio-digital engagement and their need for cognition and learning. This, on the one hand, means that the influence of socio-digital engagement among adolescents represents a useful indicator for the necessary changes in the institutionalised educational process in order to preserve the purpose and meaning of education. On the other hand, the types and characteristics of socio-digital engagement indicate those strategies which could be implemented in modern education, with the same goal.

Extreme conditions may require students to fine tune their remote online learning requiring knowledge about learning strategies. This form of knowledge may be captured by the term metaprocesses, which is defined as the knowledge and proficiency of cognitive (metacognition), emotional (metaemotion), and behavioral (metabehavior) learning strategies – and are hypothesized to shape learning outcomes. Undergraduate students were queried online during a COVID-19 lockdown (January 2021, N = 343, 62 % female) and again a few months later during a period of missile attacks (May 2021, N = 214, 41 % female). Path models suggest that perceived stress moderated the associations between metaprocesses and achievement outcomes (self-regulated learning, emotions, and engagement) somewhat differently for male and female undergraduate students. Robust findings provided evidence that metaemotion was positively associated with reappraisal emotion regulation of learning and metacognition was positively associated with engagement. For students low on perceived stress, metaprocesses were associated with outcomes, providing evidence that high levels of stress intrudes on coursework. Cumulative effects were found for perceived stress during the lockdown positively associated with negative deactivated emotions during the missile attacks, and negatively associated with cognitive and behavioral strategies for females. Challenges of learning during crisis may be eased when students have knowledge and proficiency about their learning strategies, suggesting potential benefits for resilience-building skills as part of academic learning.

There is an increasing need for computer science and engineering (CSE) online education. This study examined CSE online learners’ perceptions of self-directed learning (SDL) readiness, strategies, and satisfaction. The researchers surveyed 225 students and conducted semi-structured interviews with 15 online CSE students. The quantitative survey data and the qualitative interview data were analyzed using descriptive statistics and thematic analysis, respectively. The findings were as follows: (1) Both extrinsic motivation strategies (e.g., future career development, building a learning routine) and intrinsic motivation strategies (e.g., interest in learning) were utilized; (2) Diverse metacognition strategies (e.g., assignments, quizzes, and tests, discussing with peers, tracking progress, staying in touch with professors and teaching assistants) and cognitive strategies (e.g., watching recorded lectures, taking notes, reading books, seeking out information) were used to monitor learning; (3) Time-management (e.g., priority, checklist, fixed schedule, time-block for study) and resource management strategies (e.g., focused on lectures, textbook reading) were leveraged; (4) Specific designs or instruction promoted students’ SDL skills, such as access to documented learning materials, reminders sent from instructors, availability of the instructor, group interaction, and flexibility, and (5) Student satisfaction depended on the design of the course. The advantages and disadvantages of online learning were identified. The findings indicated that the instruction strategies and online course design are critical for CSE students’ SDL.

This research study explores the use of artificial intelligence (AI) in undergraduate assessments, specifically focusing on the ability of graduate teaching assistants (GTAs) to identify AI-generated assessments and the performance of ChatGPT, an AI model, in producing high-quality work. The study examines four guiding research questions and hypotheses related to the accuracy of GTA identification, the achievement of AI-generated work compared to student marks, the impact of GTA characteristics on identification accuracy, and the variation in identification and assessment across different subject areas. The study incorporates ten AI-generated assessments across seven classes taught by five GTAs. The findings reveal that ChatGPT consistently excelled the average student in all classes receiving 10 scores of A or higher out of 11 and receiving the top mark in 8 of the ten classes. GTAs accurately identified 50 % of the AI-generated assessments, with results suggesting a potential connection between class size and GTA accuracy in identifying AI-generated work. GTAs with prior experience and familiarity with ChatGPT demonstrated higher accuracy in identifying AI-generated assessments. However, further research is needed to explore this comprehensively. This study also reviews the effectiveness of TurnItin's new AI detector, highlighting an accuracy of 92 % across the ten assessments. The study highlights the adaptability of ChatGPT across different subject areas and assessment types, producing assessments that align with diverse educational contexts.

In conclusion, this research study contributes to understanding the effectiveness and adaptability of AI in undergraduate assessments. It underscores the need to further explore and develop AI technologies in education.

How do online students manage to balance studies, jobs, and private life – and yet succeed with their studies? Online studies typically attract students in jobs who seek to formalize their competence. Based on qualitative research design, this article sheds light on the academic learning of online students who attend a full-time program. Our focus narrows down to the inaugural class of online students enrolled in a Business and Administration bachelor's program. Specifically, our inquiry delves into the dynamic relationship between the academic progression of these students and their spatial and temporal activities. The article suggests that understanding this polycontextuality by using the metaphor of learning landscapes can help practitioners and students reflect on what influences their academic learning, which can help online students develop self-management skills. Self-management skills are essential in online education, and to help students develop such skills, educators must be aware of the individual learning landscapes of online students when designing online courses. In addition, our findings provide knowledge on how to on-board students in perennial online educational courses.

This article presents the results of a study conducted in collaboration with two universities – one in Lapland (Finland) and the other in Hong Kong (China) – during the development of an international university course on global media education. The objective of the study was to examine international students’ changing conceptual understanding of artificial intelligence (AI) literacy as part of the course. The need for this study arose from the recent rapid spread of AI across industries, which has connected human learning to machine learning. This requires competence in AI to contribute to future society. Five hours of online lectures on AI literacy were delivered during two workshops to students (N = 29) from 13 countries with no or limited prior knowledge of the subject in 2021 and 2022. The participants filled out pre- and post-workshop quantitative questionnaires and wrote diaries about their learning process, the development of their understanding of AI-literacy concepts, and their thoughts on the pedagogical approaches used. The quantitative data were analysed using a paired-samples t-test, while the qualitative data were examined using thematic-content analysis. The findings show that the students’ knowledge of AI and their awareness of the importance of AI literacy and media education increased significantly. Further research is needed so that a more appropriate curriculum can be designed for them. We outline some key activities that offer interactive and participatory ways to learn AI in order to assist educators in planning and delivering AI-literacy courses as part of cross-cultural media education.

Despite the proliferation of education technologies (EdTech) in education, past reviews that examine their effectiveness in the context of low- and middle-income countries are few and rarely seek to include studies published in languages other than English. This systematic review investigates the effectiveness of EdTech on primary and secondary student learning outcomes in China via a systematic search of both English- and Chinese-language databases. Eighteen (18) unique studies in 21 manuscripts on the effectiveness of EdTech innovations in China met the eligibility criteria. The majority of these evaluate computer aided self-led learning software packages designed to improve student learning (computer assisted learning, CAL), while the rest evaluate the use of EdTech to improve classroom instruction (ICI) and remote instruction (RI). The pooled effect size of all included studies indicates a small, positive effect on student learning (0.13 SD, 95% CI [0.10, 0.17]). CAL used a supplement to existing educational inputs – which made up the large majority of positive effect sizes – and RI programs consistently showed positive and significant effects on learning. Our findings indicate no significant differences or impacts on the overall effect based on moderating variables such as the type of implementation approach, contextual setting, or school subject area. Taken together, while there is evidence of the positive impacts of two kinds of EdTech (supplemental computer assisted learning and remote instruction) in China, more evidence is needed to determine the effectiveness of other approaches.

Current literature suggests that one of the impressive benefits of digital game-based language learning is its potential to create engaging vocabulary learning experiences. However, this literature is dominated by game-play approaches, rather than game-making approaches. Drawing upon constructivist, motivation, dual-coding, and constructionist theories, this study examined elementary English language learners’ vocabulary learning experiences in a game-based learning context and investigated their experience as they built their own games based on their understanding of different non-fiction texts. This mixed-methods case study involved novel use of network analysis to examine student learning, investigate the relationships between various aspects of learner experiences, and evaluate the strengths and weaknesses of the program. Findings indicated that a complex set of interdependencies between enactments of theoretical principles including generativity, agency, tinkering, intrinsic motivation, and contextualized learning lead to enjoyment and powerful language learning experiences. Design principles for future digital game-based language learning include optimizing visual aspects, spending more time on technical skill development, increasing learner agency, and focusing more on generative and joyful learning experiences.