Keyword: artificial intelligence

ABSTRACT: Generative artificial intelligence (AI) is rapidly transforming important areas of human activity, yet adults’ acceptance of generative AI applications remains insufficiently understood. This cross-sectional study examined the acceptance of generative AI applications among 460 adults recruited through online convenience sampling. Data were collected using the generative artificial intelligence acceptance scale (GAIAS), a validated instrument based on dimensions derived from the unified theory of acceptance and use of technology (UTAUT). The scale comprised four dimensions: performance expectancy, effort expectancy, facilitating conditions, and social influence. Participants reported moderate to high overall acceptance of generative AI. Effort expectancy, reflecting perceived ease of use (PEOU), received the highest mean score, followed by performance expectancy, reflecting perceived usefulness (PU), whereas social influence received the lowest mean score. This descriptive pattern indicates that perceived usability and usefulness were rated more positively than perceived social influence within the present sample. After Holm-Bonferroni correction across the 25 primary comparisons, statistically significant group differences remained for age and educational level in overall generative AI acceptance, performance expectancy, and effort expectancy. No multiplicity-adjusted differences were identified according to gender, personal computer ownership, or previous computer training. The age- and education-related findings should not be interpreted as evidence of linear relationships, independent effects, or causation. Within this digitally connected convenience sample, the findings indicate that PEOU and PU were more prominent dimensions of generative AI acceptance than social influence. Age- and education-related group differences were also observed. However, the cross-sectional design, online convenience sampling, and marked subgroup imbalances limit causal interpretation and generalizability to the wider adult population. The findings may nevertheless inform the development of accessible generative AI tools and targeted digital-literacy initiatives.

ABSTRACT: The widespread free access to generative artificial intelligence (GenAI) has radically shifted the educational landscape. Schools find themselves largely unprepared to navigate this new terrain: both in understanding how to harness the potential of GenAI for educational enhancement and developing strategies to ensure student integrity in demonstrating authentic learning outcomes. This study reports on a design-based research collaboration between a regional Australian university and a secondary school to address these challenges through policy and practice innovation. The aim was to both promote and maintain academic integrity, alongside considering how GenAI might be harnessed for educational benefit and lifelong learning. Over six months, researchers and educators worked collaboratively to analyze school staff needs using a whole-school survey and focus groups, followed by co-design of policy and pedagogical responses, which were iteratively tested and refined. Key findings revealed that staff required clear guidance on acceptable GenAI use, consistent policy enforcement, and professional learning. The project has resulted in the development of policy, procedure, and support resources that have been released as an Open Education Resource. The findings offer practical insights for schools navigating GenAI integration and contribute to broader discussions on educational change, assessment validity, and ethical technology use.

ABSTRACT: Collaboration between humans and artificial intelligence (AI) has the power to transform education, but research has yet to fully address how students engage cognitively with AI-powered feedback. Although studies suggest that AI can improve writing, few explore how students perceive and interact with AI tools. To fill this gap, the present study investigated Chinese university students’ perceptions and experiences of using AI-powered English writing feedback tools: automated writing evaluation, generative AI, and corpora. Two hundred and ten student reflective journals were subjected to qualitative thematic analysis using NVivo software, along with analysis of classroom observations and students’ writing. The students evaluated AI-powered feedback tools in three dimensions: content quality, delivery method, and overall effectiveness. They felt that these tools provided better grammar correction, instant feedback delivery, and an enhanced user experience, but challenges included vague explanations, limited emotional connection, and risks of overreliance. Based on these insights, this study introduces the Student-Teacher-AI Collaboration Model for feedback writing, which is designed to enhance collaboration between students, teachers, and AI in foreign language education. The findings have practical implications for the integration of AI tools into writing instruction and will inform policymaking in the rapidly evolving educational field.

ABSTRACT: Artificial intelligence (AI) has advanced rapidly in recent years and has become widely integrated across various fields, including education. This paper seeks to provide a comprehensive examination of the current state of AI in education by exploring its potential to revolutionize learning experiences through personalized approaches and data-driven multifaceted tools, while also highlighting important challenges that require consideration to ensure its responsible development and implementation. AI shows great promise to personalize instruction for each student based on assessments of their individual strengths, weaknesses, interests, and learning preferences. However, several challenges still necessitate careful examination of AI’s implications on education. Issues like algorithmic bias, the digital divide between socioeconomic groups, and concerns around reduced critical thinking skills all require addressing. If not developed and applied judiciously with these challenges in mind, AI risks exacerbating rather than alleviating existing inequities and hindering the cultivation of higher-order cognitive abilities. Through a comprehensive review of the relevant literature regarding AI’s current and potential roles in education, this paper identifies several key considerations around learning outcomes, challenges, and implications. Findings from interpretative structural modeling analysis also reveal the importance of balancing AI capabilities with safeguarding against potential downsides like those mentioned above. It is imperative that AI integration in education is approached responsibly with an understanding of both its promise and risks to learning to ensure its successful and equitable implementation for all students.

ABSTRACT: Amid the COVID-19 pandemic, design education experienced a significant shift as traditional studio teaching went online. This transition coincided with industry demands for adaptable, technology-proficient graduates prepared to work and collaborate as part of a decentralized workforce. This study examines how design educators in seven countries adapted their post-pandemic studio pedagogy to align with these industry needs. An online survey was used to efficiently reach the wide, geographically dispersed participant pool of educators. Findings indicate a rising acceptance of online technologies in studio teaching. The majority of design educators are now incorporating online elements into their design teaching. Pre-recorded lectures, online feedback and critique sessions, self-paced learning activities, and the use of cloud-based collaboration tools are among the most frequently employed methods. Nearly a third of surveyed educators are even considering teaching fully online design courses. This shift reflects a forward-thinking approach aimed at better aligning design education and industry. However, the study also highlights the importance of remaining open to disruptive technologies like generative artificial intelligence which is currently reshaping the design industry and work practices.

ABSTRACT: There is a growing interest in using generative artificial intelligence (AI) for educational purposes within the higher education environments, while AI applications (such as ChatGPT) can transform traditional teaching and learning methods. ChatGPT is an advanced AI tool that generates new content and human-like responses. The purpose of this paper is to use ChatGPT as a research assistant in order to explore ways AI can be harnessed to enhance pedagogical practices in higher education. This is a qualitative study, in which the output and responses generated by ChatGPT provided a starting point for the investigation. AI can be harnessed to enhance pedagogical practices in higher education in various ways including personalized learning, automated assessment and feedback generation, virtual assistants and chatbots, content creation, resource recommendation, time management, language translation and support, research assistance, simulations, and virtual labs. Other educational affordances that can strengthen the teaching and learning experience regard collaboration and communication, accessibility and inclusivity, as well as AI literacy. When implementing AI tools such as ChatGPT in higher education, ethical considerations (e.g., data privacy, transparency, accessibility, cultural sensitivity), potential misuses, and concerns need to also be addressed. Although ChatGPT can aid the generation of content and ideas for further exploration, it is a complementary-supportive tool, and its output necessitates human evaluation and review. The integration of ChatGPT and other AI tools in the higher educational process/practices has implications for educators, students, design of curricula, and university policy makers.