Keyword: generative 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.