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The Ancient Greeks foresaw non-human automata and the power of dialogic learning, but Generative AI and AgenticAI afford the prospect of going beyond interlocutor to co-creator in an empowering partnership between learner and AI agent to address ‘whole person’ education. This exploratory study reviews existing conceptual models and implementations of learning with AI before proposing the novel and original architecture of a human–AgenticAI learning system. In this, the learner and human tutor are each supported by AI assistants, and an AI tutor coordinates the generation, presentation and assessment of adaptive learning activities requiring the partnership of learner and AI assistant in the co-creation of learning outcomes. The proposed model is significant for incorporating 21st-century skills in a diversity of realistic learning environments. It tracks a formative assessment pathway of the learner’s contribution to co-created outcomes through to the compilation of a summative achievement portfolio for external warranting. Although focused upon learning in universities, the model is transferable to other educational milieux.

This study evaluated the relationship between information technology (IT) and competitiveness (CP), emphasizing the different dimensions of IT capabilities, including customer relationship management (CRM) and human resource management (HRM). Also, the mediating role of innovative performance (IP) was examined in the link between IT use and CP. Data were collected in 2023 through a standard questionnaire, whose validity and reliability were confirmed by experts and statistical tests. Then, 172 valid responses were analyzed after determining the minimum sample size using Cochran’s formula. SPSS version 25 was used for descriptive analyses and preliminary tests, while SmartPLS 3.3.3 was utilized for structural equation modeling and hypothesis testing. The findings indicated that the use of IT components enhances CP, and IP mediates this relationship. This research contributes to the theoretical development of innovation management and IT by highlighting the transmission mechanism of IP rather than focusing solely on the direct relationship. This study, conducted among Iranian small and medium-sized enterprises (SMEs), also fills a gap in global literature, especially in developing countries, and offers practical insights.

This paper deals with unwanted spatial distortion in virtual environments and its impact on the construction of metaverse environments that require high precision, especially in fields with specific requirements, such as medicine. At the same time, it presents the main technical factors leading to this phenomenon. The paper also emphasizes that data reliability is the first factor that needs to be analyzed and evaluated. Through a comprehensive analysis of the limitations of traditional methods and the development trend of techniques based on Artificial Intelligence (AI), a data processing method based on the Gaussian process regression method is proposed. Through experiments and result analysis, this method significantly improves data reliability, thereby enhancing the accuracy of avatar motion simulation in the virtual environment of the metaverse. Future research trends include further improvement of processing accuracy and speed; deploying on real devices; expanding the research into other factors contributing to unintended spatial distortions; exploring and applying appropriate processing techniques and technologies to enhance simulation reliability in virtual metaverse environments.