Here in this short note, we explain the hypotheses leading to the time averaged system of equations. In time averaged equations we can distinguish two different time scales: one is “Slow” time scales. The other is “Rapid or Fast” time scales. These time scales will be explained here.

This article draws a comparison between Sam Altman, the prominent advocate of artificial intelligence (AI) through OpenAI, and Theuth, the legendary inventor of writing in Plato’s ‘Phaedrus’. Just as Theuth presented writing as an infallible remedy for forgetfulness, a means to enhance memory and wisdom, and a way to improve the Egyptians’ education, Altman extols AI as a revolutionary educational tool capable of personalising learning, equalising opportunities for rich and poor pupils alike, and accelerating scientific and human progress exponentially. For example, Altman asserts that AI will offer personalised tuition to everyone, combining the strengths of human teachers with the efficiency of algorithms, thus transforming educational systems in the coming decades. However, we argue that, much like writing according to King Thamous, AI risks promoting the systematic delegation of intellectual work rather than deep, internalised learning. This can lead to a presumption of knowledge, increased cognitive dependence, a decline in critical thinking skills and an erosion of intrinsic motivation. Through a review of ancient philosophical literature and contemporary scientific studies, as well as empirical examples drawn from recent research on the observed decline in the intellectual abilities of secondary school and university students, we advocate for the critical, measured and ethical adoption of cognitive technologies. Indeed, research shows that excessive use of tools like ChatGPT reduces neural engagement by 20-30%, promotes intellectual laziness, impairs long-term memory retention and increases the risk of algorithmic bias, thereby perpetuating Thamus’s analytical error of confusing rapid access to information with authentic knowledge acquisition. This article proposes a theoretical and practical framework for the ethical integration of AI, promoting active pedagogical approaches that preserve cognitive autonomy and encourage critical thinking. It also incorporates AI-literacy training to counteract any harmful effects. Ultimately, the article invites broader reflection on the long-term societal implications of AI in education, with the aim of avoiding a collective impoverishment of wisdom while ensuring that the technology does not erode the foundations of human thought. We also emphasise the need for longitudinal studies to assess this technology’s reversible impacts, taking into account both Altman’s optimistic perspectives and Plato’s philosophical warnings.

Keywords: AI; Myth of Theuth; education; cognitive decline; cognitive offloading; personalised learning; technological dependence; critical thinking; educational innovation

Rare diseases affect millions of people worldwide. They are characterised by prolonged diagnostic uncertainty, fragmented care pathways and significant logistical and informational barriers. Current hospital information systems and conventional artificial intelligence (AI) approaches are primarily designed for common conditions and remain poorly adapted to the sparse, heterogeneous and longitudinal nature of rare disease data. This article critically reviews recent advances in AI applied to rare diseases, including electronic health record (EHR) analysis, anomaly detection, multimodal phenotyping, large language models (LLMs) and agent-based systems. Based on these developments, the paper presents a practitioner–patient framework centred on the continuous identification of unusual data, rather than direct automated diagnosis. This framework combines anomaly detection models, natural language processing, phenotypic matching based on the Human Phenotype Ontology (HPO), multimodal data integration and explainable AI mechanisms, supporting clinical vigilance throughout the care pathway. In parallel, a secure, multimodal AI assistant is introduced to empower patients by simplifying medical information, improving coordination and reducing administrative burdens. Unlike existing approaches, which focus primarily on early diagnosis, the proposed framework aims to minimise bottlenecks throughout the patient journey, while ensuring human clinical oversight is maintained and compliance with emerging regulatory requirements, such as the GDPR and the EU AI Act, is achieved. The article discusses the technical, ethical, organisational and regulatory challenges associated with such systems, and highlights the potential of AI to improve the timeliness of diagnoses, care coordination and patient autonomy in the context of rare diseases.

Keywords: rare diseases; artificial intelligence; electronic health records; anomaly detection; multimodal phenotyping; large language models; explainable AI; patient pathway optimisation; clinical decision support; healthcare interoperability

This study explores innovative pathways for developing a dynamic inheritance model of Yingge dance (a traditional Chinese folk art) through “teaching-learning-transmitting-innovation” in higher education institutions, driven by big data technology. By establishing a closed-loop system for data collection, analysis, and application, the research achieves personalized teaching pathway design, intelligent dissemination network construction, and optimized collaborative innovation mechanisms, thereby reshaping the inheritance ecosystem of traditional skills. The findings indicate that technology-driven empowerment can significantly enhance inheritance efficiency and cultural identity, while requiring ethical boundaries to balance data privacy protection and cultural authenticity preservation. Furthermore, multi-stakeholder collaboration mechanisms are proposed to overcome resource integration barriers.

Keywords: Big Data-Driven; Yingge Dance; Living Inheritance; Digital Innovation; Collaborative Mechanism

Artificial Intelligence Tutoring Systems (AITSs) have begun to reshape higher education by providing adaptive, personalized support to learners across diverse academic contexts. Building upon prior work examining the implementation of the Chatbase-powered tutor, this study explores the subsequent phase of institutional integration, evaluation, and pedagogical refinement following the initial pilot implementation. As institutions continue to experiment with AI-enabled learning environments, it becomes increasingly necessary to understand how these systems evolve from experimental deployments into sustainable components of academic ecosystems.

This paper examines how the expansion of AI tutoring systems influenced instructional practices, institutional decision-making, and student learning support structures. Drawing on established theoretical frameworks including Vygotsky’s Zone of Proximal Development, Expectancy-Value Theory, and Self-Determination Theory, the study analyzes how AI tutors function not only as automated support systems but also as catalysts for pedagogical innovation.

Following the early pilot phase, the institution began examining the broader institutional implications of AITSs, including faculty adoption patterns, ethical governance, data-driven instructional insights, and expanded accessibility considerations. Early evidence suggests that AI tutoring systems can enhance learning experiences by enabling scalable academic support while also generating valuable data that informs instructional design improvements.

The findings highlight the importance of responsible implementation strategies, continuous faculty development, and transparent governance models to ensure that AI systems augment rather than replace human teaching expertise. As AI continues to reshape educational landscapes, institutions must consider both the opportunities and the complexities associated with integrating adaptive tutoring technologies into their pedagogical infrastructures.

Keywords: Artificial Intelligence; Personalized Learning; Tutoring Systems; Higher Education Innovation