IRIS Università degli Studi dell'Aquila
Not a member yet
68355 research outputs found
Sort by
Harnessing Generative AI for Inclusive Education: Opportunities and Challenges
No full textThe 21st century is the era where most technologies for education are being created; emerging technologies have been promising teaching and learning innovations for decades. At the same time, inclusive education aims to create equal opportunities for everyone, which can sometimes become counterintuitive for individual learning needs and experiences, especially when it comes to learners with specific needs. While assistive technologies have been extremely helpful for specific types of disabilities in the context of special education, this is not always the case in terms of educational technologies (tools and conceptual frameworks), which are at times too generic and not easily applicable to specific contexts. On the other hand, current technological developments such as generative artificial intelligence, while potentially bringing new opportunities and avenues for research and development, if left underproblematized and undertheorized, might even increase existing divides and favor different types of exclusion.
The most widely used models and frameworks for inclusive education generally provide a descriptive frame of reference for an aspirational view (i.e., Universal Design for Learning). However, actionable, innovative pedagogical strategies and methodologies that go hand in hand with emerging technologies are not easily available. This is true also in terms of creating individual learning experiences fitted for specific needs in terms of planning, implementing, and assessing learning outcomes. Furthermore, aligning inclusive learning principles with those of individual needs and plans of different learners requires careful reconsideration in line with different tools, modalities, spaces, and pedagogies. Moreover, with new, emerging technologies, inclusion issues and problems might even increase if we do not carefully consider related risks and challenges: for instance, we can already witness an emergence of an exponentially increasing gap between genAI-supported and unsupported languages. With emerging technologies such as generative AI, we should be able to create theoretical, conceptual, and technological artifacts that can tackle these gaps and provide strategies and tools ranging from policy to practice, focusing on educators and learners – especially in special and inclusive education.
This Research Topic aims to explore how emerging technologies, particularly generative AI, can be harnessed to foster inclusive education while addressing the potential risks and challenges that may arise. The main objectives include investigating how generative AI can be integrated into inclusive education frameworks, identifying the ethical considerations and policy implications, and developing innovative pedagogical strategies that cater to individual learning needs. Specific questions to be answered include: How can generative AI be used to support inclusive education? What are the ethical and policy challenges associated with its use? How can we ensure that emerging technologies do not exacerbate existing educational divides?
To gather further insights into the intersection of inclusive education and generative AI, we welcome articles addressing, but not limited to, the following themes:
- Generative AI and inclusive education: theory, practice, and policy
- Emerging technologies and special education: prospects for teacher professional development
- Ethics and policies for the use of Generative AI in special and inclusive education
- Reconceptualizing innovative education in the context of emerging technologies
- Challenges, risks, and visions for the educational future in the context of inclusive educatio
Le "faretrine" nuragiche. Considerazioni sulle modalità di contatto tra Sardegna ed Etruria nei secoli iniziali del primo millennio a.C.
No full text
Quality assurance and quality control of the 26m2 SiPM production for the DarkSide-20k dark matter experiment
Get PDFDarkSide-20k is a novel liquid argon dark matter detector currently under construction at the Laboratori Nazionali del Gran Sasso (LNGS) of the Istituto Nazionale di Fisica Nucleare (INFN) that will push the sensitivity for Weakly Interacting Massive Particle (WIMP) detection into the neutrino fog. The core of the apparatus is a dual-phase Time Projection Chamber (TPC), filled with 50 tonnes of low radioactivity underground argon (UAr) acting as the WIMP target. NUV-HD-cryo Silicon Photomultipliers (SiPM)s designed by Fondazione Bruno Kessler (FBK) (Trento, Italy) were selected as the photon sensors covering two 10.5m2 Optical Planes, one at each end of the TPC, and a total of 5m2 photosensitive surface for the liquid argon veto detectors. This paper describes the Quality Assurance and Quality Control (QA/QC) plan and procedures accompanying the production of FBK NUV-HD-cryo SiPM wafers manufactured by LFoundry s.r.l. (Avezzano, AQ, Italy). SiPM characteristics are measured at 77 K at the wafer level with a custom-designed probe station. As of March 2025, 1314 of the 1400 production wafers (94% of the total) for DarkSide-20k were tested. The wafer yield is 93.2±2.5%, which exceeds the 80% specification defined in the original DarkSide-20k production plan
Where diet, vegetation, and climate overlap: Guiding Vipera ursinii ursinii conservation through a converging approach
Get PDFThe meadow viper (Vipera ursinii ursinii) is a high-altitude specialist whose last systematic dietary analysis in Italy dates back more than 20 years. Here, we present an update in which an ensemble of field-collected data, remote sensing analyses, and modelling/post-modelling procedures converge into a unique, updated assessment to support conservation-aimed spatial planning, under current and future scenarios, for V. u. ursinii in its Central Apennines range. We analyzed field-collected fecal samples to determine dietary composition, checking the species' known specialization on Orthoptera. Based on this, we developed ecological niche models for all orthopteran species occurring at high elevations in the Central Apennines, further synthesizing those into a composite “typical Orthoptera” spatial data, using a biogeographic-based weighting approach. After confirming the high Orthoptera preference of V. u. ursinii, with some prey items identified for the first time in Italy at the genus or species level, we present distribution models for 51 orthopteran species. The resulting current and future (SSPs 3–7.0 and 5–8.5) prey distribution models were integrated into an existing distribution model of V. u. ursinii, refining its estimated environmental suitability by accounting for prey availability. Finally, we coupled these outputs with a map of June-to-September NDVI, averaged across 1999–2020, producing a zonation framework at the scale of each Central Apennines massif. We identified several suitable massifs, though some show suboptimal NDVI values. Gran Sasso consistently emerged as the stronghold under all scenarios, while SSP 5–8.5 projections highlighted declines in several massifs, underscoring the need for fine-scale conservation planning
Wireless Control With Channel State Detection and Message Dropout Compensation
Get PDFThis letter presents a framework for designing optimal state-feedback control that uses a wireless actuation link with imperfect channel state information to transfer the current and future control inputs that actuators can apply if future control messages are lost. The dropout compensation strategy supports scaling inputs to actuators when necessary. We analytically solve finite- and infinite-horizon control problems and present a necessary and sufficient stability condition for any given infinite-horizon state-feedback control law. We validate the results using an illustrative example
Towards a climate neutral European Union: the impact of environmental public policies on SMEs carbon energy phase-out
No full textContemporary and Future Perspectives on Thoracic Trauma Care: Surgical Stabilization, Multidisciplinary Approaches, and the Role of Artificial Intelligence
Get PDFBackground/objectives: Thoracic trauma remains a leading cause of trauma-related illness and death. Despite advances in imaging, ventilation strategies, and surgical fixation, its management remains a topic of debate, with varying practices across hospitals. Current Gaps: Although surgical stabilization of rib fractures (SSRF) has shown a mortality benefit in cases of flail chest and in elderly patients, its indications for non-flail cases remain uncertain. Analgesia strategies are evolving, and epidural remains the gold standard; however, it is limited by contraindications. In contrast, regional blocks, such as the erector spinae plane block (ESPB) and serratus anterior plane block (SAPB), are emerging as safer alternatives to opioid and thoracic epidural analgesia (TEA). Artificial intelligence (AI) is transforming imaging interpretation and risk stratification; however, its integration into daily trauma care is still in its early stages of development. Perspective: This article examines the integration of surgical innovation, regional anesthesia, and AI-powered diagnostics as integral components of future thoracic trauma care. We emphasize the importance of standardized surgical criteria, multimodal pain management approaches, and AI-assisted decision-making tools. Conclusions: Thoracic trauma care is shifting toward a personalized, multidisciplinary, and technology-enhanced approach. Incorporating evidence-based SSRF, advanced pain management techniques, and AI-supported imaging can help reduce mortality, enhance recovery, and optimize resource utilization
