Publications​

@article{ferraroMediCAREMedicalCollaborative2026,

title = {MediCARE: Medical Collaborative Agents REasoning over Interpretable Heterogeneous Graphs},

author = {Antonino Ferraro and Antonio Galli and Valerio La Gatta and Marco Postiglione and Giuseppe Riccio and Antonio Romano and Gian Marco Orlando and Diego Russo and Vincenzo Moscato},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0933365726000965},

doi = {10.1016/j.artmed.2026.103444},

issn = {09333657},

year  = {2026},

date = {2026-08-01},

urldate = {2026-06-22},

journal = {Artificial Intelligence in Medicine},

volume = {178},

pages = {103444},

keywords = {Collaborative LLMs, Explainable artificial intelligence, Graph Neural Networks, Large Language Models, Personalized medicine},

pubstate = {published},

tppubtype = {article}

}

@article{mondalConceptRuleGuided2026a,

title = {Concept and rule guided neural network for early crop leaf nutrient deficiency diagnosis},

author = {Semanto Mondal and Antonino Ferraro and Fabiano Pecorelli and Martina Iammarino and Giuseppe De Pietro},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0168169926003303},

doi = {10.1016/j.compag.2026.111735},

issn = {01681699},

year  = {2026},

date = {2026-07-01},

urldate = {2026-06-22},

journal = {Computers and Electronics in Agriculture},

volume = {248},

pages = {111735},

keywords = {Early crop nutrition deficiency, Fuzzy logic, Neurosymbolic, RAG, ResNet},

pubstate = {published},

tppubtype = {article}

}

@article{mondalConceptRuleGuided2026,

title = {Concept and rule guided neural network for early crop leaf nutrient deficiency diagnosis},

author = {Semanto Mondal and Antonino Ferraro and Fabiano Pecorelli and Martina Iammarino and Giuseppe De Pietro},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0168169926003303},

doi = {10.1016/j.compag.2026.111735},

issn = {01681699},

year  = {2026},

date = {2026-07-01},

urldate = {2026-06-22},

journal = {Computers and Electronics in Agriculture},

volume = {248},

pages = {111735},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mondal_concept_2026,

title = {Concept and rule guided neural network for early crop leaf nutrient deficiency diagnosis},

author = {Semanto Mondal and Antonino Ferraro and Fabiano Pecorelli and Martina Iammarino and Giuseppe De Pietro},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0168169926003303},

doi = {10.1016/j.compag.2026.111735},

issn = {01681699},

year  = {2026},

date = {2026-07-01},

urldate = {2026-06-16},

journal = {Computers and Electronics in Agriculture},

volume = {248},

pages = {111735},

abstract = {Crop nutrition deficiency poses a major challenge to achieving optimal yield, particularly in smallholder farming systems where timely expert diagnosis is limited. Early detection is crucial to minimize losses and reduce unnecessary fertilizer or pesticide usage. While deep learning offers potential for automated visual diagnosis, most existing approaches operate as black boxes and lack interpretability, explainability, or actionable recommendations. In this work, we present a neurosymbolic framework for early nutrient deficiency detection in ash gourd leaves using the EarlyNSD dataset. Our approach integrates a ResNet-50 backbone with a dual-head design: a classification head for deficiency prediction and a concept-prediction head that quantifies physiologically meaningful visual patterns such as yellowing, edge discoloration, spots, and vein greenness. These concept scores are combined with predefined domain rules to guide the learning of the neural component and to generate transparent, human-aligned explanations for each diagnosis. Building on the model outputs, we incorporate a Retrieval Augmented Generation (RAG)-based pipeline along with an agricultural knowledge base to generate targeted recommendations. This approach overcomes key shortcomings of pure neural models by incorporating domain knowledge in the form of differentiable fuzzy logic rules. The study demonstrates that the proposed framework improves both classification performance and interpretability compared to standard ResNet baselines. Grad-CAM analysis demonstrates that concept-guided attention aligns with symptom-specific regions, such as yellowed areas for Nitrogen deficiency or marginal discoloration for Potassium deficiency, providing visual validation of the reasoning process. Since EarlyNSD is limited in scale and visual diversity, the results are not directly comparable to large open-field datasets. Overall, our results establish a proof of concept for integrating neural detection with symbolic reasoning, enabling interpretable, actionable, and domain-informed nutrient management for practical applications.},

keywords = {Artificial Intelligence, Deep Learning, Early crop nutrition deficiency, Fuzzy logic, Neurosymbolic, RAG, ResNet},

pubstate = {published},

tppubtype = {article}

}

@article{demartinoSustainabilityMachineLearningEnabled2026,

title = {Sustainability of Machine Learning-Enabled Systems: The Machine Learning Practitioner’s Perspective},

author = {Vincenzo De Martino and Stefano Lambiase and Fabiano Pecorelli and Willem-Jan Van Den Heuvel and Filomena Ferrucci and Fabio Palomba},

url = {https://dl.acm.org/doi/10.1145/3777553},

doi = {10.1145/3777553},

issn = {1049-331X, 1557-7392},

year  = {2026},

date = {2026-07-01},

urldate = {2026-06-22},

journal = {ACM Transactions on Software Engineering and Methodology},

volume = {35},

number = {7},

pages = {1–41},

abstract = {Software sustainability is a key multifaceted non-functional requirement that encompasses environmental, social, and economic concerns, yet its integration into the development of Machine Learning (ML)-enabled systems remains an open challenge. While previous research has explored high-level sustainability principles and policy recommendations, limited empirical evidence exists on how sustainability is practically managed in ML workflows. Existing studies predominantly focus on environmental sustainability, e.g., carbon footprint reduction, while missing 

 the broader spectrum of sustainability dimensions and the challenges practitioners face in real-world settings 

 . To address this gap, we conduct an empirical study to characterize sustainability in ML-enabled systems from a practitioner’s perspective. We investigate (1) how ML engineers perceive and describe sustainability, (2) the software engineering practices they adopt to support it, and (3) the key challenges hindering its adoption. We first perform a qualitative analysis based on interviews with eight experienced ML engineers, followed by a large-scale quantitative survey with 203 ML practitioners. Our key findings reveal a significant disconnection between sustainability awareness and its systematic implementation, highlighting the need for more structured guidelines, measurement frameworks, and regulatory support.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ferraroExpLusionExplanationdrivenLate2026,

title = {ExpLusion: Explanation-driven Late Fusion for enhanced production process monitoring},

author = {Antonino Ferraro and Antonio Galli and Mosè Gallo and Valerio La Gatta and Marco Postiglione and Vincenzo Moscato},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0360835226003025},

doi = {10.1016/j.cie.2026.112101},

issn = {03608352},

year  = {2026},

date = {2026-07-01},

urldate = {2026-06-22},

journal = {Computers & Industrial Engineering},

volume = {217},

pages = {112101},

keywords = {Artificial intelligence in manufacturing, Data-driven artificial intelligence, Manufacturing industry, Predictive Maintenance, Proactive failure prevention, Quality control},

pubstate = {published},

tppubtype = {article}

}

@article{pappalardo_analyzing_2026,

title = {Analyzing the impact of functional approximation on the resilience of Deep Neural Networks},

author = {Salvatore Pappalardo and Salvatore Barone and Bastien Deveautour and Annachiara Ruospo and Ernesto Sanchez and Marcello Traiola and Alberto Bosio},

url = {https://www.sciencedirect.com/science/article/pii/S0141933126000165},

doi = {10.1016/j.micpro.2026.105259},

issn = {0141-9331},

year  = {2026},

date = {2026-06-01},

urldate = {2026-03-06},

journal = {Microprocessors and Microsystems},

volume = {122},

pages = {105259},

abstract = {This paper investigates the use of Approximate Computing (AxC), specifically functional approximation, to enhance the resilience of Deep Neural Networks (DNNs) against hardware faults in various applications, including safety-critical systems such as autonomous vehicles. As deploying DNNs requires balancing performance, energy efficiency, and reliability, traditional methods often achieve reliability through redundancy, which can increase area, power consumption, and latency. Our work shows preliminary results that leveraging approximate multipliers can, under some conditions, lead to energy reductions without compromising DNN resilience, under the right conditions. We evaluate the impact of approximation on DNN performance and robustness, exploring the interplay between energy efficiency and fault tolerance. Through comprehensive benchmarking, we highlight the potential of AxC to enable more efficient and reliable DNN implementations, paving the way for advanced applications in real-time and edge computing environments. Results obtained on four different DNNs show that it is possible to achieve up to a 3× reduction in power consumption without any negative impact on resilience.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chandranFunctionalAssessmentNeonatal2026,

title = {Functional Assessment of Neonatal Hypoxic–Ischemic Encephalopathy Using Long-Duration EEG and Interpretable Deep Learning Models},

author = {Athira Chandran and Lekshmi Chandrika Reghunath and Claudio Tomazzoli and Christian Napoli and Cristian Randieri},

url = {https://www.mdpi.com/2504-2289/10/6/175},

doi = {10.3390/bdcc10060175},

issn = {2504-2289},

year  = {2026},

date = {2026-06-01},

urldate = {2026-06-18},

journal = {Big Data and Cognitive Computing},

volume = {10},

number = {6},

pages = {175},

abstract = {Neonatal hypoxic–ischemic encephalopathy (HIE) remains a critical neurological emergency resulting from perinatal asphyxia, often leading to lifelong neurodevelopmental disabilities or mortality. The accurate and timely grading of HIE severity is paramount for initiating therapeutic interventions such as therapeutic hypothermia. This work proposes a diagnostic framework that uses long-duration electroencephalogram (EEG) recordings through a hierarchical classification strategy and advanced sequence modeling. A Hybrid Mamba-inspired architecture was developed to effectively capture long-range temporal dependencies in multi-channel neonatal EEG while maintaining computational efficiency. In order to enhance clinical consistency and initialize the models appropriately, a Self-Supervised Learning step based on Masked Signal Modeling is implemented with a mask ratio of 30%. The model structure takes into consideration clinically verified biomarkers, including the suppression ratio, Delta–Alpha Ratio, Spectral Edge Frequency, and Rhythmicity Index, extracted from signals at a microvolt level prior to normalization for physiological interpretability purposes. These features are combined with waveforms using feature gating. In an experiment conducted on a dataset of 169 records using 5-fold subject-wise cross-validation, the designed Hybrid Mamba-based model achieves significant stability and generalizability, achieving an accuracy score of 90%, with an average accuracy of 88.45% ± 6.8% per hierarchical level.},

keywords = {Deep Learning, Explainable AI, Healthcare},

pubstate = {published},

tppubtype = {article}

}

@article{ippolitoHowInteractionTechnological2026a,

title = {How the interaction between technological innovations and management control affects health performance accountability},

author = {Adelaide Ippolito and Raffaella Montera and Carmela Di Guida and Francesca Landolfi and Marco Sorrentino},

url = {https://www.emerald.com/qram/article/23/3/346/1335160/How-the-interaction-between-technological},

doi = {10.1108/QRAM-11-2024-0255},

issn = {1176-6093, 1758-7654},

year  = {2026},

date = {2026-05-01},

urldate = {2026-06-25},

journal = {Qualitative Research in Accounting & Management},

volume = {23},

number = {3},

pages = {346–369},

abstract = {Purpose 

 The purpose of this paper is to analyse how the effective interaction between management control and innovative information technologies gives rise to effective accountability of performance in the public sector, with particular reference to a public health organization. 

 Design/methodology/approach 

 This paper applied a retrospective longitudinal case study method for understanding how performance accountability is influenced by interaction between management control and innovative information technology. The research considered the Chronic Care system of the Local Health Authority (LHA) of Caserta (Italy). 

 Findings 

 The retrospective longitudinal case study highlights how the effective interaction between management control and innovative information technologies allows an effective accountability of performance in the LHA Caserta analysed, although such technological innovations derive from a package of management control systems that has been stratified over time. 

 Research limitations/implications 

 The limitation of this paper is that only one case study is analysed, albeit in depth, while it would be interesting to consider more public health organizations. 

 Originality/value 

 This research contributes to the literature confirming that, although management control flows are the result of a management control package that has been formed over time, it is possible to promote a profitable integration between management control and IT innovations for fostering an effective performance accountability.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karthikLightweightEdgeAI2026,

title = {Lightweight Edge AI Hardware-Oriented Photovoltaic Fault Detection Using Generative Augmentation with Potential Drone-Based Inspection Applications},

author = {Gandrothu Karthik and Namburi Rupesh and Joel John and Rayappa David Amar Raj and Claudio Tomazzoli and Cristian Randieri},

url = {https://www.mdpi.com/2504-446X/10/6/422},

doi = {10.3390/drones10060422},

issn = {2504-446X},

year  = {2026},

date = {2026-05-01},

urldate = {2026-06-18},

journal = {Drones},

volume = {10},

number = {6},

pages = {422},

abstract = {To ensure the reliability and sustained performance of industrial photovoltaic (PV) systems, fault detection frameworks must achieve both high detection accuracy and computational efficiency, particularly for deployment on resource-constrained edge platforms. This work proposes a lightweight and low-latency photovoltaic defect detection framework that integrates DCGAN-based generative augmentation with the proposed GhostViT-YOLOv10n architecture. The augmentation strategy helps address class imbalance, improve representation of rare defects, and enhance generalization capability in electroluminescence (EL) imagery through structured geometric and photometric transformations. The proposed framework integrates lightweight Ghost-based optimization, Cross-Stage Partial Fusion (C2f), Spatial Pyramid Pooling—Fast (SPPF), MobileViT contextual learning, and SimAM-based attention refinement to improve multi-scale feature extraction while maintaining low computational complexity. Experimental evaluation on the PVEL-AD and PV Multi Defect benchmark datasets demonstrates strong detection performance. On the PVEL-AD dataset, the BaseLine achieves a mAP@0.5 of 71.6% with only 2.7 M parameters and 8.4 GFLOPs, while our proposed GhostViT-YOLOv10n framework with DCGAN-enhanced version further improves detection performance to 93.6% mAP@0.5 with only 2.19 M parameters and 6.6 GFLOPs. On the PV Multi Defect dataset, the BaseLine achieves a mAP@0.5 of 74.0% with 2.71 M parameters and 8.4 GFLOPs, and the optimized framework with DCGAN-augmented configuration further improves performance to 95.4% mAP@0.5 with 2.58 M parameters and 7.7 GFLOPs. These results demonstrate the effectiveness of combining lightweight architectural optimization with generative augmentation for improving rare defect representation and multi-scale photovoltaic defect detection. To validate practical deployment feasibility, the optimized framework was deployed on a Raspberry Pi 5 using ONNX Runtime under CPU-only conditions. The deployed model achieved an average inference time of 43.05 ms and a real-time processing speed of 23.23 FPS while maintaining moderate CPU utilization and stable thermal behavior. These deployment results demonstrate the suitability of the proposed framework for lightweight edge-oriented photovoltaic inspection applications without requiring GPU acceleration. All evaluations were conducted exclusively on real test datasets, while synthetic samples were used only during training to improve data diversity and rare defect representation. Overall, the proposed framework provides a balanced solution that combines detection accuracy, computational efficiency, lightweight edge deployment capability, and generative augmentation for practical photovoltaic defect inspection applications with potential suitability for future drone-assisted inspection scenarios.},

keywords = {Artificial Intelligence, Drones, Edge AI, Edge computing},

pubstate = {published},

tppubtype = {article}

}

@article{nenna_impact_2026,

title = {Impact of digital technologies on pediatric asthma care},

author = {Raffaella Nenna and Sara Manti and Giuliana Ferrante and Velia Malizia and Pietro Alfano and Giuseppe Fabio Parisi and Domenico Paolo La Regina and Luigi Gallo and Alessandra Pandolfo and Amelia Licari and Stefania La Grutta},

url = {https://www.pediatric-respiratory-journal.com/impact-of-digital-technologies-on-pediatric-asthma-care/},

doi = {10.56164/PediatrRespirJ.2026.01},

issn = {3035-2134},

year  = {2026},

date = {2026-04-01},

urldate = {2026-04-03},

journal = {Pediatric Respiratory Journal},

volume = {04},

number = {01},

pages = {2–15},

publisher = {Edra Media S.r.l.},

abstract = {Asthma is one of the most common chronic diseases in children, significantly impacting their health, quality of life, and healthcare systems globally. Pediatric asthma accounts for substantial morbidity, including frequent exacerbations, emergency department visits, and missed school days. Despite the availability of effective treatments and clear management guidelines, achieving optimal asthma control remains a challenge. In recent years, digital technologies have emerged as transformative tools in asthma care, offering new ways to monitor, educate, and treat pediatric patients. A systematic review was conducted to examine the impact of digital technologies on pediatric asthma care, synthesizing evidence on their effectiveness, challenges, and future directions. Covering studies from January 2020 to December 2024, the review analyzed 59 primary studies that involved mobile health (mHealth) applications, electronic medication monitoring systems, wearable devices, artificial intelligence (AI)-powered solutions, and school-based telemedicine programs. Findings reveal that mHealth applications and serious games promote self-management, improve medication adherence, and support patient education. Telemedicine, including school-based and remote patient monitoring, enhances care accessibility, reduces emergency visits, and promotes continuity of care, particularly in underserved populations. Wearable devices and electronic monitoring tools enhance symptom tracking and evaluation of inhaler technique. AI-driven interventions, such as digital twin systems, show promise in personalizing treatment and predicting exacerbations. Despite encouraging outcomes, challenges remain, including digital literacy gaps, limited access to devices and the internet, and difficulties integrating digital tools into clinical workflows. Usability and sustainability vary widely depending on design approaches, caregiver engagement, and infrastructure readiness.},

keywords = {Artificial Intelligence, Asthma, Healthcare, Mobile Application, Serious games},

pubstate = {published},

tppubtype = {article}

}

@article{generosiFacialExpressionbasedAssessment2026,

title = {Facial expression-based assessment of emotional engagement under multimodal stimuli},

author = {Andrea Generosi and Milena Martarelli and Paolo Castellini and Maura Mengoni},

url = {https://link.springer.com/10.1007/s44163-026-01216-0},

doi = {10.1007/s44163-026-01216-0},

issn = {2731-0809},

year  = {2026},

date = {2026-04-01},

urldate = {2026-06-17},

journal = {Discover Artificial Intelligence},

volume = {6},

number = {1},

pages = {511},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{agostinelli_comparing_2026,

title = {Comparing Virtual and Augmented Reality Applications for Museums: User Immersion and Learning Performance},

author = {Thomas Agostinelli and Silvia Ceccacci and Andrea Generosi and Maura Mengoni},

url = {https://dl.acm.org/doi/10.1145/3811814},

doi = {10.1145/3811814},

issn = {1556-4673, 1556-4711},

year  = {2026},

date = {2026-04-01},

urldate = {2026-06-17},

journal = {Journal on Computing and Cultural Heritage},

pages = {3811814},

abstract = {This study investigates the impact of Augmented Reality and Virtual Reality technologies on User Immersion and Learning Performance in museum settings. A quasi-experimental study, conducted in the context of an archaeological virtual museum, compares the effects of AR and VR interfaces on User Immersion and Learning Performance. Participants (n = 176) interacted with digital replicas of archaeological artefacts via AR and VR interfaces, followed by assessments using the Augmented Reality Immersion (ARI) questionnaire and knowledge-based multiple-choice tests. The use of the ARI questionnaire made possible a comprehensive investigation of the effect of User Immersion (and not only some of its aspects, e.g., Presence or Flow) on Learning Performance in the context of Cultural Heritage. The results revealed that AR users achieved higher Learning Performance, while VR users experienced a greater immersive experience. Significant correlations were found between higher User Immersion levels and lower Learning Performance. These findings highlight the importance for Museum curators to find a balance between exploiting technology to entertain and maintaining the educational integrity of the exhibits.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{carere_uncertainty_2026-1,

title = {Uncertainty analysis of encoder-based method for the calibration design of on-board vehicle speed meters},

author = {Federico Carere and Luca Podestà and Gianfranco Miele and Silvia Sangiovanni and Marco Laracca},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0263224126005191},

doi = {10.1016/j.measurement.2026.120810},

issn = {02632241},

year  = {2026},

date = {2026-04-01},

urldate = {2026-06-18},

journal = {Measurement},

volume = {271},

pages = {120810},

abstract = {Vehicle speed meters have an important role in various application fields such as monitoring and controlling speed limit for safety purposes, evaluating driver and vehicle performance, managing the traffic in real-time, etc. Compliance with ISO/IEC 17,025 and the ILAC (International Laboratory Accreditation Cooperation) policy on measurement traceability is required in calibration procedures to guarantee the quality of traceability of the results obtained by speed meters. The scientific literature, as well as the activity of some calibration centers, reports a variety of methodologies for calibration. Designing the most suitable calibration procedure for a specific category of instrument is not an easy task due to the high number of parameters affecting the calibration uncertainty. In this context, the paper proposes a design tool for encoder-based methods for the calibration of on-board vehicle speed meters. After the definition of all the measurement uncertainty contributions involving the calibration process, a sensitivity analysis was carried out to assess the applicability and limitations of the selected calibration method for speeds up to 300 km/h. Afterwards, a design tool is proposed to enable the optimal design of the calibration method aiming to find the best trade-off between technical requirements (calibration uncertainty, speed range) and economic aspects.},

keywords = {Calibration, Design Tool, Encoder, Metrological Traceability, Speed Measurement, Uncertainty},

pubstate = {published},

tppubtype = {article}

}

@article{vergalloNetCarbTraceProbingTool2026,

title = {NetCarbTrace: A Probing Tool to Measure and Explore the Carbon Footprint of Computer Networks},

author = {Roberto Vergallo and Antonio Campa and Simone Casciaro and Giovanni Ciccarese and Antonio Mongelli and Luca Mainetti},

url = {https://ieeexplore.ieee.org/document/11269323/},

doi = {10.1109/MS.2025.3636577},

issn = {0740-7459, 1937-4194},

year  = {2026},

date = {2026-03-01},

urldate = {2026-06-19},

journal = {IEEE Software},

volume = {43},

number = {2},

pages = {70–77},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{galloEditorialAIInnovations2026,

title = {Editorial: AI innovations in education: adaptive learning and beyond},

author = {Luigi Gallo and Maria Concetta Carruba and Antonino Ferraro and Henrik Hautop Lund and Angelo Rega and Stefano Triberti},

url = {https://www.frontiersin.org/articles/10.3389/fcomp.2026.1822456/full},

doi = {10.3389/fcomp.2026.1822456},

issn = {2624-9898},

year  = {2026},

date = {2026-03-01},

urldate = {2026-06-22},

journal = {Frontiers in Computer Science},

volume = {8},

pages = {1822456},

keywords = {adaptive learning, AI literacy, artificial intelligence in education (AIeD), ethical AI, Generative AI, human-AI synergy, multimodal learning analytics (MMLA), personalized learning},

pubstate = {published},

tppubtype = {article}

}

@article{gallo_editorial_2026,

title = {Editorial: AI innovations in education: adaptive learning and beyond},

author = {Luigi Gallo and Maria Concetta Carruba and Antonino Ferraro and Henrik Hautop Lund and Angelo Rega and Stefano Triberti},

url = {https://www.frontiersin.org/articles/10.3389/fcomp.2026.1822456/full},

doi = {10.3389/fcomp.2026.1822456},

issn = {2624-9898},

year  = {2026},

date = {2026-03-01},

urldate = {2026-04-03},

journal = {Frontiers in Computer Science},

volume = {8},

pages = {1822456},

publisher = {Frontiers Media SA},

abstract = {Artificial Intelligence (AI) is gradually transforming educational practices. AI-powered teaching assistants, large language models, and multimodal analytics platforms are reshaping how learning experiences are designed and assessed. However, AI integration is not merely a technological matter: it is also heavily influenced by pedagogical, psychological, and sociocultural factors. Beyond technical implementation, AI systems can be framed within a human augmentation perspective, where technologies enhance sensory, motor, and cognitive processes in hybrid environments (Augello et al., 2022), including immersive contexts in which presence and cognition dynamically interact (Palombi et al., 2023). At the same time, advances in adaptive and data-driven AI, including explainable and diversity-aware approaches, highlight the role of algorithmic choices in shaping users' experiences and behaviors (Ferraro et al., 2025). Recent studies further show that, while educators are using AI tools for instructional purposes (e.g., creating teaching materials), concerns remain about the risk of unfair AI use and the difficulty of detecting it (Amato et al., 2023; Carruba et al., 2025). Accordingly, research on AI in education is becoming increasingly focused on factors that support implementation and adoption in real-life contexts, beyond mere improvement of algorithms from a computer science perspective (Triberti et al., 2024; Acosta-Enriquez et al., 2025; Galindo-Domĺnguez et al., 2024). 

Against this backdrop, this Research Topic (RT), which spans four Frontiers journals, focuses on empirical and theoretical aspects of personalized and adaptive learning. More specifically, it examines the role of AI in fostering inclusive, data-driven, and emotionally responsive educational ecosystems, with particular attention to motivation, beliefs, creativity, self-regulation, and ethics. 

For analytical clarity, the contributions are organized into six interrelated thematic areas: AI Adoption, Acceptance, and Self-Regulation; Teacher AI Literacy and Sustainable Integration; Adaptive, Immersive, and AI-Enhanced Learning Environments; Multimodal Analytics, Assessment, and Predictive AI; Learner Psychological, Cognitive, and Sociocultural Factors; Conceptual, Ethical, and Human-AI Synergy Perspectives. These areas reflect three broader dimensions of current research: the adoption of AI by learners and teachers, the design of intelligent learning environments, and the broader psychological and ethical implications of AI-supported education.},

keywords = {adaptive learning, AI literacy, Artificial Intelligence, artificial intelligence in education (AIeD), Education, ethical AI, Generative AI, human-AI synergy, multimodal learning analytics (MMLA), personalized learning},

pubstate = {published},

tppubtype = {article}

}

@article{carere_uncertainty_2026,

title = {Uncertainty Effects on Smart Grid Services for Low-Voltage Distribution Networks},

author = {Federico Carere and Tommaso Bragatto and Alberto Geri and Silvia Sangiovanni and Marco Laracca},

url = {https://www.mdpi.com/1424-8220/26/6/1800},

doi = {10.3390/s26061800},

issn = {1424-8220},

year  = {2026},

date = {2026-03-01},

urldate = {2026-06-18},

journal = {Sensors},

volume = {26},

number = {6},

pages = {1800},

abstract = {This study investigates the impact of monitoring infrastructure characteristics (specifically sensor penetration and measurement accuracy) on the effectiveness of voltage regulation and congestion management within distribution networks. As distribution system operators transition toward active management, the integration of Distributed renewable Generation (DG) and demand response introduces significant physical and cyber-physical uncertainties. To address these challenges, a smart grid service framework has been employed to optimize flexibility resources from aggregated users and DG inverters through a genetic algorithm. The framework was tested on the IEEE European Low Voltage Test Feeder across various scenarios defined by distributed monitoring systems’ penetration and their measurement accuracy. Results show that sensor penetration has a dominant impact: increasing monitoring coverage from 0% to 100% raises the percentage of cases with fewer than one residual congestion from 46.2% to 91.9% (sensors with an accuracy class of 2%), reaching 97.9% with an accuracy class of 0.5%, while voltage violations are eliminated under full monitoring. These findings suggest that widespread sensor deployment, with a suitable measurement accuracy, is a fundamental prerequisite for reliable and efficient smart grid operation.},

keywords = {Distributed energy resources, Genetic algorithms, Measurement uncertainty, Sensor penetration, Smart grids, Voltage regulation},

pubstate = {published},

tppubtype = {article}

}