ABSTRACT The rise of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) is accelerating the integration of social robots into education. These technologies enhance robots' abilities in natural language interaction, adaptive behaviour, and personalised learning support. To advance real-world implementation, it is essential to identify the main challenges and opportunities in this field. We conducted a two-round Delphi study with 16 experts in human-robot interaction and educational technology. In the first round, participants outlined opportunities, challenges, and potential robot roles expected in the short term (1 year) and medium term (5 years). Content analysis revealed 8 opportunities, 10 challenges and 10 roles. In the second round, experts ranked their importance and feasibility across both time horizons. The results show that the most critical opportunities and challenges are also the least feasible to achieve in practice. Conversely, the proposed roles of educational robots demonstrated alignment between importance and feasibility. Experts highlighted three promising roles for robots in the GenAI era: supporting teachers in boosting learner engagement, serving as conversational interfaces for students to access knowledge and assisting teachers in supporting disadvantaged learners. These findings provide a roadmap for prioritising feasible innovations in educational robotics.

The proportion of lung cancers affecting individuals who have never smoked is growing, with these cancers being prone to harbor mutations in the EGFR gene. Little is known about risk factors and prognostic indicators for EGFR-mutant cancers, with current research limited by the scarcity of datasets integrating genomic, clinical, and environmental data. We created the Meyer Cancer Center Molecularly Enhanced Lung Cancer Database (MCC-MELD), including lung cancer cases from a large catchment area in New York City. We identified cases through linkage to our institution's cancer registry and a clinician-initiated, manually curated database. We linked all cases to the electronic health record and in-house tumor genomic testing results. We used natural language processing (NLP) to extract unstructured genomic testing results and detailed smoking history. We linked geocoded addresses to detailed area-level measures. MCC-MELD contains 9,573 patients with lung cancer diagnosed from 1988 to 2024, of whom 20% were non-Hispanic Asian, 14% were non-Hispanic Black, and 8% were Hispanic. We identified 1,092 (11.4%) EGFR-mutant cancers, with NLP identifying 397 cases not identified by structured data. NLP showed high accuracy in ascertaining EGFR status (97%) and quantitative smoking history variables (90%-98%). Never smokers made up 16% of the cases in MCC-MELD. MCC-MELD is an NLP-enhanced database containing clinical information, genomic testing results, and linkages to area-level data for patients with lung cancer from a diverse urban setting. This resource can facilitate studies on lung cancer risk factors, treatment patterns, and outcomes by EGFR and other driver mutation status.

Modelling phylogeny in 16S rRNA gene sequencing datasets using string-based kernels.

A fuzzy set is a mathematical construct that assigns a membership grade to each element within a universe of discourse, representing the degree to which the element belongs to the set. This approach extends classical binary logic by allowing continuous values between 0 and 1, making it a natural framework for handling uncertainties and vague concepts often expressed in natural language. Fuzzy sets are particularly powerful in modelling real-world scenarios where ambiguity and imprecision are inherent, such as in human decision-making, linguistic expressions, and complex systems. This paper introduces a novel application of fuzzy logic by proposing a fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method. DEMATEL is a well-established technique used to analyse cause-and-effect relationships within complex systems. Still, its traditional form relies on crisp values, which may not adequately capture the inherent uncertainties in real-world data. Our proposed method integrates triangular fuzzy numbers into the DEMATEL framework, enabling the representation and analysis of data with imprecision and vagueness. Specifically, we apply the fuzzy DEMATEL approach to study the cause-and-effect relationships among factors affecting transgender individuals, a population often marginalized and underrepresented in research. By leveraging triangular fuzzy numbers, our method provides a more nuanced and realistic representation of the uncertainties and complexities in the data. This approach not only enhances the accuracy of the analysis but also offers a meaningful way to interpret vague or subjective information, ultimately contributing to more informed decision-making and policy development for transgender communities.

A novel span and syntax enhanced large language model based framework for fine-grained sentiment analysis.

Assessment of a Zero-Shot Large Language Model in Measuring Documented Goals-of-Care Discussions.

Increasing timely colonoscopy surveillance for patients with high-risk colorectal polyps: Protocol for a cluster randomized trial.

A scalable natural language processing framework for drug repurposing in chemotherapy-induced adverse events from clinical narrative records.

Feelings behind words: A systematic review on how effective IS NLP-based assessment for mental health diagnosis in human studies.

Sustainable electronic trauma registry with NLP/ML-enhanced ICD-10 classification: 13-year insights from violence-heavy KwaZulu-Natal, South Africa.

Stabilized neural ordinary differential equation for text classification in natural language processing

WhyMedQA: Enhanced biomedical why question answering using transfer learning approach.

Sentiment analysis, a key component of natural language processing and data mining, plays a pivotal role in extracting subjective insights from textual data, particularly on social media platforms. In response to the growing importance of digital engagement, understanding public sentiment has become essential for formulating effective marketing strategies. This study aims to enhance the marketing strategy of energy products in subsidiaries of State-Owned Enterprises (SOEs) by employing a hybrid sentiment analysis model that integrates the Valence Aware Dictionary and Sentiment Reasoner (VADER) with Long Short-Term Memory (LSTM) neural networks. Utilizing a mixed-method approach that combines both quantitative and qualitative analyses, the study collects and processes data from multiple social media sources to identify and classify consumer sentiment. The results demonstrate that the hybrid VADER-LSTM model achieves an accuracy rate of up to 84%, enabling a more nuanced interpretation of consumer opinions. These insights inform the development of data-driven, responsive, and targeted marketing strategies. Furthermore, the study highlights the significance of fostering interactive communication between companies and consumers to enhance the impact of digital marketing efforts. Theoretical implications include a contribution to the academic discourse on information systems and digital marketing, while practical outcomes offer valuable guidance for SOEs in adopting adaptive, sentiment-informed marketing approaches within the energy sector.

Leveraging large language models for automating water distribution network optimization.

An integrated bibliometric and content analysis of financial natural language processing: advancements and challenges

Artificial intelligence for arterial blood gas interpretation.

Large language modeling of hallucinatory problem mitigation based on the wheel of emotions.

Conceptual similarity as aggregation over feature sets in geometric spaces.

Enhancing healthcare worker mental health via artificial intelligence-driven work process improvements: a scoping review.

Natural language processing and text mining in transportation: Current status, challenges, and future roadmap