Navigating Relationships with GenAI Chatbots: User Attitudes, Acceptability, and Potential

Despite the growing adoption of GenAI chatbots in health and well-being contexts, little is known about public attitudes toward their use for relationship support or the factors shaping acceptance and effectiveness. This study aims to address the research gap across three studies. Study 1 involved five focus groups with 30 young people to gauge general attitudes toward GenAI chatbots in relationship contexts. Study 2 evaluated user experiences during a single relationship intervention session with 20 participants. Study 3 quantitatively measured changes in attitudes toward GenAI chatbots and online interventions among 260 participants, assessed before, immediately after, and two weeks following their interaction with a GenAI chatbot or a writing task. Three main themes emerged in Studies 1 and 2: Accessible First-Line Treatment, Artificial Advice for Human Connection, and Internet Archive. Additionally, Study 1 revealed themes of Privacy vs. Openness and Are We in a Black Mirror Episode?, while Study 2 uncovered themes of Exceeding Expectations and Supporting Neurodivergence. The Study 3 results indicated that GenAI chatbot interactions led to reduced effort expectancy and short-term effects in increased acceptance and decreased objections to GenAI chatbots, though these effects were not sustained at a two-week follow-up. Both intervention types improved general attitudes toward online interventions, suggesting that exposure can enhance the uptake of digital health tools. This research underscores the evolving role of GenAI chatbots in augmenting therapeutic practices, highlighting their potential for personalized, accessible, and effective relationship interventions in the digital age

Impact of a Recipe Kit Scheme (BRITE Box) on Cooking and Food‐Related Behaviours of Children and Families: Exploring Parental/Carer Views

Background: Dietary intakes in UK children fail to meet national recommendations, especially in low‐income groups. Involving children in food preparation and cooking may enhance acceptability of a wider range of foods, enhance their skills and increase their enjoyment of food. An innovative recipe meal kit scheme, Building Resilience in Today's Environment (BRITE) Box, was developed during the pandemic primarily to address food insecurity (FI). Administered via schools, it offers pre‐weighed ingredients sufficient for a meal for a family of five, plus a child‐focused recipe, weekly during school termtimes. Methods: Qualitative and quantitative exploration of BRITE Box using questionnaires and semi‐structured interviews among parents/carers of children receiving the boxes was conducted at two timepoints a year apart. Results: A total of 154 parents/carers completed questionnaires and 29 were interviewed. Responses indicated multiple benefits of the scheme, including increased confidence in cooking among both children and parents/carers. Both questionnaire responses and interviews suggested improvements in a range of food‐related behaviours, including cooking and eating together and talking more about food. Parents/carers suggested that their children were more willing to eat vegetables and healthy foods and to try new foods and flavours. They also reported greater use of leftovers thereby potentially reducing food waste. Improved behaviours, willingness to try new foods and flavours, reduced food waste and lower stress of trying to think of new and acceptable family meals are likely to have contributed to the positive impact on their mental health reported by BRITE Box parents/carers. Conclusions: Meal kits for children may improve dietary diversity, enhance enjoyment and skills and impact positively on a range of family food‐related behaviours. We argue that BRITE Box has the potential for widespread positive impacts on cooking and food‐related behaviours in children and families, meriting wider study and dissemination as a positive approach to healthy eating in children

Causal AI for Business Decision Making:A Multi-Domain Investigation, Practical Applications and Implementation Challenges

This working paper presents an investigation into the emerging field of Causal Artificial Intelligence (Causal AI) and its transformative potential for business decision-making processes. While traditional machine learning methodologies excel at identifying correlational patterns within complex datasets, they fundamentally lack the capacity to address critical "why" questions essential for strategic business decisions. Our research employs a multi-domain case study methodology across financial services, retail, healthcare, and manufacturing sectors to examine how contemporary organisations are implementing causal inference frameworks to enhance decision-making robustness. The findings reveal that successful causal AI implementation necessitates not merely technical sophistication but also substantial organisational readiness factors, including causal literacy among executive decision-makers, integrated decision processes, and appropriate governance frameworks. Furthermore, we identify significant implementation challenges regarding data quality requirements, model validation approaches, and ethical considerations specific to causal reasoning systems. The paper concludes with a proposed developmental trajectory model for organisational adoption of causal AI and practical recommendations for businesses seeking to transcend correlation- based analytics paradigms

Automated Detection and Severity Prediction of Wheat Rust Using Cost‐Effective Xception Architecture

Wheat crop production is under constant threat from leaf and stripe rust, an airborne fungal disease caused by the pathogen Puccinia triticina. Early detection and efficient crop phenotyping are crucial for managing and controlling the spread of this disease in susceptible wheat varieties. Current detection methods are predominantly manual and labour‐intensive. Traditional strategies such as cultivating resistant varieties, applying fungicides and practicing good agricultural techniques often fall short in effectively identifying and responding to wheat rust outbreaks. To address these challenges, we propose an innovative computer vision‐based disease severity prediction pipeline. Our approach utilizes a deep learning‐based classifier to differentiate between healthy and rust‐infected wheat leaves. Upon identifying an infected leaf, we apply Grabcut‐based segmentation to isolate the foreground mask. This mask is then processed in the CIELAB color space to distinguish leaf rust stripes and spores. The disease severity ratio is calculated to measure the extent of infection on each test leaf. This paper introduces a ground‐breaking disease severity prediction method, offering a low‐cost, accessible and automated solution for wheat rust disease screening in field conditions using digital colour images. Our approach represents a significant advancement in crop disease management, promising timely interventions and better control measures for wheat rust

Real-Time, Adaptive AI Driven Business Simulation::Design Science Research on a Dynamic Learning Platform

This working paper presents a design science research (DSR) investigation into the development and evaluation of an innovative real-time, adaptive AI-driven business simulation platform. Traditional business simulations typically operate with static scenarios and predefined parameters that fail to capture the dynamic complexity of contemporary business environments. Using a rigorous DSR methodology spanning four design cycles over twenty-four months, we developed and refined a prototype system that integrates machine learning algorithms, natural language processing, and knowledge graph technologies to create dynamically evolving simulation scenarios. The platform was evaluated across diverse contexts including MBA education programmes, corporate strategy training, and entrepreneurial incubators, involving 287 participants across multiple evaluation phases. Our findings demonstrate the system's efficacy in enhancing strategic decision-making capabilities, improving knowledge transfer, and fostering adaptive reasoning skills among users. The paper lays the groundwork for next-generation business education and strategy testing environments that more authentically reflect the complex, evolving nature of real-world business ecosystems

Reading picture books with infants and toddlers TorrJane. Reading Picture Books with Infants and Toddlers. London, New York: Routledge, 2023, p. 138, ISBN 9780367768911

©2025, [SAGE Publications]. This is an author produced version of a paper published in Journal of Early Childhood Literacy uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it

Academic Behavioural Confidence: The Role of Demographic, Institutional, Psychosocial, and Behavioural Factors Across Diverse University Students in England

Background: research shows that university students’ academic engagement and performance can be usefully predicted by academic behavioural confidence (ABC), a set of self-beliefs in study-focused behaviours. While demographic and institutional variations in ABC are often reported, less is known about its psychosocial or behavioural correlates. Methods: A total of 328 students in 16 English universities completed an online survey with measures of ABC, self-esteem, ethnic identity, peer pressure, social support, and substance dependence and theirs and their tutor’s demographics. Results: Aspects of ABC differed by student gender (ps < 0.01), university (modern/traditional; ps < 0.01), and degree (nonvocational/vocational; p < 0.01) types and correlated with self-esteem, social support, peer pressure, drug dependence, and, for ethnic minority students, ethnic identity. Hierarchical regression analyses identified gender (β = 0.14–0.25), age (β = −0.16–0.12), self-esteem (β = 0.22–0.46), peer pressure (β = −0.15–−0.17), and drug dependence (β = −0.15–−0.21) as consistent predictors across ABC components. Conclusions: The findings highlight the importance of individual factors and social networks for academic self-efficacy. Recommendations for monitoring ABC and its contributors for targeted study and pastoral support are made

Impact of a mineral enriched, fiber complex on glycaemic response and satiation in healthy adults: a double-blind, crossover intervention study

Purpose: To investigate the effects of a chromium-enriched glucomannan-fructooligosaccharide complex (SB) on glycaemic and insulin responses, satiation, and hunger biomarkers in healthy adults. Methods: Using a double-blind, placebo-controlled, randomised crossover design, we assessed the acute impact of a single 3 g SB dose in 16 healthy adults (BMI 18.5–24.9 kg/m2) during a modified oral glucose tolerance test. On separate days, participants consumed 50 g dextrose or 50 g dextrose with 3 g SB (SBD). Blood glucose and insulin were analysed over 2.5 h. Hunger, fullness, and desire to eat were assessed via visual analogue scales. Additionally, the impact of SB on gastric viscosity was assessed in vitro. Results: SBD intake significantly reduced the insulin concentration compared to dextrose alone at 45, 75, and 90 min post-intake. Additionally, SBD resulted in significantly greater fullness and a lower desire to eat at 75 min when compared to dextrose (p < 0.05). Although hunger increased over time for both interventions, SBD led to lower hunger, desire to eat, and food desire scores compared to dextrose at 150 min (p < 0.05). The viscosity of SB, even when combined with dextrose, was significantly higher compared to dextrose alone. Conclusions: These novel findings suggest that SB can modulate insulin response and influence appetite regulation, highlighting its potential use in weight management strategies

Knowledge-Grounded Attention-Based Neural Machine Translation Model

Neural machine translation (NMT) model processes sentences in isolation and ignores additional contextual or side information beyond sentences. The input text alone often provides limited knowledge to generate contextually correct and meaningful translation. Relying solely on the input text could yield translations that lack accuracy. Side information related to either source or target side is helpful in the context of NMT. In this study, we empirically show that training an NMT model with target-side additional information used as knowledge can significantly improve the translation quality. The acquired knowledge is leveraged in the encoder-/decoder-based model utilizing multiencoder framework. The additional encoder converts knowledge into dense semantic representation called attention. These attentions from the input sentence and additional knowledge are then combined into a unified attention. The decoder generates the translation by conditioning on both the input text and acquired knowledge. Evaluation of translation from Urdu to English with a low-resource setting yields promising results in terms of both perplexity reduction and improved BLEU scores. The proposed models in the respective group outperform in LSTM and GRU with attention mechanism by +3.1 and +2.9 BLEU score, respectively. Extensive analysis confirms our claim that the translations influenced by additional information may occasionally contain rare low-frequency words and faithful translation. Experimental results on a different language pair DE-EN demonstrate that our suggested method is more efficient and general