Search Results (34)

This research highlights the importance of Emotion Aware Technologies (EAT) and their implementation in serious games to assist children with Autism Spectrum Disorder (ASD) in developing social-emotional skills. As AI is gaining popularity, such tools can be used in mobile applications as invaluable teaching tools. In this paper, a new AI framework application is discussed that will help children with ASD develop efficient social-emotional skills. It uses the Jetpack Compose framework and Google Cloud Vision API as emotion-aware technology. The framework is developed with two main features designed to help children reflect on their emotions, internalise them, and train them how to express these emotions. Each activity is based on similar features from literature with enhanced functionalities. A diary feature allows children to take pictures of themselves, and the application categorises their facial expressions, saving the picture in the appropriate space. The three-level minigame consists of a series of prompts depicting a specific emotion that children have to match. The results of the framework offer a good starting point for similar applications to be developed further, especially by training custom models to be used with ML Kit. Full article

This position paper proposes a conceptual framework (CF-BIAI-SXT) for integrating blockchain with AI to enhance ethical governance, transparency, and privacy in high-risk AI applications that ensure societal resilience through the mitigation of sexual exploitation. Sextortion is a growing form of digital sexual exploitation, and the role of AI in its mitigation and the ethical issues that arise provide a good case for this paper. Through a combination of systematic and narrative literature reviews, the paper first explores the ethical shortcomings of existing AI systems in sextortion prevention and assesses the capacity of blockchain operations to mitigate these limitations. It then develops CF-BIAI-SXT, a framework operationalized through BPMN-modeled components and structured into a three-layer implementation strategy composed of technical enablement, governance alignment, and continuous oversight. The framework is then situated within real-world regulatory constraints, including GDPR and the EU AI Act. This position paper concludes that a resilient society needs ethical, privacy-first, and socially resilient digital infrastructures, and integrating two core technologies, such as AI and blockchain, creates a viable pathway towards this desideratum. Mitigating high-risk environments, such as sextortion, may be a fundamental first step in this pathway, with the potential expansion to other forms of online threats. Full article

This study explores the role of public theology in addressing contemporary societal challenges, emphasizing ethical dialogue in response to secularization, pluralism, technological transformation, and social and environmental issues. It situates pastoral theology in the Christian tradition as an active social practice aimed at promoting justice, equality, and the common good. The study highlights the emergence of public theology as a response to the participation of religious discourse in the public arena, considering communication and digital technology, and articulating theological reflection with real-world social issues. Additionally, it examines the profound significance of dialogue within religious discourse and stresses the importance of ethical reflection in technological advancements, particularly concerning AI (Artificial Intelligence). Moreover, Catholic social thought and the concept of integral ecology are analyzed in dialogue with the SDGs (Sustainable Development Goals), underlining the potential of public theology to promote socio-environmental justice through a holistic approach. Full article

Artificial intelligence is increasingly deployed as a vehicle for “social good” in agriculture, ostensibly advancing the UN Sustainable Development Goals whilst uplifting smallholders. This study examines how such claims materialise through a selective case study analysis of eleven Thai Agricultural AI providers, analysing governance practices and impact framing. The research develops the “hybrid intersection” concept as an analytical lens for understanding how Agricultural AI simultaneously delivers genuine social benefits whilst reproducing structural constraints that limit transformative change. Findings reveal that “social good” becomes operationalised primarily through economic gains, reflecting farmers’ immediate financial predicament and market-driven innovation constraints. Governance practices prioritise functional trust over procedural safeguards, reflecting institutional pressures to demonstrate immediate value. The study reveals two systemic tensions: data commodification models enabling free farmer access whilst extracting behavioural surplus for third-party monetisation, and market optimisation approaches delivering incremental improvements whilst leaving structural challenges unaddressed. Thailand’s Agricultural AI landscape thus embodies a “hybrid intersection” where genuine social good coexists with constrained transformation, providing analytical tools for understanding similar patterns in other Southern contexts. Full article

Generative artificial intelligence (GenAI) technologies based on big language models are becoming a transformative power that reshapes the future shape of education. Although the impact of GenAI on education is a key issue, there is little exploration of the challenges and response strategies of GenAI on the sustainability of education from a public perspective. This data mining study selected ChatGPT as a representative tool for GenAI. Five topics and 14 modular semantic communities of public attitudes towards using ChatGPT in education were identified through Latent Dirichlet Allocation (LDA) topic modeling and the semantic network community discovery process on 40,179 user comments collected from social media platforms. The results indicate public ambivalence about whether GenAI technology is empowering or disruptive to education. On the one hand, the public recognizes the potential of GenAI in education, including intelligent tutoring, role-playing, personalized services, content creation, and language learning, where effective communication and interaction can stimulate users’ creativity. On the other hand, the public is worried about the impact of users’ technological dependence on the development of innovative capabilities, the erosion of traditional knowledge production by AI-generated content (AIGC), the undermining of educational equity by potential cheating, and the substitution of students by the passing or good performance of GenAI on skills tests. In addition, some irresponsible and unethical usage behaviors were identified, including the direct use of AIGC and using GenAI tool to pass similarity checks. This study provides a practical basis for educational institutions to re-examine the teaching and learning approaches, assessment strategies, and talent development goals and to formulate policies on the use of AI to promote the vision of AI for sustainable development in education. Full article

This study investigates the evolving role of artificial intelligence (AI) in consumer decision-making, particularly in comparison to traditional human influencers. As consumer trust in social media influencers has declined, largely due to concerns about the financial motivations behind endorsements, AI tools such as ChatGPT have emerged as perceived neutral intermediaries. The research aims to understand whether AI systems can replace human influencers in shaping purchasing decisions and, if so, in which sectors. A mixed-methods approach was employed, involving a quantitative survey of 478 participants with prior experience using both AI tools and interacting with social media influencers, complemented by 15 semi-structured interviews. The results reveal that AI is favoured over human influencers in product categories where objectivity and precision are critical, such as electronics and sporting goods, while human influencers remain influential in emotionally driven sectors like fashion and beauty. These findings suggest that the future of marketing will show a reduced need for human social media influencers and may involve a hybrid model where AI systems dominate data-driven recommendations and human influencers continue to foster emotional engagement. This shift has important implications for brands as they adapt to changing consumer trust dynamics. Full article

This study provides a comprehensive analysis of the opportunities for democratizing artificial intelligence (AI) for social good using a bibliometric–systematic literature review method. It combines the quantitative analysis of bibliometric methods with the qualitative synthesis of systematic reviews. This approach helps identify patterns, trends, and gaps in the literature, advancing theoretical insights and mapping future research directions. Design/methodology/approach: Scopus, PubMed, and Web of Science, as prominent scientific databases, were utilized to examine publications between 2014 and 2024. The article selection followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The bibliometric analysis was conducted using CiteSpace software. Findings: The bibliometric analysis identified the most influential articles, journals, countries, authors, and key themes. The systematic thematic analysis identified established modes of using AI for social good. Moreover, future research directions are suggested and discussed in this article. Practical implications: The findings give future research directions and guidance to academics, practitioners, and policymakers for real-world applications. Full article

Background: The impact of medical intervention on a patient’s quality of life (QoL) is more and more important. Treatment success is defined not only in terms of the success of the procedure performed but also with regard to its impact on different areas of the patient’s life. The aim of the study was to assess the QoL of patients after cataract surgery and identify factors that affect it. Methods: Between January and March 2018, a survey was carried out among 100 patients who had undergone cataract surgery with intraocular lens implantation at the ‘Spektrum’ Clinical Ophthalmology Centre in Wrocław. The World Health Organization Quality of Life—BREF (WHOQOL-BREF) questionnaire and Illness Acceptance Scale (AIS) were used. Results: Most respondents (67%) rated their overall health as very good. The median score on the AIS was 34 (31.5–39), indicating a high level of illness acceptance. There was no statistically significant relationship (p > 0.05) between sex and QoL nor between the level of illness acceptance and QoL. We found no statistically significant relationships between place of residence and QoL (p > 0.05) nor between place of residence and AIS. Conclusions: The respondents reported the highest QoL scores for the environment domain and the lowest QoL scores for the social relationships domain. QoL had a positive impact on illness acceptance among the study patients. Younger patients (aged 50 or under) reported significantly higher scores for all the domains of QoL. Being employed was found to be associated with better QoL and greater illness acceptance. Full article

Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe. Full article

Artificial intelligence (AI) is transforming much of society in a short time. Regardless of whether we know it, we interact with AI systems when we seek information online, shop, work, and engage with social media. AI has massive potential to promote human wellbeing but also poses considerable risks, as set out in an open letter signed by leaders in the field, such as Geoffrey Hinton, the “Godfather of AI”. This paper examines how AI can be used as a powerful tool to change pro-social behaviors as part of social marketing programs. We examine opportunities to build on existing efforts to use AI for pro-social behavior changes and the challenges and potential risks that AI may pose. The specific aims of the paper are to explore how AI can be used in social marketing policy, strategy development, and operational delivery. We also explore what this means for future social marketing practice. We present an overview of case studies from the social marketing field and the application of AI in the past, present, and future. We examine the following key question: can these new technologies can be used to promote social good, and if so, how? Through examples from policy, strategy development, operations, and research in social marketing, we examine how AI has been used and successfully applied to improve consumer outcomes and analyze its implications for social marketing. We conclude that AI has substantial promise but also poses some challenges and has potential negative impacts on efforts to promote pro-social behavior changes. Used well, AI may enable social marketers to more rapidly assess how to modify programs of action to ensure maximum efficiency and effectiveness. We suggest future research and programs within this field. Full article

With rapid economic development and urban expansion, China faces a serious imbalance between production, living, and ecological land use, in which the erosion of water ecological space by urban expansion is especially notable. In order to alleviate or solve this imbalance, this study constructs the water ecological space in the Mulan River Basin based on national land spatial planning using remote sensing statistics and the 2000–2020 statistical yearbooks for the Mulan River Basin. A landscape index is applied to explore this landscape in terms of its production–living–ecological space (PLES) patterns and evolutionary characteristics. Factors affecting the drivers of PLES changes are analyzed through Geo-Detector, and predictions are made using the cellular automata Markov (CA-Markov) model. It was found that (1) PLES distribution patterns in the Mulan River Basin from 2000 to 2020 are dominated by non-watershed ecological spaces, with a significant expansion of living space. Its ecological space is shrinking, and there is significant spatial variation between its near-river and fringe areas. (2) Of the PLES conversions, the most dramatic conversions are those of production space and living space, with 81.14 km² of production space being transferred into living space. Non-water ecological space and water ecological space are also mainly transferred into production space. (3) As shown by the results of the landscape index calculation, non-water ecological space in the Mulan River Basin is the dominant landscape, the values of the Shannon diversity index (SHDI) and Shannon homogeneity index (SHEI) are small, the overall level of landscape diversity is low, the aggregation index (AI) is high, and the degree of aggregation is obvious. (4) The progressive PLES changes in the Mulan River Basin are influenced by a combination of natural geographic and socioeconomic factors, with the mean population density and mean elevation being the most important factors affecting PLES changes among social and natural factors, respectively. (5) The Kappa coefficient of the CA-Markov model simulation is 0.8187, showing a good simulation accuracy, and it is predicted that the area of water ecological space in the Mulan River Basin will increase by 3.66 km² by 2030, the area of production space and non-water ecological space will further decrease, and the area of construction land will increase by 260.67 km². Overall, the aquatic ecological space in the Mulan River Basin has made progress in terms of landscape ecological protection, though it still faces serious erosion. Therefore, attaching importance to the restoration of the water ecological space in the Mulan River Basin, integrating multiple elements of mountains, water, forests, fields, and lakes, optimizing the spatial structure of its PLES dynamics, and formulating a reasonable spatial planning policy are effective means of guaranteeing its ecological and economic sustainable development. This study offers recommendations for and scientific defenses of the logical design of PLES spatial functions in the Mulan River Basin. Full article

Water is one of the most basic and essential resources for life and is also a strategic component for the development of the economies of the different countries of the planet. The water sector in the context of ecological transition and the circular economy has enormous economic potential. However, the water resources present in a territory are, in many cases, very limited, and their availability is increasingly restricted. In this respect, current technologies make it possible to generate a whole range of renewable resources. In the case of wastewater treatment plants, in addition to obtaining clean water in sufficient quantity and quality, it is possible to take advantage of multiple other resources generated in the purification processes, such as fertilizers, biogas, bioplastics, and glass, and even recover adsorbents such as enzymes and proteins from wastewater. These resources represent a valuable social, environmental, and economic contribution. The scarcity of some of these resources causes continuous increases in market prices, generating economic tensions between producers and potential users. This work proposes to guide the potential of artificial intelligence (AI)-based methodologies in aspects related to the supply and demand of the resources generated in these infrastructures. Specifically, the use of machine learning (ML) allows for projecting economic scenarios based on multiple variables, such as the quality and quantity of the treated flows, the resources generated in the infrastructures, the current demands, and the prices of substitute goods. This aspect represents a substantial advance in terms of the circular economy since, beyond the technical aspects related to the processes, it ensures a sustainable balance between potential producers and end users. In conclusion, it brings sustainability to the urban water-cycle sector, ensuring the viability of the resources generated. Full article

Artificial intelligence has been attracting the attention of educational researchers recently, especially ChatGPT as a generative artificial intelligence tool. The context of generative artificial intelligence could impact different aspects of students’ learning, such as the motivational aspect. The present research intended to investigate the characteristics of students’ task motivation in the artificial intelligence context, specifically in the ChatGPT context. The researchers interviewed 15 students about their experiences with ChatGPT to collect data. The researchers used inductive and deductive content analysis to investigate students’ motivation when learning with ChatGPT. To arrive at the categories and sub-categories of students’ motivation, the researchers used the MAXQDA 2022. Five main categories emerged: task enjoyment, reported effort, result assessment, perceived relevance, and interaction. Each category comprised at least two sub-categories, and each sub-category was further organized into codes. The results indicated more positive characteristics of motivation than negative ones. The previous results could be due to the conversational or social aspect of the chatbot, enabling relationships with humans and enabling the maintenance of good quality conversations with them. We conclude that a generative AI could be utilized in educational settings to promote students’ motivation to learn and thus raise their learning achievement. Full article

In recent years, ESG (Environmental, Social, and Governance) has become a critical indicator for evaluating sustainable companies. However, the actual logic used for ESG score calculation remains exclusive to rating agencies. Therefore, with the advancement of AI, using machine learning to establish a reliable ESG score prediction model is a topic worth exploring. This study aims to build ESG score prediction models for the non-financial industry in Taiwan using random forest (RF), Extreme Learning Machines (ELM), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) and investigates whether the COVID-19 pandemic has affected the accuracy of these models. The dependent variable is the Taiwan ESG Sustainable Development Index, while the independent variables are 27 financial metrics and corporate governance indicators with three parts: pre-pandemic, pandemic, and the entire period (2018–2021). RMSE, MAE, MAPE, and r² are conducted to evaluate these models. The results demonstrate the four supervised models perform well during all three periods. ELM, XGBoost, and SVM exhibit excellent performance, while RF demonstrates good accuracy but relatively lower than the others. XGBoost’s r² shows inconsistency with RMSE, MAPE, and MAE. This study concludes the predictive performance of RF and XGBoost is inferior to that of other models. Full article

Background: In a Beveridgean decentralized healthcare system, like the Italian one, where regions are responsible for their own health planning and financing, the analysis of patients’ mobility appears very interesting as it has economic and social implications. The study aims to analyze both patients’ mobility for hospital rehabilitation and if the beds endowment is a driver for these flows; Methods: From 2011 to 2019, admissions data were collected from the Hospital Discharge Cards database of the Italian Ministry of Health, population data from the Italian National Institute of Statistics and data on beds endowment from the Italian Ministry of Health website. To evaluate patients’ mobility, we used Gandy’s Nomogram, while to assess if beds endowments are mobility drivers, we created two matrices, one with attraction indexes (AI) and one with escape indexes (EI). The beds endowment, for each Italian region, was correlated with AI and EI. Spearman’s test was carried out through STATA software; Results: Gandy’s Nomogram showed that only some northern regions had good hospital planning for rehabilitation. A statistically significant correlation between beds endowment and AI was found for four regions and with EI for eight regions; Conclusions: Only some northern regions appear able to satisfy the care needs of their residents, with a positive attractions minus escapes epidemiological balance. The beds endowment seems to be a driver of patients’ mobility, mainly for escapes. Certainly, the search for mobility drivers needs further investigation given the situation in Molise and Basilicata. Full article