AI Practice Research Articles

The integration and innovative practice of intelligent AI and local opera in college teaching

This paper explores the impacts of integrating AI into the teaching of Chinese Opera using a mixed-methods approach, examining performance, engagement, and psychological factors in students. A quasi-experimental design involving 199 participants over a one-year period was conducted, involving teaching with and without AI enhancement. Quantitative data, derived from standardized tests and analytics provided by AI platforms, were supported by qualitative data from interviews and observational studies. These results suggest that there were significant increases in the AI-enhanced cohort in opera performance competencies (Δ = 13.6, p < 0.001); retention of cultural knowledge (Δ = 15.5, p < 0.001), and overall engagement levels (r = 0.73, p < 0.001). Time series analysis revealed nonlinear learning trajectories, with participants showing greatest gains during the intervention’s midpoint. The psychological data showed a strengthening relationship between self-efficacy and in-performance outcomes, demonstrating an increase from r_initial = 0.38 to r_final = 0.67, p < 0.001. This study indicates both the potential of AI in the preservation and development of traditional artistic work and highlights some challenges in initial implementation. The findings facilitate the ongoing discussion of integrating technologies into arts education and provide valuable insights to support curriculum development, in addition to conserving cultural heritage in the modern-day digital world.

AI-Driven Sustainable Marketing in Gulf Cooperation Council Retail: Advancing SDGs Through Smart Channels

This paper explores how AI drives GCC sector retail towards the fulfillment of the UN SDGs. Analyzing a survey conducted on 410 retail executives, using PLS-SEM, this study underlines the role of AI in promoting operational efficiency, waste reduction, and consumer engagement with greener products. Key highlights include that AI-enabled marketing strategies improve the adoption of sustainable practices among consumers; AI-powered smart distribution channels enhance supply chain efficiency, reduce carbon emissions, and optimize logistics. For a retailer, practical applications of AI include the use of AI in demand forecasting to potentially reduce waste, personalized marketing to efficiently promote sustainable products, and deploying smart systems that reduce energy consumption. While these benefits are real, data privacy and algorithmic bias remain valid concerns, thus underlining the need for ethics and transparency in the practice of AI. The following study provides actionable insights for GCC retailers on how to align AI adoption with sustainability goals, fostering competitive advantages and environmental responsibility.

Research on school-based AI curriculum design and practice for cultivating computational thinking in high school students

Research on school-based AI curriculum design and practice for cultivating computational thinking in high school students

AI-Driven Scaling of Sustainable Startups: Business Models and Environmental Impact

With fast growth in sustainable startups at variance with profitability, the likelihood of high profitability contrasted with minimal environmental impact becomes a great challenge and opportunity. In this article, the role of AI in scaling through innovative business models to meet sustainability goals is investigated. The latest development in optimized operating efficiency, lesser resource consumption, and improved scalability has seen AI use go unexplored in business models designed particularly for environmental sustainability goals. This paper addresses how AI-enabled business model innovation can facilitate scaling up of sustainable start-ups in renewable energy, waste management, and circular economy practices. In addition, resource reallocation under such a scenario comes with a double dividend: it brings better economic growth alongside lower ecological damage, while reduction in AI's adverse environmental impact arises as a by-product. Case studies are expected to summarize the complexity in practice and limitations of AI for sustainable business growth. This study, therefore, provides an answer that contributes to understanding how startups may leverage AI to drive not only economic value but also create environmental value, thus providing the much-needed roadmap for future research in a rapidly changing field.

Evaluation of Knowledge, Attitude, and Practice (KAP) of Artificial Intelligence in Endodontics and Implantology Education: A Cross-sectional Study

Purpose: The purposes of this study were to assess the knowledge, attitude, and practice (KAP) of AI in Endodontics and implantology education among dental professionals' and dental students in Endodontics and implantology education at the Kingdom of Saudi Arabia. Materials and methods: The present study is a descriptive cross-sectional online survey that was carried out among dental students and dental professionals in the Kingdom of Saudi Arabia. A self-structured, close-ended questionnaire that was administered that consisted of 17 questions was included. The questionnaire validity and reliability were evaluated for vetting and remarks. The questions were circulated through Google Forms, and it was circulated among the study participants through online mode. The data were collected systematically, and SPSS Statistics version 26.0 was used for data analysis. Results: There were 805 responses, (443 dental students and 362 dental professionals') participated in the study through Google Forms. Among these, 435 (54%) were females and 370 (46%) were males. In the study group, 491 (61.0%) were aware of AI, and 314 (39.0 %) were not aware (p-value 0.000). Among the 17 questions used to assess the KAP, 12 questions were significant with a p-value less than 0.05. More than 73 % prefer to use artificial intelligence in endodontics and implantology education. About 120 (14.9%) agreed that AI will replace the role of dentists in the future. There were no significant results in comparing dental students and dental professionals. Conclusion: The current study contributes valuable insights into knowledge, attitudes, and perceptions related to artificial intelligence among dental student and professionals in the Kingdom of Saudi Arabia. Despite some reservations, the majority show a positive view towards the role of AI in the endodontic education, which indicates fertile ground for further exploration and integration of AI technologies into endodontic education. There is a need for continued future research to explore strategies to improve the potential of AI while test their reliability and relevance in endodontic and implantology education.

Now, Later, and Lasting: 10 Priorities for AI Research, Policy, and Practice

Shaping the future of artificial intelligence.

Using explainable AI to unravel classroom dialogue analysis: Effects of explanations on teachers' trust, technology acceptance and cognitive load

AbstractDeep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt and valuable feedback on their teaching practices. However, these deep learning models often have intricate structures with numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding their classroom dialogue analysis likely leads teachers to distrust and underutilize these AI‐powered models. To tackle this issue, we leveraged explainable AI to unravel classroom dialogue analysis and conducted an experiment to evaluate the effects of explanations. Fifty‐nine pre‐service teachers were recruited and randomly assigned to either a treatment (n = 30) or control (n = 29) group. Initially, both groups learned to analyse classroom dialogue using AI‐powered models without explanations. Subsequently, the treatment group received both AI analysis and explanations, while the control group continued to receive only AI predictions. The results demonstrated that teachers in the treatment group exhibited significantly higher levels of trust in and technology acceptance of AI‐powered models for classroom dialogue analysis compared to those in the control group. Notably, there were no significant differences in cognitive load between the two groups. Furthermore, teachers in the treatment group expressed high satisfaction with the explanations. During interviews, they also elucidated how the explanations changed their perceptions of model features and attitudes towards the models. This study is among the pioneering works to propose and validate the use of explainable AI to address interpretability challenges within deep learning‐based models in the context of classroom dialogue analysis.Practitioner notesWhat is already known about this topic Classroom dialogue is recognized as a crucial element in the teaching and learning process. Researchers have increasingly utilized AI techniques, particularly deep learning methods, to analyse classroom dialogue. Deep learning‐based models, characterized by their intricate structures, often function as black boxes, lacking the ability to provide transparent explanations regarding their analysis. This limitation can result in teachers harbouring distrust and underutilizing these models. What this paper adds This paper highlights the importance of incorporating explainable AI approaches to tackle the interpretability issues associated with deep learning‐based models utilized for classroom dialogue analysis. Through an experimental study, this paper demonstrates that providing model explanations enhances teachers' trust in and technology acceptance of AI‐powered classroom dialogue models, without increasing their cognitive load. Teachers express satisfaction with the model explanations provided by explainable AI. Implications for practice and/or policy The integration of explainable AI can effectively address the challenge of interpretability in complex AI‐powered models used for analysing classroom dialogue. Intelligent teaching systems designed for classroom dialogue can benefit from advanced AI models and explainable AI approaches, which offer users both automated analysis and clear explanations. By enabling users to understand the underlying rationale behind the analysis, the explanations can contribute to fostering trust and acceptance of the AI models among users.

From applied ethics and ethical principles to virtue and narrative in AI practices

AbstractThe question of how we can use ethics and ethical frameworks to avert the negative consequences of AI through guidance on human behaviour and the design of technological systems has recently been receiving increasing attention. The appropriate response to an ethics of AI has certainly been contentious. For some years the wisdom of deontology and utilitarianism in the ethics of technology has been questioned. Today, a kind of AI ethics principlism has gained a degree of widespread acceptance, yet it still invites harsh rejections in recent scholarship. In this paper, we wish to explore the contribution to an ethics of AI made by a narrative philosophy and ethics of technology inspired by the ‘little ethics’ of Paul Ricoeur, and virtue ethics of Alasdair MacIntyre, most recently and promisingly built upon by Wessel Reijers and Mark Coeckelbergh. The objective of this paper is to examine the extent to which a narrative and virtue based ethics (or, VPD, i.e., virtuous practice design) might be a plausible candidate for the foundation of an ethics of AI, or rather ethical AI practice. This will be achieved by exploring the ways in which this approach can respond to some of the significant faults with or critiques of applied and principles and guidelines based ethical approaches to AI ethics.

Generative AI: An AI paradigm shift in the making?

AbstractIt is sometimes difficult to evaluate progress in Generative AI, that is, image generation and large language models. This may be because they represent a paradigm shift in AI, and the traditional ways of developing, evaluating, understanding, and deploying AI systems no longer apply. Instead, we need to develop new such approaches, possibly by extending those currently in use in cognitive neuroscience and psychology. In this manner, a new AI paradigm can be created, providing a significant leap in AI research and practice.

The practice and reflection of generative AI in the cultivation of aesthetic education in colleges and universities: Centred on environmental design major

Through a comprehensive analysis of the development history of generative AI and its application in the aesthetic education of environmental design majors, this paper aims to reveal its potential significance and revelations in the field of aesthetic education. This paper first outlines the concept and development history of generative AI, and then delves into its practice in the aesthetic education of environmental design majors. For different application scenarios, including natural language processing, image recognition, audio processing and video synthesis, its specific applications and effects in aesthetic education are explored respectively. By analysing the practice of generative AI in the cultivation of aesthetic education for environmental design majors, it is found that it has potential value in several aspects, and generative AI can provide students with more personalised and diverse learning experiences and expand the boundaries of aesthetic education. At the same time, it can also assist teachers in teaching design and assessment, and improve teaching efficiency and quality. With the continuous development and application of AI technology, the combination of generative AI and aesthetic education in environmental design will show a more diverse and extensive trend

Knowledge, Perceptions, and Practice of Artificial Intelligence among Medical Students and Doctors at King Faisal University, Saudi Arabia

Background and Aims: Artificial Intelligence is no longer a new concept for the medical profession but is still finding its way into the realm of healthcare. Saudi Arabia is attempting to make most of it, especially in major healthcare settings. Methods: A cross-sectional survey was conducted at King Faisal University using a self-administered questionnaire assessing sociodemographic characteristics as well as knowledge, perceptions, and practice of AI. Universal sampling was implemented to recruit all medical students in the clinical stage of MBBS (Bachelors of Medicine and Surgery) and all available doctors working at KFU. Descriptive analysis and a chi-squared test was applied for determining correlation of sociodemographic variables with outcome variables, i.e., knowledge, perceptions, and practices relating to AI. Results: A total of 159 participants participated in our study. Those who were being taught about AI had significantly more basic knowledge about it (96.9%) than those not being taught about it (81.1%). Knowledge about deep learning was significantly higher among men (43.9%) than women (16.1%). The majority of the participants strongly agreed (84.4%) that AI is essential in medical practice, and most doctors and students have applied AI in the past in the medical field, are willing to practice it in the future, and/or are considering how to include it in the medical curriculum. Conclusions: There is a need for rigorous research, education, and focused training of present and future doctors to help them learn the significance of, and acquire the skills to implement, AI as one component of modern technology

From data to design: AI practice in design decisions

Abstract Facing the increasingly fierce market competition, enterprises need to make correct marketing decisions. Based on the decision support system, cloud server, and SOA framework, the article establishes an enterprise intelligent marketing decision-making system by combining the TCP/IP communication protocol and designing a data warehouse to store the marketing decision-making data. The Apriori algorithm in association rules is used to generate marketing rules to assist marketing decision-making, the marketing data is classified using the random forest algorithm, and the sparrow search algorithm optimizes the parameters of the random forest model. In practice, simulation experiments were designed to analyze the performance of the random forest model and the marketing decision-making system, and they were applied to the marketing decision-making example of the e-commerce platform. It was found that the AUC value obtained when the number of binary trees of the random forest model was 75 was 0.836, and the number of conflicts per second of the system was only 0.41 when the number of concurrent users was 5*104. The use of association rules resulted in eight marketing decisions, with the highest probability of user purchase prediction reaching 98.54%. Based on the intelligent marketing decision system, it can help decision-makers better analyze market changes and provide decision support for enterprises to develop scientific and reasonable marketing strategies.

Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review.

This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in the manual review of predicted hypnograms, a necessity due to the notable inter-scorer variability inherent in polysomnography (PSG) databases. Our efforts target the extent of review required to achieve predefined agreement levels, examining both in-domain (ID) and out-of-domain (OOD) data, and considering subjects' diagnoses. A total of 19,578 PSGs from 13 open-access databases were used to train U-Sleep, a state-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical database of an additional 8832 PSGs, covering a full spectrum of ages (0-91 years) and sleep-disorders, to refine the U-Sleep, and to evaluate different uncertainty-quantification approaches, including our novel confidence network. The ID data consisted of PSGs scored by over 50 physicians, and the two OOD sets comprised recordings each scored by a unique senior physician. U-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID and 73.8-78.8% on OOD data. The confidence network excelled at identifying uncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on OOD data. Independently of sleep-disorder status, statistical evaluations revealed significant differences in confidence scores between aligning vs discording predictions, and significant correlations of confidence scores with classification performance metrics. To achieve κ ≥ 90% with physician intervention, examining less than 29.0% of uncertain epochs was required, substantially reducing physicians' workload, and facilitating near-perfect agreement. Inter-scorer variability limits the accuracy of the scoring algorithms to ~80%. By integrating an uncertainty estimation with U-Sleep, we enhance the review of predicted hypnograms, to align with the scoring taste of a responsible physician. Validated across ID and OOD data and various sleep-disorders, our approach offers a strategy to boost automated scoring tools' usability in clinical settings.

Role of robotics and artificial intelligence in oral health education. Knowledge, perception and attitude of dentists in India

BACKGROUND: Artificial intelligence or AI may be identified as the use of Personal Computers and/or machines in performing certain specific types of tasks that usually have the requirement of humanized knowledge. These specific tasks include acknowledgment of the problem, understanding disease dynamics, and determining the clinical diagnosis. MATERIALS AND METHODS: This cross-sectional and prospective study was conducted on Dental professionals who were practicing all across India after obtaining approval from the Institutional Ethical Board. A previously validated as well as pre-analyzed questionnaire form was distributed using electronic mail and through the use of social media with a briefly explained description of the study purpose and an informed consent form. The study questionnaire comprised “close-ended” queries that were then divided into foursections. All the study participants were then instructed to select any one option among all the provided answers. The entire study was completed within one month. Collected observations were entered within a Microsoft Excel 2007® master chart. Statistical analytical software tool SPSS version 20.0, IBM Corporation was employed. “Chi-square” test was performed for evaluating statistical association. A P value lesser than 0.05 was fixed with statistical significance. RESULTS: On analyzing the level of knowledge, 82.5% of subjects had knowledge of artificial intelligence while 11.4% had no knowledge and 6.1% had some knowledge of this tool. 69.1% were knowledgeable regarding the use of AI in lesional diagnosis, 12.8% had no knowledge regarding artificial intelligence for the diagnosis and 18.1% had no knowledge regarding AI in the diagnosis. 71% had knowledge concerning the use of AI for Imaging. Knowledge of AI in Oral Hygiene was seen in 54.3%. 91.2% of participants had knowledge of robotics use in Oral Surgery. 77% of dentists had knowledge regarding the use of AI for the enhancement of clinical practice. 95.5% had a higher ‘positive’ attitude toward the use of AI in academics. 69.1% of dentists had a positive attitude regarding AI incorporation in practice. 5% of dentists considered artificial intelligence better than human intelligence for diagnosis. 10% believed that disparities can exist between AI-based and human diagnosis. CONCLUSION: Positive correlations were noted between knowledge, attitude, and practice of AI among studied dentists.

Artificial intelligence in farming: Challenges and opportunities for building trust

AbstractArtificial intelligence (AI) represents technologies with human‐like cognitive abilities to learn, perform, and make decisions. AI in precision agriculture (PA) enables farmers and farm managers to deploy highly targeted and precise farming practices based on site‐specific agroclimatic field measurements. The foundational and applied development of AI has matured considerably over the last 30 years. The time is now right to engage seriously with the ethics and responsible practice of AI for the well‐being of farmers and farm managers. In this paper, we identify and discuss both challenges and opportunities for improving farmers’ trust in those providing AI solutions for PA. We highlight that farmers’ trust can be moderated by how the benefits and risks of AI are perceived, shared, and distributed. We propose four recommendations for improving farmers’ trust. First, AI developers should improve model transparency and explainability. Second, clear responsibility and accountability should be assigned to AI decisions. Third, concerns about the fairness of AI need to be overcome to improve human‐machine partnerships in agriculture. Finally, regulation and voluntary compliance of data ownership, privacy, and security are needed, if AI systems are to become accepted and used by farmers.

Children’s right to participation in AI: Exploring transnational co-creative approaches to foster child-inclusive AI policy and practice

The right to participation in matters related to children is a fundamental right of every child. AI systems are emerging in all contexts of children’s lives, both in the US and EU, yet children’s voices are often ignored in AI policy and practice, particularly children from historically marginalised communities. This article explores the policy and practice gaps in Europe and the US and the lack of children’s participation in AI at the global level. Current AI policies and practices discount the transnational implications of AI on children’s lives and their rights in the context of the United Nations Convention on the Rights of the Child (UNCRC). The article calls for co-creative approaches which should be implemented transnationally to elevate the benefits of the inclusion of children in AI policy and practice – not only as users but also as contributors and innovators. This approach offers AI systems and policies that are more inclusive and children-friendly. By offering children more agency as contributors and innovators, children would gain more power compared to only being users. We propose that the balance in transnational power dynamics in AI policy and practice could become reversed. Ultimately, children’s increased agency as innovators in shaping AI practices can offer mutual benefits for children’s individual and social development, inclusive AI policy, and innovation practice.

Analyzing breast cancer invasive disease event classification through explainable artificial intelligence.

Recently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable. Thus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis. Age, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames. Thus, our framework aims at shortening the distance between AI and clinical practice.

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Data is central to the development and evaluation of machine learning (ML) models. However, the use of problematic or inappropriate datasets can result in harms when the resulting models are deployed. To encourage responsible AI practice through more deliberate reflection on datasets and transparency around the processes by which they are created, researchers and practitioners have begun to advocate for increased data documentation and have proposed several data documentation frameworks. However, there is little research on whether these data documentation frameworks meet the needs of ML practitioners, who both create and consume datasets. To address this gap, we set out to understand ML practitioners' data documentation perceptions, needs, challenges, and desiderata, with the ultimate goal of deriving design requirements that can inform future data documentation frameworks. We conducted a series of semi-structured interviews with 14 ML practitioners at a single large, international technology company. We had them answer a list of questions taken from datasheets for datasets~\citegebru2018datasheets. Our findings show that current approaches to data documentation are largely ad hoc and myopic in nature. Participants expressed needs for data documentation frameworks to be adaptable to their contexts, integrated into their existing tools and workflows, and automated wherever possible. Despite the fact that data documentation frameworks are often motivated from the perspective of responsible AI, participants did not make the connection between the questions that they were asked to answer and their responsible AI implications. In addition, participants often had difficulties prioritizing the needs of dataset consumers and providing information that someone unfamiliar with their datasets might need to know. Based on these findings, we derive seven design requirements for future data documentation frameworks such as more actionable guidance on how the characteristics of datasets might result in harms and how these harms might be mitigated, more explicit prompts for reflection, automated adaptation to different contexts, and integration into ML practitioners' existing tools and workflows.

The AI ethics maturity model: a holistic approach to advancing ethical data science in organizations

The field of AI ethics has advanced considerably over the past years, providing guidelines, principles, and technical solutions for enhancing the ethical development, deployment and usage of AI. However, there is still a clear need for research that facilitates the move from the ‘what’ of AI ethics to the ‘how’ of governance and operationalization. Although promising literature on the challenge of implementation is increasingly more common, so far no systemic analysis has been published that brings the various themes of operationalization together in a way that helps the gradual advancement of AI ethics procedures within organizations. In this opinion paper we therefore set out to provide a holistic maturity framework in the form of an AI ethics maturity model comprising six crucial dimensions for the operationalization of AI ethics within an organization. We contend that advancing AI ethics in practice is a multi-dimensional effort, as successful operationalization of ethics requires combined action on various dimensions. The model as presented is a preliminary result of literature analysis complemented with insights from several practical mutual learning sessions with some of the major public, private and research organizations of the Netherlands. The article contributes to the AI ethics literature and practice by synthesizing relevant aspects of operationalization and relating these to the praxis of AI in a maturity model that provides direction for organizations seeking to implement these ethical principles.

Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Syria: A cross-sectional online survey.

Artificial intelligence has been prevalent recently as its use in the medical field is noticed to be increased. However, middle east countries like Syria are deficient in multiple AI implementation methods in the field of medicine. So, holding these AI implementation methods in the medical field is necessary, which may be incredibly beneficial for making diagnosis more accessible and help in the treatment. This paper intends to determine AI's knowledge, attitude, and practice among doctors and medical students in Syria. A questionnaire conducted an online cross-sectional study on the google form website consisting of demographic data, knowledge, and perception of AI. There were 1,494 responses from both doctors and medical students. We included Syrian medical students and doctors who are currently residing in Syria. Of the 1,494 participants, 255 (16.9%) are doctors, while the other 1,252 (83.1%) are undergraduate medical students. About 1,055 (70%) participants have previous knowledge about AI. However, only 357 (23.7%) participants know about its application in the medical field. Most have shown positive attitudes toward its necessity in the medical field; 689 (45.7%) individuals strongly agree, and 628 (41.7%) agree. The undergraduate students had 3.327 times more adequate knowledge of AI than students in the first year. In contrast, the undergraduate 6th-year students had 2.868 times the attitude toward AI higher than students in the first year. The residents and assistant professors had 2.371 and 4.422 times the practice of AI higher than students, respectively. Although most physicians and medical students do not sufficiently understand AI and its significance in the medical field, they have favorable views regarding using AI in the medical field. Syrian medical authorities and international organizations should suggest including artificial intelligence in the medical field, particularly when training residents and fellowship physicians.