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2026 ehlert2026the DATABASE
The Application of Artificial Intelligence in Speech and Language Therapy: Attitudes and Expectations.

Ehlert, Hanna; Lüdtke, Ulrike

International journal of language & communication disorders , 61 : e70172

Artificial intelligence (AI) shows promise to support the prevention, diagnosis, and treatment of diseases in medicine and therapy. However, ethics is a priority concern in the development and implementation of AI across sectors. Common ethical themes in the healthcare literature of the last 5 years surround algorithmic bias, accountability, privacy, transparency and trust issues. The question arises how these challenges apply to speech and language therapy (SLT). Stakeholder attitudes towards the use of AI in healthcare have been investigated for the populations of physicians, medical students, and patients. However, no study has yet addressed the specific perspective of speech and language therapists on this technology. Therefore, the aim was to gather insights on the attitudes, hopes and concerns towards the (future) use of artificial intelligence from speech and language therapists working in clinical practice or research. An online survey with 11 closed and three open-ended questions was conducted in four German speaking countries. The quantitative analysis of the results involved correlating demographic factors, such as age, with the responses. The qualitative analysis compared the responses to this survey with the findings of healthcare literature and studies addressing other healthcare stakeholders. Five hundred eighty-seven professionals from Germany, Switzerland, Austria and Liechtenstein answered the questionnaire. In the results all but 3% of the participants expect that AI will be applied at least to some extend in SLT in the future. The majority of the participants (65%) are open-minded towards the application of AI in SLT. Perceived potential benefits show a larger overlap than identified challenges with the existing literature. The possible loss of the 'human-factor' in assessment and therapy is by far the most frequent concern (41%) the participating speech and language therapists have towards the use of AI. Results further reflect the current level of knowledge about this technology in our profession. The use of AI in SLT can have a positive impact, but many factors need to be considered to prepare our profession for this type of technology. These include the expansion of education, the development of guidelines and the establishment of interdisciplinary collaborations all aiming to develop, implement and enable the use of truly beneficial AI-tools for assessment and intervention in SLT. What is already known on this subject Stakeholder involvement is important in the development and implication of artificial intelligence in health care. Stakeholder attitudes towards the use of AI in healthcare have been investigated for the populations of physicians, medical students and patients. However, no study has yet addressed the specific perspective of speech and language therapists on this technology. What this paper adds to existing knowledge The majority of the participants are open-minded towards the application of AI in SLT and think that it will be used in our profession in the future. Perceived potential benefits and challenges align with the literature to some degree. One aspect that is especially emphasised by the participants is the potential loss of 'human-factor' in SLT. Results reflect participants' knowledge on AI as well as a specific therapeutic view on healthcare and intervention. What are the potential or actual clinical implications of this work? AI application in SLT has great potential, but also comes with challenges. Speech and language therapists need to expand their knowledge on this technology, prepare specific guidelines and engage in interdisciplinary collaborations to specify their perspective and needs in developing and implementing AI-software. Only then will it become truly useful for clinicians and they will be able to use it in a responsible and informed way.
2026 hu2026the DATABASE
The effects of perceived teacher support on online mathematics learning power: the mediating roles of artificial intelligence literacy and cognitive tools.

Hu, Yaping; Liu, Liying; Yang, Hua; Jia, Xuelong

Frontiers in psychology , 17 : 1763924

With the rapid advancement of generative artificial intelligence (GenAI), teaching and learning in higher education are undergoing profound transformations. As a foundational competency for students in the digital age, pathways to foster online mathematics learning power require exploration. Although teacher support is recognized as a critical factor, the mechanisms through which it might foster students' new digital competencies, thereby contributing to online mathematics learning power within the context of intelligent technologies remain underexplored. This study aims to construct a multiple mediation model to examine how perceived teacher support predicts online mathematics learning power through two pathways: AI literacy (the ability to leverage artificial intelligence for mathematical cognition) and the use of cognitive tools (e.g., MATLAB, GeoGebra). A questionnaire survey was conducted among 758 undergraduates enrolled in online mathematics courses at a comprehensive university in eastern China. The instruments included scales for perceived teacher support, AI literacy, cognitive tools, and online mathematics learning power. Data were analyzed using structural equation modeling and bootstrap sampling to examine direct and mediating effects. The results confirm that within the generative artificial intelligence context, perceived teacher support directly predicts students' online mathematics learning power while also indirectly predicts it by fostering AI literacy and cognitive tools proficiency. This reveals the mechanisms linking environmental support, digital competencies, and learning outcomes. This study suggests that educators adopt a teaching strategy integrating direct support, AI literacy cultivation, and cognitive tool guidance. This entails incorporating AI literacy and cognitive tools training into online mathematics course design within supportive learning environments. Doing so can effectively develop student competencies and prepare them for the intelligent era.
2025 edrian t. galabo2025impluwensya DATABASE
IMPLUWENSYA NG ARTIFICIAL INTELLIGENCE AT KALIGIRANG PAGKATUTO NG MGA MAG-AARAL SA AKADEMIKONG PERFORMANS NG UNANG TAON NG BSED FILIPINO NG KCAST

Edrian T. Galabo; Elealeh S. Timosa

EPRA International Journal of Multidisciplinary Research (IJMR) : 565-576

<jats:p>Ang pangunahing layunin ng pag-aaral na ito ay upang malaman ang ugnayan ng impluwensya ng artificial intelligence at kaligirang pampagkatuto sa akademikong perfromans ng mga mag-aaral. Kung kaya’t ang pag-aaral na ito ay may layuning siyasatin ang makabuluhang ugnayan sa pagitan ng impluwensya ng artificial intelligence at kaligirang pampagkatuto sa akademikong performans ng mga mag-aaral sa unang taon ng BSED Filipino ng KCAST. Nagtamo ang mga layunin ng pag-aaral na ito sa pamamagitan ng estadistikong gamit. Ang mga kalahok na pag-aaral ay binubuo ng 117 ng mag-aaral sa unang taon ng BSED Filipino. Ayon sa resulta nakakuha ng mataas na antas sa nakikitang panganib, nakikitang pagganap, nakikitang pagsisikap, kalagayan ng pasilidad, pag-uugali, gustong pag-uugali, at adaptsyon ng AI sa mataas na edukasyon sa artificial intelligence. Pagdating sa kaligirang pampagkatuto ang tiyak na positibong pakikitungo sa loob ng silid-aralan, pagkakaiba-iba ng paniniwala at pag-uugali, pansariling pagsalungat at pagtitiyaga sa katayuan ay nakakuha ng mataas. Sa pagdating sa akademikong performans na kakayahan, ugali, at kilos ay nakakuha ng mataas. Samakatuwid, ang resulta ay nangangahulugang ang null hypothesis ng pag-aaral ay tinanggap sapagkat malinaw na ang tatlong baryabol ng pag-aaral na ito ay walang makabuluhang ugnayan. Ibig sabihin nito na ang indikasyon na nagpapatunay na nararapat pagtuunan ng pansin ang pagpapaunland ng artificial intelligence upang umunlad ang kaligirang pampagkatuto at umunlad rin ang akademikong perfromans ng mga mag-aaral. MGA SUSING SALITA: artificial intelligence, kaligirang pampagkatuto, akademikong perfromans, at mag-aaral.</jats:p>
2025 satyadhar joshi2025comprehensive DATABASE
Comprehensive review of Artificial General Intelligence (AGI): Applications in Business and Finance

Satyadhar Joshi, Satyadhar Joshi

International Journal of Advances in Engineering and Management , 7 : (5):250-261

<jats:p>This paper delves into the multifaceted realm of Artificial General Intelligence (AGI), exploring its definition, evolution, potential applications, and the ongoing debates surrounding its development. We examine AGI’s theoretical underpinnings, contrasting it with narrow AI and artificial superintelligence (ASI). Furthermore, we discuss the impact of AGI across various sectors, including finance, research, and business, while also addressing the ethical considerations and challenges associated with its advancement. This survey synthesizes current research and perspectives, providing a comprehensive overview of AGI’s trajectory and its potential to reshape the future. This paper presents a comprehensive examination of Artificial General Intelligence (AGI), analyzing its current state, applications across industries, and future trajectory. Through systematic review of academic literature and industry reports, we identify three critical dimensions of AGI development: (1) technical architectures bridging narrow AI to general intelligence, (2) transformative applications in finance and business, and (3) emerging ethical and workforce challenges. Our findings reveal accelerating market growth (projected 36.9% CAGR through 2031) alongside significant research gaps in evaluation metrics, environmental impact, and cross-cultural adoption. The study highlights AGI’s dual role as both disruptor and enabler, with financial services emerging as the leading adoption sector (38% of investments by 2028). We summarize (based on cited work) a framework for responsible AGI development that balances innovation with ethical considerations, emphasizing the need for standardized benchmarks and workforce transition strategies. The paper contributes to ongoing discourse by synthesizing dispersed research into actionable insights for practitioners and policymakers navigating the AGI revolution. This is a pure review paper and all results and findings are form cited literature.</jats:p>
2023 liu2023games DATABASE
Games for Artificial Intelligence Research: A Review and Perspectives

Chengpeng Hu; Yunlong Zhao; Ziqi Wang; Haocheng Du; Jialin Liu

arXiv Preprint

Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision making in dynamic and uncertain environments, game theory, planning and scheduling, design and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based environments have been implemented for studying artificial intelligence. In addition to single- or multi-player, collaborative or adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing artificial intelligence ideas and techniques. This paper reviews the games and game-based platforms for artificial intelligence research, provides guidance on matching particular types of artificial intelligence with suitable games for testing and matching particular needs in games with suitable artificial intelligence techniques, discusses the research trend induced by the evolution of those games and platforms, and gives an outlook.
2021 filho2021watershed DATABASE
Watershed of Artificial Intelligence: Human Intelligence, Machine Intelligence, and Biological Intelligence

Li Weigang; Liriam Enamoto; Denise Leyi Li; Geraldo Pereira Rocha Filho

arXiv Preprint

This article reviews the "Once learning" mechanism that was proposed 23 years ago and the subsequent successes of "One-shot learning" in image classification and "You Only Look Once - YOLO" in objective detection. Analyzing the current development of Artificial Intelligence (AI), the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI. As a watershed for the branches of AI, some classification standards and methods are discussed: 1) Human-oriented, machine-oriented, and biological-oriented AI R&D; 2) Information input processed by Dimensionality-up or Dimensionality-reduction; 3) The use of one/few or large samples for knowledge learning.
2021 raedt2021from DATABASE
From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey

Giuseppe Marra; Sebastijan Dumančić; Robin Manhaeve; Luc De Raedt

arXiv Preprint

This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence. Neurosymbolic artificial intelligence (NeSy) studies the integration of symbolic reasoning and neural networks, while statistical relational artificial intelligence (StarAI) focuses on integrating logic with probabilistic graphical models. This survey identifies seven shared dimensions between these two subfields of AI. These dimensions can be used to characterize different NeSy and StarAI systems. They are concerned with (1) the approach to logical inference, whether model or proof-based; (2) the syntax of the used logical theories; (3) the logical semantics of the systems and their extensions to facilitate learning; (4) the scope of learning, encompassing either parameter or structure learning; (5) the presence of symbolic and subsymbolic representations; (6) the degree to which systems capture the original logic, probabilistic, and neural paradigms; and (7) the classes of learning tasks the systems are applied to. By positioning various NeSy and StarAI systems along these dimensions and pointing out similarities and differences between them, this survey contributes fundamental concepts for understanding the integration of learning and reasoning.
2021 bennett2021compression DATABASE
Compression, The Fermi Paradox and Artificial Super-Intelligence

Michael Timothy Bennett

arXiv Preprint

The following briefly discusses possible difficulties in communication with and control of an AGI (artificial general intelligence), building upon an explanation of The Fermi Paradox and preceding work on symbol emergence and artificial general intelligence. The latter suggests that to infer what someone means, an agent constructs a rationale for the observed behaviour of others. Communication then requires two agents labour under similar compulsions and have similar experiences (construct similar solutions to similar tasks). Any non-human intelligence may construct solutions such that any rationale for their behaviour (and thus the meaning of their signals) is outside the scope of what a human is inclined to notice or comprehend. Further, the more compressed a signal, the closer it will appear to random noise. Another intelligence may possess the ability to compress information to the extent that, to us, their signals would appear indistinguishable from noise (an explanation for The Fermi Paradox). To facilitate predictive accuracy an AGI would tend to more compressed representations of the world, making any rationale for their behaviour more difficult to comprehend for the same reason. Communication with and control of an AGI may subsequently necessitate not only human-like compulsions and experiences, but imposed cognitive impairment.
2017 hurwitz2017artificial DATABASE
Artificial Intelligence and Economic Theories

Tshilidzi Marwala; Evan Hurwitz

arXiv Preprint

The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that are considered are: demand and supply, asymmetrical information, pricing, rational choice, rational expectation, game theory, efficient market hypotheses, mechanism design, prospect, bounded rationality, portfolio theory, rational counterfactual and causality. The benefit of this book is that it evaluates existing theories of economics and update them based on the developments in artificial intelligence field.
2016 kaplan2016defining DATABASE
Defining Artificial Intelligence

Kaplan, Jerry

Artificial Intelligence

<p>What is artificial intelligence?</p> <p>That’s an easy question to ask and a hard one to answer—for two reasons. First, there’s little agreement about what intelligence is. Second, there’s scant reason to believe that machine intelligence bears much relationship to human intelligence, at least so...</p>