Predicting a Future Where the Future Is Routinely Predicted

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

Evaluating generative AI responses to real-world drug-related questions

In this final set of explorations/meditations (of three), we examine the requirements for developing artificial general intelligence (AGI) through the lens of human cognitive architecture, with particular emphasis on the role of narrative selfhood and social cognition. Drawing on perspectives from cognitive science, philosophy of mind, and artificial intelligence research, we critically evaluate current claims about the capabilities of large language models, particularly regarding their purported achievements of theory of mind and self-awareness. We argue that genuinely human-like artificial intelligence may require more than sophisticated pattern recognition and language modeling, potentially necessitating the development of coherent narrative self-models and rich causal understanding. Special attention is given to the relationship between consciousness, conscience, and trustworthy AI systems, suggesting that meaningful artificial intelligence may require forms of richly-embodied and socially-embedded development to achieve robust and reliable functionality. We conclude by proposing that the path to artificial general intelligence may require recapitulating aspects of human cognitive development, particularly regarding the construction of narrative identity and social-moral reasoning capabilities. This analysis has implications for both the technical development of AI systems and the ethical frameworks through which we evaluate artificial minds.

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

Continual learning in medical devices: FDA's action plan and beyond

Aim/Purpose In a post-pandemic learning environment, artificial intelligence (AI) may become the new standard. More and more college students are utilizing AI tools like ChatGPT and Bard to enhance their learning. This study compares the use of web-search and AI-based systems to assess how much students' knowledge has improved in an Internet Marketing course. Background While authorship and academic integrity have received a lot of attention in higher education research thus far, using AI effectively provides several benefits for both teaching and learning. In other words, by utilizing these capabilities, AI-based systems may boost students' motivation, interest, and level of knowledge. Methodology The course will teach the students how to use the application of AI (i.e. ChatGPT). To evaluate the efficacy of AI technology, this study will conduct empirical research to compare the knowledge improvement of students who would obtain knowledge about buying sustainable production products in Taiwan between using the web-search systems and the AI-based systems. The assessments of students' knowledge growth are evaluated by using a dependent samples t-test. Contribution This finding expands on prior research examining the possibility of using AI-powered learning aids to inspire students (Al Shamsi et al., 2022; Lund et al., 2023). The ability of ChatGPT to deliver cutting-edge content, personalized feedback, and interactive learning sessions may all contribute to generating curiosity, a sense of accomplishment, and ultimately a greater desire to learn more about subjects. Findings It is anticipated that the AI chatbots created for information delivery might have something to do with students' increased interest in, motivation for, and understanding of their studies. This study examines the potential benefits to students' understanding of sustainable development goals with an AI-specific method of information provision relevant to purchasing items produced sustainably. Recommendations for Practitioners In the near run, using AI systems has been proven to boost university students' sentiments. University students may feel more supported by AI when they use it more regularly. Long-term AI use may cause reliance, especially in the absence of human companionship. Recommendations for Researchers Not all students and instructors have a lot of experiences in teaching and learning with generative AI, despite the fact that it is a relatively new technology. After generative AI is completely incorporated into classrooms, the roles of AI in teaching and learning could reconsider. Impact on Society In order to prepare students for work in a world driven by generative AI, higher education in the future needs change. With an emphasis on in-class and hands-on activities for assessment, new learning outcomes—skills in learning and teaching with AI, AI literacy—and the importance of interdisciplinarity should be highlighted. Future Research Future studies should concentrate on the following topics: interdisciplinary education, creative pedagogies and their evaluation, new assessment and its acceptability.

The International Conference on Information Systems and Artificial Intelligence [ISAI] is an academic conference on the advancement of artificial intelligence and information systems. The conference includes the fields of machine vision, natural language processing, speech recognition and the industry application of AI, and is of great significance to the development of intelligent machines, intelligent human-computer interaction, and effective use of information, human health and social security.The primary goal of the conference is to promote research and developmental activities in information system and artificial intelligence and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in information system and artificial intelligence and related areas.The 2020 5th Annual International Conference on Artificial Intelligence and Information Systems received 608 full-paper and abstract submissions from China and many other countries or regions over the world. Through a rigorous peer-review process, all submissions were refereed based on their quality of content, level of innovation, significance, originality and legibility. The proceedings are published by IOP Conference Series: Journal of Physics: Conference Series (JPCS).We would like to thank all the authors, speaker and participants for their great contributions to the success of ISAI2020 and to express our sincere gratitude to all the members of Technical Program Committee and organizers for their enthusiasm, time, and expertise.ISAI 2020 Organizing Committee

The International Conference on Information Systems and Artificial Intelligence is an academic conference on the advancement of artificial intelligence and information systems. The conference includes the fields of machine vision, natural language processing, speech recognition and the industry application of AI, and is of great significance to the development of intelligent machines, intelligent human-computer interaction, and effective use of information, human health and social security.The primary goal of the conference is to promote research and developmental activities in information system and artificial intelligence and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in information system and artificial intelligence and related areas. In order to further promote the development of artificial intelligence and information systems disciplines and strengthen domestic and international academic exchanges and cooperation.

This article explores the impact of artificial intelligence (AI) on society through the lens of technological determinism and singularity theories. Technological determinism is the notion that technology shapes and controls society and human behaviour. Singularity is a theory that asserts that AI has already become a million times smarter than humans and can self-improve beyond what humans first taught AI applications and machines. The Singularity Theory predicts an intelligence explosion from Artificial General Intelligence soon, in which humans are likely to lose the dominion that they have enjoyed since creation, millions of years ago. AI, in its generative and autonomous or selfimproving state or form, may lead to the automation of many tasks currently performed by humans. This could lead to both benefits and challenges, such as increased efficiency but also job losses. In addition, the article discusses the impact of AI on privacy and raises ethical concerns about the potential misuse when in the hands of bad people. It also discusses ways to ensure that AI is used responsibly and beneficially. This includes governmental authorities developing ethical guidelines for AI development and implementation and ensuring that AI systems are safe, transparent and accountable.

Emotions are ubiquitous to human life. Previous studies suggest that ninety percent of the life experience of humankind is affected by at least one emotion, and that emotional state guides their thoughts and behavior. The neurological study suggests that the design of human brain ensures that the emotional brain almost always influences the rational brain. Which means every rational decision is affected by the underlying emotional state. For decades, the influence of emotional states on human life has been overlooked by the AI researchers until recently. For the last twenty years, the study on different aspects of emotions and its detection for the more significant cause is gaining considerable attention and importance. With the advent of the Fourth Industrial Revolution (4IR), advancements in Artificial Intelligence (AI) is already touching new heights and in this context successful detection of emotional states by AI systems will be another big leap towards more realistic social AI agents. This chapter will discuss in detail the importance and recent technological advancements towards emotion recognition (ER) in the backdrop of the Fourth Industrial Revolution. ER uses different types of human physical and behavioral expressions, such as facial features, speech features, and their variants. Deep Learning (DL) has emerged as one of the prominent pillars of 4IR. ER task extensively uses DL techniques for classifying different emotions. So, this chapter includes a discussion of relevant technical aspects of DL. Finally, it includes a brief description of a newly proposed Speech Emotion Recognition (SER) model as a case study of the ER systems.

This study presents a comprehensive framework designed to enhance data security in artificial intelligence (AI) systems by integrating robust cybersecurity measures with information governance principles. Employing a convergent parallel mixed-methods design, the research combines a quantitative meta-analysis of 11,245 AI deployments, a qualitative synthesis of 89 governance studies, and technical validations across three open-source AI models (BERT, YOLOv7, and federated learning). Critical threats, including data poisoning (42% success rate), adversarial examples (23% higher vulnerability in vision models), and model inversion (31% more frequent in generative AI), were identified. The proposed framework demonstrated a Composite Security Score (CSS) of 0.87, outperforming existing models (CSS range: 0.78–0.82), and achieved 84–89% attack mitigation and 90–95% regulatory compliance under standards like GDPR and NIST RMF. The framework’s practical contribution lies in offering sector-specific, scalable solutions for strengthening AI system security, thereby enabling healthcare, finance, and public sector organizations to align innovation with ethical and legal obligations. Theoretically, the study advances knowledge by bridging cybersecurity engineering with governance structures, addressing a critical gap in AI risk management. Preliminary evaluations into quantum-ready encryption strategies were explored, though more extensive testing is suggested for future research. Key limitations include reliance on secondary datasets, potential model generalizability constraints, and emerging threats like quantum attacks that require ongoing adaptation. Recommendations advocate sector-specific adoption, quantum-ready encryption, and research into adaptive security and third-party risks to ensure scalable, ethical AI security.

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

BackgroundInformation systems based on artificial intelligence (AI) have increasingly spurred controversies among medical professionals as they start to outperform medical experts in tasks that previously required complex human reasoning. Prior research in other contexts has shown that such a technological disruption can result in professional identity threats and provoke negative attitudes and resistance to using technology. However, little is known about how AI systems evoke professional identity threats in medical professionals and under which conditions they actually provoke negative attitudes and resistance.ObjectiveThe aim of this study is to investigate how medical professionals’ resistance to AI can be understood because of professional identity threats and temporal perceptions of AI systems. It examines the following two dimensions of medical professional identity threat: threats to physicians’ expert status (professional recognition) and threats to physicians’ role as an autonomous care provider (professional capabilities). This paper assesses whether these professional identity threats predict resistance to AI systems and change in importance under the conditions of varying professional experience and varying perceived temporal relevance of AI systems.MethodsWe conducted 2 web-based surveys with 164 medical students and 42 experienced physicians across different specialties. The participants were provided with a vignette of a general medical AI system. We measured the experienced identity threats, resistance attitudes, and perceived temporal distance of AI. In a subsample, we collected additional data on the perceived identity enhancement to gain a better understanding of how the participants perceived the upcoming technological change as beyond a mere threat. Qualitative data were coded in a content analysis. Quantitative data were analyzed in regression analyses.ResultsBoth threats to professional recognition and threats to professional capabilities contributed to perceived self-threat and resistance to AI. Self-threat was negatively associated with resistance. Threats to professional capabilities directly affected resistance to AI, whereas the effect of threats to professional recognition was fully mediated through self-threat. Medical students experienced stronger identity threats and resistance to AI than medical professionals. The temporal distance of AI changed the importance of professional identity threats. If AI systems were perceived as relevant only in the distant future, the effect of threats to professional capabilities was weaker, whereas the effect of threats to professional recognition was stronger. The effect of threats remained robust after including perceived identity enhancement. The results show that the distinct dimensions of medical professional identity are affected by the upcoming technological change through AI.ConclusionsOur findings demonstrate that AI systems can be perceived as a threat to medical professional identity. Both threats to professional recognition and threats to professional capabilities contribute to resistance attitudes toward AI and need to be considered in the implementation of AI systems in clinical practice.

Building a human-like integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model (PGM)-based cognitive architecture to develop a cognitive system for developmental robots by integrating PGMs. The proposed development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information.In this paper, we describe the rationale for WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, WB-PGM provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics.

The history of artificial intelligence (AI), from its conception to the creation of general AI (Gen-AI), is a fascinating story of human inventiveness, technical growth, and philosophical research. This article examines the historical milestones, major inventions, and transformational concepts that have influenced AI's trajectory. Beginning with early symbolic AI and rule-based systems, it investigates the shift to machine learning, highlighting discoveries in neural networks and deep learning that transformed disciplines such as computer vision and natural language processing. The introduction of generative models, such as GANs and VAEs, resulted in a considerable increase in AI capabilities, paving the path for Gen-AI. Unlike narrow AI, Gen-AI strives to imitate human-like intelligence and adaptability across a wide range of jobs, bringing serious ethical and philosophical concerns. This essay also looks at the current state of Gen-AI, its problems, and possible applications in healthcare, education, finance, and other areas. It finishes by picturing a future in which human-machine collaboration and ethical AI development are prioritized, highlighting the need of continual learning and responsible innovation.