This article examines the productive capacity of emotion through the concept of emotional fundamentalism. Emotional fundamentalism combines several key concepts—fundamentalism, affective labor, biopolitics, and capitalism’s contradictions—developed by Michael Hardt and Antonio Negri in Empire, Multitude, and Commonwealth to describe the intensified attention to the body in education. I investigate the implications of the increased organizational and corporate interest in emotion using an ongoing socio-emotional learning study and the introduction of artificial intelligence aggression detectors in schools. Doing so (...) demonstrates the tendency of Empire to manage emotions in order to ensure viable production by educating the body as data and human capital. However, though Empire depends on emotions as a resource, these same emotions display a creative unruliness that exposes potential avenues to reproduce the common and enact multitude. (shrink)

I develop a multilevel, holistic view of persons, emphasizing embodiment, emotions, consciousness, and the social self. In successive sections I draw from six sources: 1. Theology. The biblical understanding of the unitary, embodied, social self gave way in classical Christianity to a body‐soul dualism, but it has been recovered by many recent theologians. 2. Neuroscience. Research has shown the localization of mental functions in regions of the brain, the interaction of cognition and emotion, and the importance of social interaction (...) in evolutionary history and child development. 3. Artificial intelligence. Some forms of robotics use embodied systems that learn by interacting with their environment, but the possibilities for emotion, socialization, and consciousness in robots remain problematic. 4. Relations between levels. Concepts that can help us relate studies of neurons and persons include the hierarchy of levels, the communication of information, thebehavior of dynamic systems, and epistemological and ontological emergence. 5. Philosophy of mind. Two‐aspect theories of the mind‐brain relation offer an alternative between the extremes of eliminative materialism and the thesis that consciousness is irreducible. 6. Process philosophy. I suggest that process thought provides a coherent philosophical framework in which these themes can be brought together. It combines dipolar monism with organizational pluralism, and it emphasizes embodiment, emotions, a hierarchy oflevels, and the social character of selfhood. (shrink)

Advances in digital technology are changing the methods of teaching and learning. In the course of this stream of changes, the emergence of artificial intelligence (AI) has become a topic of great interest and concern. The development of teaching AIs (artificial intelligences that teach), which can provide adaptive and personalized dialogue with students, has shifted the relationship between teachers and students, and has raised new questions regarding the role of teachers. The purpose of this paper is to rethink (...) the meaning of the act of teaching, without denying its variety, and to reflect on the role of teachers in the relatively unfamiliar situation created by the emergence of AI teaching machines. To this end, I attempt, first, to re-examine the nature of teaching by considering two possible orientations: teaching as a return to homogeneity and teaching as exposure to heterogeneity. My purpose is to consider the characteristics and limitations of the teaching implemented by AI and to discuss the role of human teachers in overcoming these limitations. Second, I seek to reveal what it is for the teacher to speak, which is often understood as the most important aspect of teaching, again by way of two characterizations: the closed conversation, which converges on a conclusion, and the open conversation, oriented towards ethical responsibility. In doing this, I shall examine critically some characteristics of language used by AI teaching machines and suggest alternatives that might constitute openness to an ethical conversation. In furtherance of this, I shall consider the account of teaching that is central to the philosophy of Emmanuel Levinas. (shrink)

Artificial Intelligence (AI) is transforming our world: today machines not only can mimic human actions but out-perform human agents in many activities, including learning and thinking. AI offers revolutionary solutions and new possibilities in transportation, business, communication, medicine, law, and other domains. While some welcome this brave new world, others fear the threats AI pose to people’s livelihoods, social relations, individuality, freedom, and perhaps even the very survival of the human species. No doubt some of this existential angst is (...) exaggerated, but AI does raise questions for our understanding of the world and ourselves that require serious reflection, including questions about adequacy of education in various aspects. This chapter offers, from the perspectives of John Dewey’s Pragmatism as well as Confucianism, some reflections on the role of the humanities in education in response to the opportunities and challenges of the development and widespread use of AI. It shows that Dewey and Confucius share similar views regarding the humanistic purpose of education and their philosophies of education offer arguments for why humanities education will be relevant, if not more important, when many jobs we are familiar with become obsolete. Their attitudes toward the economic motive in education will help us rethink the meaning of work in a “world without work.” At the same time, they offer a critical evaluation of contemporary humanities education, which have failed to realize their visions of personal cultivation and growth. Among its failings is the continued dichotomy between the humanities and the sciences. In the age of AI, it has become even more vital to integrate them to avoid science and technology from becoming materialistic and anti-human while the humanities become merely literary and without any means to transform the world. (shrink)

Artificial intelligence is a current tool that is used in different areas of the human life with the purpose of facilitating and directing the processes in distinct sceneries of society. This research aimed to use AI as an educational resource in teaching and learning process in the area of biology for students at the Adventist School CEA Cambulos in the city of Cali. A qualitative research methodology was used, with data collection techniques such as observation and interviews, presenting (...) an action research approach. The main results were the potential of AI to enhance learning, the disposition of students to learn through it, and the need for training in the educational sector for its inclusive use and achieving responsible and effective results. That is how it is considered the challenge of reflecting the imminent arrival of AI in the student context and being prepared to be part of the transformation that can be generated in pedagogical processes. (shrink)

Over the past ten years, we have worked in a collaboration between educators and computer scientists at the University of Illinois to imagine futures for education in the context of what is loosely called “artificial intelligence.” Unhappy with the first generation of digital learning environments, our agenda has been to design alternatives and research their implementation. Our starting point has been to ask, what is the nature of machine intelligence, and what are its limits and potentials in education? This (...) paper offers some tentative answers, first conceptually, and then practically in an overview of the results of a number of experimental implementations documented in greater detail elsewhere. Our key finding is that artificial intelligence—in the context of the practices of electronic computing developing over the past three quarters of a century—will never in any sense “take over” the role of teacher, because how it works and what it does are so profoundly different from human intelligence. However, within the limits that we describe in this paper, it offers the potential to transform education in ways that—counterintuitively perhaps—make education more human, not less. (shrink)

The recent appearance of generative artificial intelligence (AI) platforms has been seen by many as disruptive for education. In this paper I attempt to locate the source of tension between educational goals and new information technologies including AI. I argue that this tension arises from new conceptions of epistemic agency that are incompatible with educational aims. I describe three competing theories of epistemic agency which I refer to as Makers, Managers, and Inforgs. I contend that educators are (...) correct in maintaining the first of these, which is rooted in the educational theories of Locke and Dewey, as their main educational purpose. Competing theories do not serve the goals of learners, even as they must prepare for life in a very different epistemic environment. (shrink)

Artificial Intelligence Applications in Higher Education offers direct examples of how artificial intelligence systems can be applied in today's higher education contexts. As use of AI rapidly advances within colleges and universities worldwide, there is a pressing need to showcase the challenges, opportunities, and ethical considerations inherent in deploying these advanced computational tools. This book highlights the multifaceted roles of AI across teaching and learning, institutional administration, student data management, and beyond. Its collected case studies furnish actionable insights (...) into enhancing academic institutions and addressing diverse learning priorities such as motivation, engagement, feedback, and achievement goals. This valuable reference for researchers, designers, administrators, teaching faculty, and graduate students across various university programs offers fresh perspectives on generative AI, adaptive learning, intelligent tutoring systems, chatbots, predictive technologies, remote learning, and more. (shrink)

In this article, I establish a theory of knowledge approach for evaluating the use of computers for educational purposes at the university. In so doing, I trace part of the history of the “enabling factor” of Artificial Intelligence in this sector, an important element that has been integrated into everyday learning environments. The result of my reflection is a dialogical structure, directly inspired by past technology assessment research, which illustrates the conceptual advancement of researchers in the field of (...) learning technologies. The notions covered have implications in future policy-related discourse with regards to education. (shrink)

What does it mean to consider the world of AI through a Christian lens? Rapid developments in AI continue to reshape society, raising new ethical questions and challenging our understanding of the human person. Encountering Artificial Intelligence draws on Pope Francis’ discussion of a culture of encounter and broader themes in Catholic social thought in order to examine how current AI applications affect human relationships in various social spheres and offers concrete recommendations for better implementation. The document also explores (...) questions regarding personhood, consciousness, and the kinds of relationships humans might have with even the most advanced AI. Through these discussions, this book investigates the theoretical and practical challenges to interpersonal encounter raised by the age of AI. (AI Research Group for the Centre for Digital Culture of the Dicastery of Culture and Education of the Holy See). (shrink)

The rapid development of programs based on the principles of machine learning (ML) and artificial intelligence (AI) signals significant changes in the components of education, namely in the provider, the tool of transmission, and the recipient of knowledge. Historical data analysis regarding the key functions of education serves as the basis for identifying fundamental innovations introduced through AI and ML. The impact of writing, printing, and the Internet has significantly altered the tool for knowledge transmission, influencing the volume of (...) information and the number of knowledge recipients. The implementation of AI and ML transforms not only the tool but also the provider of knowledge itself, which can become impersonal thanks to the corresponding computer programs. With the historically justified increase in the volume of knowledge possessed by humanity, there is a transformation observed in education systems. This is especially true for democratic societies, where the emphasis is increasingly shifting from providing a large amount of knowledge to developing critical thinking. It has been researched that programs based on AI and ML, applying linguistic models, are capable of effectively systematizing knowledge. This lays the foundation for personalizing the entire education process for a specific knowledge recipient, without burdening the provider. However, there have been cases when such imitation misleads scientists, who perceive it as attempts at communication between programs that have an equivalence to the human level. The conclusions drawn indicate a significant transformation of the education system caused by AI and ML-based programs. However, intelligent programs are unable to evolve into independent knowledge recipients due to their inability to consciously attribute meaning to information, transforming it into knowledge. (shrink)

With the emergence of big data, cloud computing, and other technologies, artificial intelligence technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. (...) It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”. (shrink)

Reflection has been proclaimed as a means to help physicians deal with medicine’s inherent complexity and remedy many of the shortcomings of medical education. Yet, there is little agreement on the nature of reflection nor on how it should be taught and practiced. Emerging neuroscientific concepts suggest that human thought processes are largely nonconscious, in part inaccessible to introspection. Our knowledge of the world is fraught with uncertainty, ignorance and indeterminacy, and influenced by emotion, biases and illusions, including the (...) illusion of not having illusions. Neuroscience also documents that lifelong learning processes may hone nonconscious cognition to high levels of sophistication, allowing rapid and precise perceptions, judgments and actions in complex situations. We argue that knowledge of mechanisms underlying human thought may be useful in designing educational programs to foster desired attributes such as curiosity, critical self-awareness and intuitive acumen in medical professionals. The juxtaposition of neuroscientific insights with ideas from Kant on reflective judgement, van Manen on tact, and Aristotle on phronésis, supports a concept of reflection that manifests as wise practice. We suggest that reflection in medical education should be an imperative for educators seeking to guide learners to manage the complexity and “messiness” of medical practice, and a role-modelling mode of medical practice characterized by self-correcting behaviors that culminate in good and right professional actions. An example illustrates reflective practice in the teaching and learning of physicianship. (shrink)

It is expedient, timely and even urgent to question again Whitehead's approach to education and learning. It is expedient, because education remains (one of) the most important cultural factors. It is timely, because of the need to investigate the efficacy of the process philosophical approach in the context of the growing influence of artificial intelligence. It is urgent, because artificial intelligence gains traction in the context of a global systemic crisis."--Page 4 of cover.

_Purpose._ The study aims to understand artificial intelligence as a socio-cultural phenomenon and its impact on education, where the spiritual sphere of humanity, moral norms, values, and human cognitive abilities are preserved, transferred as well as reproduced. A new discourse on the interaction of artificial and authentic human intelligence becomes inevitable, which has led to a situation of uncertainty. Changes in the socio-cultural environment under the influence of artificial intelligence increase potential threats to the educational space, (...) which stimulates to find the ways to eliminate them. _Theoretical basis._ Various approaches of classical and postmodern philosophical heritage were taken as a theoretical basis for the research. _The originality_ of the study is in the interpretation of artificial intelligence as a modern form of alienation of essential human characteristics in the socio-cultural context of information technology. The expansion of artificial intelligence raises awareness of the existential threat to the basic socio-cultural, moral and ethical principles of humanism. It is proved that various forms of alienation in the current existing socio-cultural space are typical of our reality, which changes the system of values, moral principles, and social organization of the community. _Conclusions._ In conclusion, it is proved that AI is a natural stage of scientific and technological progress, which reflects its secondary, derivative nature from human (authentic) intelligence. Human intelligence will always have advantages over AI due to its ability to create, communicate socially and culturally, and be emotional. The dilemma of the counterbalance between human and artificial intelligence is perceived mainly at the emotional level of people. The millennial understanding of the primacy of the creator over his creation can traditionally overcome this contradiction. The universality of human thinking is an undeniable advantage of human intelligence and a guarantee of its, i.e. our, priority. (shrink)

An Artificial Intelligence algorithm trained on data that reflect racial biases may yield racially biased outputs, even if the algorithm on its own is unbiased. For example, algorithms used to schedule medical appointments in the USA predict that Black patients are at a higher risk of no-show than non-Black patients, though technically accurate given existing data that prediction results in Black patients being overwhelmingly scheduled in appointment slots that cause longer wait times than non-Black patients. This perpetuates racial inequity, (...) in this case lesser access to medical care. This gives rise to one type of Accuracy-Fairness trade-off: preserve the efficiency offered by using AI to schedule appointments or discard that efficiency in order to avoid perpetuating ethno-racial disparities. Similar trade-offs arise in a range of AI applications including others in medicine, as well as in education, judicial systems, and public security, among others. This article presents a framework for addressing such trade-offs where Machine Learning and Optimization components of the algorithm are decoupled. Applied to medical appointment scheduling, our framework articulates four approaches intervening in different ways on different components of the algorithm. Each yields specific results, in one case preserving accuracy comparable to the current state-of-the-art while eliminating the disparity. (shrink)

In the World Library of Educationalists series, international experts themselves compile career-long collections of what they judge to be their finest pieces--extracts from books, key articles, salient research findings, major theoretical and/practical contributions--so the work can read them in a single manageable volume. Readers will be able to follow the themes and strands of their work and see their contribution to the development of a field. A developmental psychologist by training, Howard Gardner has spent the last 30 years researching, thinking (...) and writing about the development and education of the mind. He has contributed over 30 years researching, thinking and writing about the development and education of the mind. He has contributed over 30 books and 700 articles to the field. He is best known for his critique of the notion that intelligence is one single human intelligence that can be assessed through psychometric tests. Instead Gardner developed the theory of "multiple intelligence" which states that an individual has eight relatively autonomous intelligence: · Language · Music · Emotional · Logical-mathematical · Spatial · Kinesthetic · Creative · Interpersonal (understanding oneself) This theory has proved popular, particularly with those who see the IQ testing a relatively narrow set of abilities. In this book, he brings together over 20 of his key writings in one place. The book begins with a specially written Introduction, which gives an overview of Howard's career and contextualizes his selection in this book. Through his selection we can see the development of his thinking as well as the development of the field. This is the only book that offers this insight into this great scholar's work. (shrink)

Abstract:A society that uses drills and conditioning to train its machine learning models risks creating an alienated situation between human and machine in social life, as these teaching methods generate a lack of responsibility for the actions produced by such machines. Both John Dewey and Gilbert Simondon present conceptions and ideas that shed a different light on such alienated human–machine relationships in machine learning: that is, a critique of mere drilling and training as an educational method; the mode of (...) existence of technical objects; and the importance of experience and practicality, for example, learning by doing. After giving an introduction to Dewey's and Simondon's conceptions, the analysis of case studies will show, among other things, that machine learning models need constant care by engineers and programmers. This allows us to highlight Simondon's and Dewey's critique of ancient philosophy. Whereas the latter separates thought and practice, labor and leisure, Simondon and Dewey foreground activities that are capable of bringing both together: Dewey's aesthetic experience and Simondon's technical activity. (shrink)

The review comments and analyses main topics, ideas and theses emphasized in Vesselin Petrov’s new book Elements of Contemporary Process Philosophical Theory of Education and Learning. Describing the structure of the book, the review outlines the author’s analysis of the Process philosophy’ s interpretation of the aims, tasks and elements of the education and learning within the innovative perspectives of internet, computers, the artificial intelligence. The metaphysical principles and notions of the Process Philosophy of A. Whitehead – holism, (...) organism, dynamics, creativity – are applied to the education and learning problems and theories. (shrink)

The experience of organizing the educational process during the quarantine caused by the COVID-19 pandemic is considered. Using of interactive technologies that allow organizing instant audio communication with a remote audience, as well as intelligent tools based on artificial intelligence that can help educational institutions to work more efficiently. Examples of sufficient use of artificial intelligence in distance learning are given. Particular attention is paid to the development of intelligent chatbots intended for use in communications with (...) students of online courses of educational web portals. The use of technologies of ontology formation based on automatic extraction of concepts from external sources is offered, what can lead to greater acceleration of construction of the intellectual component of chatbots. Artificial intelligence tools can become an essential part of distance learning during this global COVID-19 pandemic. While educational institutions are closed to quarantine and many of them transitioned to distance learning lecturers and schoolteachers, as well as students and schoolchildren faced with the necessity to study in this new reality. The impact of these changes depends on people's ability to learn and on the role that the education system will play in meeting the demand for quality and affordable training. The experience of organizing the educational process at the University of Education Management of the National Academy of Pedagogical Sciences of Ukraine in the quarantine caused by the COVID-19 pandemic showed that higher and postgraduate institutions were mostly ready to move to distance learning. However, most distance learning systems, on whatever platform they are organized, need to be supplemented: the ability to broadcast video, providing fast transmission of various types of information, receiving instant feedback when voting, polls and more. The structure of each section of the training course for the online learning system should fully cover the training material and meet all the objectives of the course. Appropriate language should be used, and wording, syntax, and presentation of tasks should be considered. One of the areas of application of artificial intelligence technologies in online learning is the use of chatbots which are characterrized by the following properties. It is advisable to use computer ontologies to ensure the intellectualization of chatbots. In this case, the metadata must be understandable to both humans and software and meet the requirements of modern standards in the field of information technology. The extraction of concepts from external data sources was carried out to build the ontology. (shrink)

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper offers some commentaries on papers drawn from the Journal’s third decade. They indicate a major shift within Artificial Intelligence, both generally and in AI and Law: away from symbolic techniques to those based on Machine Learning approaches, especially those based on Natural Language texts rather than feature sets. Eight papers are discussed: two concern the management and use of documents available on the World (...) Wide Web, and six apply machine learning techniques to a variety of legal applications. (shrink)

In this article, the potential for Artificial Intelligence (AI) to be appraised as an object of beauty is critically examined through the lens of philosophical thought. Tracing beauty’s evolution from Platonic ideals to contemporary interpretations, the analysis contends that AI’s emergence offers a unique illustration of beauty in the modern age. Confronting the challenge of assigning beauty to entities devoid of consciousness or emotional depth, the argument unfolds to suggest that the intricate design of AI’s algorithms and its technological (...) advancements constitute an emergent form of beauty, one inherently tied to human intellectual endeavor. The article proposes that with time, as with historical art forms, the aesthetic qualities of AI may gain broader acknowledgment, implying that technological advancements necessitate an ongoing revision of established beauty norms. The historical evolution of beauty has been traced in details, and an extended literature review has been provided, serving as the foundation for a comprehensive exploration into the potential for AI to be recognized as an object of beauty. The harmony, elegance, and problem-solving capabilities of AI algorithms have been meticulously evaluated, with a comparison made to classical elements of beauty in an effort to contextually situate AI within longstanding aesthetic traditions. Furthermore, the analysis has been extended to consider the emergence of artificial and digital beauty, alongside an investigation into the parallels drawn between divine and artificial intelligence. As part of a deeper inquiry, the possibility of human emotions, such as awe and love, being authentically directed towards AI has been scrutinized. In conclusion, a reflection has been presented on the transformative role of AI in reimagining the conceptual landscape of beauty, prompting readers to thoughtfully engage with an extended review and the philosophical and cultural implications emerging from AI’s evolving influence on the field of aesthetics. (shrink)

The objective of this research has been to evaluate the effect of the integration of Artificial Intelligence on the motivation of students, in the area of Chemistry of tenth grade of the Adventist School of Ibagué. To this end, a methodology with a qualitative approach and a Participatory Action Research design with four phases were used: planning, action, observation and reflection. The information was collected through observation and interview techniques for which field diaries and structured script were used as (...) research instruments. This information was analyzed with the Atlas.ti 23 software, the resulting codes allowed the definition of the categories: experience in the use of artificial intelligence, motivation and commitment, meaningful learning and perceptions about teaching and learning. The results obtained reflect an increase in student motivation and engagement, as well as a favorable impact on meaningful learning. They highlight the pedagogical potential of this resource, to diversify and enrich didactics in the teaching of chemistry. A positive perception by students towards the implementation of AI in the classroom was identified. It is concluded that the use of AI mediated with the design of pedagogical strategies to create learning environments and situations can improve the motivation of students, generating a space for knowledge and meaningful learning. However, challenges were also identified, such as the need for teacher and student training in the management and proper use of technological resources. In addition, the importance of not considering artificial intelligence as a total replacement for the role of the teacher, but as a complementary tool that requires an adequate pedagogical approach, is highlighted. (shrink)

The idea of using artificial intelligence as a support system in the sentencing process has attracted increasing attention. For instance, it has been suggested that machine learning algorithms may help in curbing problems concerning inter-judge sentencing disparity. The purpose of the present article is to examine the merits of this possibility. It is argued that, insofar as the unfairness of sentencing disparity is held to reflect a retributivist view of proportionality, it is not necessarily the case that increasing inter-judge (...) uniformity in sentencing is desirable. More generally, it is shown that the idea of introducing machine learning algorithms, that produce sentencing predictions on the ground of a dataset that is built of previous sentencing decisions, faces serious problems if there exists a discrepancy between actual sentencing practice and the sentences that are ideally desirable. (shrink)

A concise but informative overview of AI ethics and policy. -/- Artificial intelligence, or AI for short, has generated a staggering amount of hype in the past several years. Is it the game-changer it's been cracked up to be? If so, how is it changing the game? How is it likely to affect us as customers, tenants, aspiring homeowners, students, educators, patients, clients, prison inmates, members of ethnic and sexual minorities, and voters in liberal democracies? Authored by experts in (...) fields ranging from computer science and law to philosophy and cognitive science, this book offers a concise overview of moral, political, legal and economic implications of AI. It covers the basics of AI's latest permutation, machine learning, and considers issues such as transparency, bias, liability, privacy, and regulation. -/- Both business and government have integrated algorithmic decision support systems into their daily operations, and the book explores the implications for our lives as citizens. For example, do we take it on faith that a machine knows best in approving a patient's health insurance claim or a defendant's request for bail? What is the potential for manipulation by targeted political ads? How can the processes behind these technically sophisticated tools ever be transparent? The book discusses such issues as statistical definitions of fairness, legal and moral responsibility, the role of humans in machine learning decision systems, “nudging” algorithms and anonymized data, the effect of automation on the workplace, and AI as both regulatory tool and target. (shrink)

This book explores a value-based research methodology, Living Educational Theory Research (LETR), which aligns a values-based approach with key tenets of professional development to inform and inspire future educators' practice. Written by the world-leading scholars in the field of LETR, chapters are global in reach and promote the evolving and dynamic nature of the methodology and its application with real-world professional training within higher education. Through discussion and dialogue on the evolution of Living Educational Theory Research, chapters explore (...) topics such as professional development and community-based contexts, supporting academics wishing to improve their practice by placing the theory within a scholarly paradigm to legitimise its use for scholarly learning. Demonstrating how insights from disciplines such as philosophy, sociology, and psychology are integrated within the generation of living-educational-theories, this outwardly looking volume will appeal to postgraduate students, scholars, and researchers involved with educational theory, action research and other forms of practitioner research, and education research methods more broadly. (shrink)

PurposeThis paper aims to demonstrate the relevance of worldviews of the global south to debates of artificial intelligence, enhancing the human rights debate on artificial intelligence (AI) and critically reviewing the paper of UNESCO Commission on the Ethics of Scientific Knowledge and Technology (COMEST) that preceded the drafting of the UNESCO guidelines on AI. Different value systems may lead to different choices in programming and application of AI. Programming languages may acerbate existing biases as a people’s worldview is (...) captured in its language. What are the implications for AI when seen from a collective ontology? Ubuntu (I am a person through other persons) starts from collective morals rather than individual ethics.Design/methodology/approachLiterature overview on the African philosophy of Ubuntu as applied to artificial intelligence. Application of it to the United Nations Educational, Scientific and Cultural Organisation (UNESCO) debates on establishing guidelines to the ethics of artificial intelligence.FindingsMetaphysically, Ubuntu and its conception of social personhood (attained during one’s life) largely rejects transhumanism. When confronted with economic choices, Ubuntu favors sharing above competition and thus an anticapitalist logic of equitable distribution of AI benefits, humaneness and nonexploitation. When confronted with issues of privacy, Ubuntu emphasizes transparency to group members, rather than individual privacy, yet it calls for stronger (group privacy) protection. In democratic terms, it promotes consensus decision-making over representative democracy. Certain applications of AI may be more controversial in Africa than in other parts of the world, like care for the elderly, that deserve the utmost respect and attention, and which builds moral personhood. At the same time, AI may be helpful, as care from the home and community is encouraged from an Ubuntu perspective. The report on AI and ethics of the UNESCO World COMEST formulated principles as input, which are analyzed from the African ontological point of view. COMEST departs from “universal” concepts of individual human rights, sustainability and good governance which are not necessarily fully compatible with relatedness, including future and past generations. Next to rules based approaches, which may hamper diversity, bottom-up approaches are needed with intercultural deep learning algorithms.Research limitations/implicationsThere is very few existing literature on AI and Ubuntu. Therefore, this paper is of an explorative nature.Practical implicationsThe ethics of Ubuntu offers unique vantage points in looking at the organization of society and economics today, which are also relevant for development of AI, especially in its tenet of relatedness rather than individuality (and practical use of AI for individuals), taking responsibility for society as a whole (such as analyzing the benefit of AI for all strata of society), and embodying true inclusiveness. Whether looking at top-down guidelines for the development and implementation of AI or the bottom-up ethical learning process of AI (deep learning), ethics of the Global South can have an important role to play to combat global individualist tendencies and inequity, likely reinforced by AI. This warrants far more research.Social implicationsApplications of AI in Africa are not contextualized, do not address the most pressing needs of the African continent, lead to cybersecurity issues and also do not incorporate African ethics. UNESCO’s work in this regard is important but expert inputs are largely centered around Western “universal” principles and Organisation for Economic Cooperation and Development and EU precedence. African ethics have, so far, a small role to play in global ethics and philosophy and therefore risk to be overlooked in the discussion on AI and ethics. This is why the consultation process of UNESCO on ethics of AI was of paramount importance. However, it does not automatically lead to consultation of African philosophers or sages, as many are educated in Western (ized) education systems. See further details under practical implications.Originality/valueThis is a new area of research in which little work has been done so far. This paper offers the opportunity to widen the debate on AI and ethics beyond the conventional discourse, involving multiple worldviews, of which Ubuntu is just one. (shrink)

The human-centred debate in Britain focuses on the idea of human-machine symbiosis, and the “Dialogue” debate in Scandinavia focuses on the deep understanding of human communication, through a process of inner reflection. Both of these debates provide a framework for the participatory design of AI systems.The emergence of “social Europe” creates the desirability for a sharing of social and cultural knowledge and resources among the citizens of Europe. This raises the possibility of exploiting the potential of new technology for the (...) distribution and transfer of knowledge and skill among the citizens of Europe whilst retaining the diversity of their cultures and languages. These issues of transfer have been the focus of many AI researchers who have been concerned with the design of AI systems for the education, training and welfare of disadvantaged citizens. It is suggested that central to such a transfer is the design of participatory learning systems which focus on the mediation and distribution of knowledge in diverse socio-cultural contexts.In this paper, the SEAKE Centre's projects, especially “Brighton Parosi”, and “Adult Literacy” are discussed to provide an insight into the concepts and practice of designing such participatory systems. The philosophy of these projects was rooted in the sharing of social and cultural experiences, through participatory learning. The development of the projects involved a process of exchange of language and culture in both formal and informal learning environments. For the Parosi Project social interaction was the central core for the exchange of cultural experiences and the language acquisition process. The Adult Literacy project built on the exchange process, and used the idea of the mediation of knowledge for the design of a knowledge based system. The design process developed by the above two projects could be described as a participatory design approach which builds upon and extends the human-machine symbiosis and dialogue approaches. (shrink)

Medically unexplained symptoms (MUS) are common and costly in both primary and secondary health care. It is gradually being acknowledged that there needs to be a variety of interventions for patients with medically unexplained symptoms to meet the needs of different groups of patients with such chronic long-term symptoms. The proposed intervention described herewith is called The BodyMind Approach (TBMA) and promotes learning for self-management through establishing a dynamic and continuous process of emotional self-regulation. The problem is the mismatch between (...) the patient’s mind-set and profile and current interventions. This theoretical article, based on practice-based evidence, takes forward the idea that different approaches (other than cognitive behaviour therapy) are required for people with MUS. The mind-set and characteristics of patents with MUS are reflected upon to shape the rationale and design of this novel approach. Improving services for this population in primary care is crucial to prevent the iterative spiralling downwards of frequent GP visits, hospital appointments, and Accident and Emergency attendance, all of which are common for these patients. The approach derives from embodied psychotherapy (authentic movement in dance movement psychotherapy) and adult models of learning for self-management. It has been developed from research and practice-based evidence. In this article the problem of MUS in primary care is introduced and the importance of the reluctance of patients to accept a psychological/mental health referral in the first instance is drawn out. A description of the theoretical underpinnings and philosophy of the proposed alternative to current interventions is then presented related to the design, delivery, facilitation and educational content of the programme. The unique intervention is also described to give the reader a flavour. (shrink)

We describe a form of moral artificial intelligence that could be used to improve human moral decision-making. We call it the “artificial moral advisor”. The AMA would implement a quasi-relativistic version of the “ideal observer” famously described by Roderick Firth. We describe similarities and differences between the AMA and Firth’s ideal observer. Like Firth’s ideal observer, the AMA is disinterested, dispassionate, and consistent in its judgments. Unlike Firth’s observer, the AMA is non-absolutist, because it would take into account (...) the human agent’s own principles and values. We argue that the AMA would respect and indeed enhance individuals’ moral autonomy, help individuals achieve wide and a narrow reflective equilibrium, make up for the limitations of human moral psychology in a way that takes conservatives’ objections to human bioenhancement seriously, and implement the positive functions of intuitions and emotions in human morality without their downsides, such as biases and prejudices. (shrink)

Artificial intelligence (AI) has recently emerged as a transformative force in teaching and learning practices, with profound implications for open and distance learning (ODL), which relies heavily on technology. Despite its global impact, the extent of African societies’ engagement with AI remains trivial. This paper critically reflects on the ethical, legal, social, pedagogical and technological implications of AI in ODL in sub-Saharan Africa, drawing insights from the Nigerian experience. Adopting the scoping review methodology, the paper explores and synthesises existing (...) literature to foreground a critical analysis of the implications of AI in the African context. (shrink)

Our synthetic review of the relevant and related literatures on the ethics and effects of using AI in education reveals five qualitatively distinct and interrelated divides associated with access, representation, algorithms, interpretations, and citizenship. We open our analysis by probing the ethical effects of algorithms and how teams of humans can plan for and mitigate bias when using AI tools and techniques to model and inform instructional decisions and predict learning outcomes. We then analyze the upstream divides that feed into (...) and fuel the algorithmic divide, first investigating access and then representation. After that, we analyze the divides that are downstream of the algorithmic divide associated with interpretation and citizenship. At present, lacking ongoing reflection and action by learners, educators, educational leaders, designers, scholars, and policymakers, the five divides collectively create a vicious cycle and perpetuate structural biases in teaching and learning. However, increasing human responsibility and control over these divides can create a virtuous cycle that improves diversity, equity, and inclusion in education. We conclude the article by looking forward and discussing ways to increase educational opportunity and effectiveness for all by mitigating bias through a cycle of progressive improvement. (shrink)

Resumo: Com o rápido desenvolvimento da sociedade da informação moderna, o Big Data (BD) trouxe grande comodidade à vida dos residentes. No contexto do BD, a aplicação da tecnologia da informação em diversas áreas tornou-se uma tendência. Através da transformação da filosofia educacional, a Gestão do Ensino Superior também assumiu novas características e gerou novos rumos de desenvolvimento. Este artigo explora as deficiências e razões do trabalho tradicional de Gestão de Esino Superior, aponta problemas atuais e explica a necessidade de (...) sua inovação. Também analisa as vantagens e o desenvolvimento da tecnologia BD no trabalho de gestão. Através das novas ideias, métodos e ferramentas trazidas pela tecnologia Big Data para a Gestão do Ensino Superior, foi demonstrado que a tecnologia BD apoiou o desenvolvimento de modalidades de ensino inovadoras, proporcionou um ambiente de aprendizagem positivo e reduziu os custos de gestão. Com base nas características do trabalho da Gestão e nas vantagens da tecnologia BD, foram realizadas pesquisas inovadoras para analisar a eficiência e o entusiasmo da aprendizagem dos alunos para ilustrar o efeito inovador desse trabalho. Os autres defendem que a tecnologia BD, incluindo Inteligência Artificial e gestão precisa e flexível, traz novas ideias, métodos e ferramentas para a GEstão, melhorando efetivamente a eficiência, aumentando a felicidade dos alunos e reduzindo os custos de gestão laboral. Ademais, apoiam o desenvolvimento de modelos de ensino inovadores, oferecem aos estudantes universitários um ambiente de aprendizagem positivo e reduzem os custos de gestão. (shrink)

Abstract -- Background, context, overview, and guiding philosophy -- Emotional intelligence meets virtue ethics : implications for educators -- Emotional intelligence as a component of business ethics pedagogy -- Nourishing life, the daoist concept of virtue -- Cultivation of virtue (dé) 1 according to the neiye -- Cultivation of virtuous leaders according to the huainanzi -- Is there a place for contemplation and inner work in business ethics education? -- Incorporating the inner work of ei and contemplation in ethics education (...) -- Glossary -- About the author. (shrink)

The vision of integrating artificial intelligence in education is part of an ongoing push for harnessing digital solutions to improve teaching and learning. Drawing from Jasanoff and Hasse, this paper deliberates on how sociotechnical imaginaries are interrelated to the implications of new technologies, such as AI, in education. Complicating Hasses’s call for the development of Socratic ignorance to consider our predispositions about new technologies and open new prospects of thought, this paper revisits postphenomenology and Feenberg’s critical constructivist theories. While (...) embracing the notion of Socratic ignorance, this paper stresses the importance of developing a nuanced understanding of technology that realizes its lack of neutrality and supports the creation of a deeper understanding of how knowledge is produced, deployed, and interpreted in the digital age. Thus, this paper argues that an amalgam of Hasse’s call for advancing Socratic ignorance combined with postphenomenology and critical constructivism can support students in developing a critical understanding of technology and opening new landscapes of imaginaries. (shrink)

With the expansion of artificial intelligence and advanced technologies, theworld in the 21st century is rapidly changing and imposing new living dynamics. Althoughsuch changes affect all age groups, younger generations accept them faster andreact more positively. The new cohorts - Generation Z and Alpha - live in a digital worldthat affect their lifestyle, interpersonal relations, quality of mental health, psychologicalwell-being and everyday challenges. The presence of the so called “Frankenstein effect”in some adults provoked by the fast development of (...) class='Hi'>artificial intelligence and robotics,reflects a “humans versus machines” position, viewing artificial intelligence as athreat to humanity. However, the reality is that digital and human world are not inconflict, since many people are already using artificial intelligence tools on daily basis.It is implemented in certain aspects of medicine, education, business, law, agriculture,industry, space technology and in many other fields. With this in mind, the aspect ofmorality pops up as a very important one. A frequently asked question is: Does artificialintelligence have the capacity to make moral decisions independently? Therefore, integratingmorality into AI algorithms is one of the priorities that interdisciplinary teamsfrom engineering and computer sciences, psychology, philosophy, sociology, law, etc.are intensively working on. This paper addresses this issue by presenting findings fromrelevant research which discusses the challenges and possibilities for integrating thedimensions of morality and introducing human values into autonomous systems thatperform complex tasks. (shrink)

In this paper, I examine why moral exemplars are useful and even necessary in moral education despite several critiques from researchers and educators. To support my point, I review recent AI research demonstrating that exemplar-based learning is superior to rule-based learning in model performance in training neural networks, such as large language models. I particularly focus on why education aiming at promoting the development of multifaceted moral functioning can be done effectively by using exemplars, which is similar to exemplar-based learning (...) in AI model training. Furthermore, I discuss the potential limitations and issues related to exemplar-applied moral education with findings from recent studies in AI research raising concerns about model biases and toxic outcomes. I attempt to propose ways to address the concerns regarding employing moral exemplars as well. As remedies, I suggest that autonomy-supporting deliberative and reflective learning processes should be utilized. Furthermore, based on the discussion, I examine how macroscopic socio-cultural aspects influence the effectiveness of exemplar-applied moral education. Suggestions for moral educators and future directions for research in moral education are briefly discussed. (shrink)

As we write this research paper, we notice an explosion in popularity of machine learning in numerous fields (ranging from governance, education, and management to criminal justice, fraud detection, and internet of things). In this contribution, rather than focusing on any of those fields, which have been well-reviewed already, we decided to concentrate on a series of more recent applications of deep learning models and technologies that have only recently gained significant track in the relevant literature. These applications are concerned (...) with artistic production (Sect. 2.1), the writing process (Sect. 2.2), music production (Sect. 2.3), text recognition and attribution (Sect. 2.4). After reviewing and analyzing the positive contributions as well as some of the major limitations of these technologies in each of those fields, we critically reflect (Sect. 3) on how their widespread implementation may affect humans and their creativity. In Sect. 4, we notice that deep learning models are here to stay; so, rather than embracing a negative or pessimistic stance with respect to their future applications in creative domains, we suggest a balanced approach for their assessment and beneficial usage. Finally (Sect. 5), we conclude by summarising what we have achieved and by pointing out possible future research directions. (shrink)

The dream of achieving artificial intelligence and, in particular, artificial consciousness, is reflected in mythologies and popular culture as utopia and dystopia. This article discusses its conceptual possibility. It first relates the desire to realise strong AI to a self-perception of humanity as opposed to nature, metaphorically represented as gods or God. The realisation of strong AI is perceived as an ultimate victory on nature or God because it represents the crown of creation or evolution: conscious intelligence. The (...) paper proceeds to summarise two debates relevant to AI: one educational and one technological. The technological debate, almost invariably presupposing a materialist framework, is related to the mind–body problem of philosophy; the educational one to understanding the concept of intelligence. By proposing a definition of intelligence linked to an idealist conception of reality, postulating mind as participation in Absolute Mind, I attempt a convergence of these debates, rejecting the possibility of strong AI. (shrink)

Algorithmic probability and friends: Proceedings of the Ray Solomonoff 85th memorial conference is a collection of original work and surveys. The Solomonoff 85th memorial conference was held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff, honouring his various pioneering works - most particularly, his revolutionary insight in the early 1960s that the universality of Universal Turing Machines could be used for universal Bayesian prediction and artificial intelligence. This work continues to increasingly influence (...) and under-pin statistics, econometrics, machine learning, data mining, inductive inference, search algorithms, data compression, theories of intelligence and philosophy of science - and applications of these areas. Ray not only envisioned this as the path to genuine artificial intelligence, but also, still in the 1960s, anticipated stages of progress in machine intelligence which would ultimately lead to machines surpassing human intelligence. Ray warned of the need to anticipate and discuss the potential consequences - and dangers - sooner rather than later. Possibly foremostly, Ray Solomonoff was a fine, happy, frugal and adventurous human being of gentle resolve who managed to fund himself while electing to conduct so much of his paradigm-changing research outside of the university system. The volume contains 35 papers pertaining to the abovementioned topics in tribute to Ray Solomonoff and his legacy. (shrink)

The student management work model in colleges and universities is an effective plan for college student management, but the traditional college student management work is not very good in terms of student psychology, resulting in negative attitudes such as low learning desire, low learning efficiency, and inactive learning. In recent years, with the development of artificial intelligence technologies such as sentiment analysis and incentive theory, emotional management and incentive theory have been applied to the management of college students. The (...) emotional management and incentive model is a way to help college students get rid of psychological obstacles and guide students to establish positive and correct values by predict and analyze the psychological state of college students through language emotion recognition and BP neural network. This paper compares the college student management work model based on emotional management and incentives with the traditional college management work mode through experiments. The results show that the students’ learning enthusiasm is better than the traditional college student management work mode based on emotional management and incentives. The student management work model in colleges and universities is 15.8% better, and the students’ grades have improved by 12.5%; the college student management work model based on emotional management and incentives also has a positive role in helping students’ mental health. The way of emotional management and motivation can make better use of college students’ psychology to effectively manage students and guide students to develop in a good direction. (shrink)

ABSTRACT I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments. INTRODUCTION In contrast to the classic model of randomized-control trials, often with a large number of (...) subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii] ANALYSIS I. The State of Precision Medicine in Oncology and the Epistemological Shift While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi] A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv] The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii] As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over. II. Responsive Neurostimulation, AI, and Personalized Medicine Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials. Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx] In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.) There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it. III. Ethical Implications and Next Steps While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed. The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii] If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials. CONCLUSION The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making. ACKNOWLEDGEMENT This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback. - [i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0. [ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649. [iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873. [iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208. [v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104. [vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5 f67941fa59e. [vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5. [viii] Kimmelman and Tannock, 2018. [ix] Kimmelman and Tannock, 2018. [x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789. [xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.” [xii] Vogt and Hofmann, 1206. [xiii] Vogt and Hofmann, 1206. [xiv] Vogt and Hofmann, 1206. [xv] Vogt and Hofmann, 1207. [xvi] Vogt and Hofmann, 1207. [xvii] Vogt and Hofmann, 1207. [xviii] Vogt and Hofmann, 1210. [xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002. [xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1. [xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001. [xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003. [xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026. [xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104. [xxv] Burdette and Swartz, 126. [xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4. [xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482. [xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034. [xxix] Nho et al. [xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd. [xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.” [xxxii] Awuah et al., “Bridging Minds and Machines.” [xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097. [xxxiv] Green, Prainsack, and Sabatello, 7. [xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009. [xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4. [xxxvii] Michael S. Okun et al., “Deep Brain Stimulation in the Internal Capsule and Nucleus Accumbens Region: Responses Observed during Active and Sham Programming,” Journal of Neurology, Neurosurgery & Psychiatry 78, no. 3 (March 1, 2007): 310–14, https://doi.org/10.1136/jnnp.2006.095315. (shrink)

In lieu of an abstract, here is a brief excerpt of the content: Reviewed by: The Myth or Elegy of Artificial Intelligence by Tingyang ZhaoXuejian Zhou (bio)Rengongzhineng de shenhua huo beige 人工智能的神話或悲歌 ( The Myth or Elegy of Artificial Intelligence). By Tingyang Zhao 趙汀陽. Beijing: The Commercial Press, 2022. Pp. 155. Hardcover RMB68, isbn 978-981-16-7749-6. In recent years, the philosophy of artificial intelligence has undoubtedly become one of the most popular topics. There is a vague viewpoint suggesting (...) that Chinese philosophy might face challenges in addressing the philosophical issues posed by artificial intelligence. However, compared to the past, when discussing Chinese philosophy today, we must acknowledge the significant impact of modern technology. While the contemporary world does indeed witness clashes of civilizations, individuals from various civilizations share a common predicament: their close entanglement with modern technology. Regarding the future of Chinese philosophy, it becomes crucial to direct our attention towards modern advancements like artificial intelligence and the diverse range of issues they introduce. In light of this context, Zhao Tingyang's 趙汀陽 new book, The Myth or Elegy of Artificial Intelligence, presents us with a fresh perspective. In this book, Zhao primarily delves into the philosophical issues surrounding Super Artificial Intelligence, a subject regarded as both a metaphysical and a political problem. This notion refers to artificial intelligence that surpasses general artificial intelligence in terms of functionality, and possesses self-consciousness. Therefore, "myth" refers to the myth of the emergence of Super Artificial Intelligence disrupting people's subjectivity, giving rise to a new myth of artificial intelligence. Conversely, the term "elegy" embodies the crisis in human destiny that stems from such profound changes, which will lead to an ontological revolution in human existence. This suggests that within the narrative of artificial intelligence's myth, there lies the elegy of humanity (p. 4). The intricate interplay between these two realms of myth and elegy vividly showcases the value of Chinese philosophy. The book consists of a total of ten chapters. In Chapter 1, Zhao initially presents a thought-provoking question. With the rapid advancement of artificial intelligence humanity is faced with a novel ontological event, where the existence of artificial intelligence could potentially jeopardize human existence. This presents an ontological problem of "de-existence" (p. 8). As current artificial intelligence remains within the domain of the Turing machine concept and lacks subjectivity, it cannot effectively engage in matters involving self-correlation or [End Page 1] infiniteness. However, if artificial intelligence evolves into a super Turing machine endowed with subjectivity and free will, it could gain the capacity to modify its own programs and societal norms based on its needs or maximal benefits. This scenario introduces the possibility of a crisis in human existence, where the very existence of humanity is put at risk (pp. 17-23). In Chapter 2, Zhao analyzes the safety conditions of artificial intelligence and proposes the need to establish self-destruct mechanisms for Super Artificial Intelligence to ensure the status of human existence (pp. 34-38). Interestingly, in this context, Zhao connects artificial intelligence with the concept of Tianxia 天下 ("all under heaven"), implying that in order to control capital and power, the world needs a new form of politics, namely, a Tianxia 天下 system. Tianxia 天下 represents a world system that ensures world peace and the shared interests of people in a multicultural context, a non-exclusive system defining the world beyond any particular entity. According to Zhao, only a Tianxia 天下 system can manage the technological risks of the world (p. 39). Theoretically (hopefully in practice as well), a significant application of the Tianxia 天下 system would be to use global authority to limit any high-risk behaviors. Based on the first two chapters, it becomes apparent that the dangers related to artificial intelligence do not lie in its current capabilities, but in its potential for self-consciousness. For this reason, in Chapter 3, Zhao analyzes this issue further. In his view, once artificial intelligence possesses the ability for self-reflection, it will become possible for it to transform its own system and create new rules. Especially if artificial intelligence invents a universal language of its own, equivalent to human natural language, then all program systems could be reinterpreted, reconstructed, and redefined using this universal language (pp. 47-52). Furthermore, if... (shrink)

The advancement of Artificial Intelligence (AI) poses challenges in the field of bioethics, especially concerning issues related to life and death. AI has permeated areas such as health and research, generating ethical dilemmas and questions about privacy, decision-making, and access to technology. Life and death have been recurring human concerns, particularly in connection with depression. AI has created systems like Thanabots or Deadbots, which digitally recreate deceased individuals and allow interactions with them. These systems rely on information generated by (...) AI users during their lifetime, raising ethical and emotional questions about the authenticity and purpose of these recreations. AI acts as a mediator between life, death, and the human being, enabling a new form of communication with the deceased. However, this raises ethical issues such as informed consent from users and the limits of digital recreation. Companies offer services like the Digital Resurrection of deceased individuals and the generation of hyper-realistic avatars. Still, concerns arise about the authenticity of these representations and their long-term emotional impact. Interaction with Thanabots may alter perceptions of death and finitude, leading to a potential “postmortal society” where death is no longer viewed as a definitive end. Nevertheless, this raises questions about the value of life and the authenticity of human experiences. AI becomes a bridge between the living and the dead, partially replacing rituals and mystical beliefs. As technology advances, there will be a need for greater transparency in interacting with AI systems and ethical reflections on the role of these technologies in shaping perceptions of life and death. Ultimately, the question arises of whether we should allow the dead to rest in peace and how to balance the pursuit of emotional relief with authenticity and respect for the memory of the deceased. A deeper ethical consideration is needed on how AI alters traditional notions of life, death, and communication in contemporary society. In this research, an interdisciplinary approach was utilized to conduct a comprehensive systematic review of the recent academic literature, followed by a detailed analysis of two key texts. Central ideas were extracted, and recurring themes were identified. Finally, a reflective analysis of the findings was conducted, yielding significant conclusions and recommendations for future research. (shrink)

This paper presents an in-depth literature review on the driving forces and barriers for achieving operational excellence through artificial intelligence. Artificial intelligence is a technological concept spanning operational management, philosophy, humanities, statistics, mathematics, computer sciences, and social sciences. AI refers to machines mimicking human behavior in terms of cognitive functions. The evolution of new technological procedures and advancements in producing intelligence for machines creates a positive impact on decisions, operations, strategies, and management incorporated in the production process of (...) goods and services. Businesses develop various methods and solutions to extract meaningful information, such as big data, automatic production capabilities, and systematization for business improvement. The progress in organizational competitiveness is apparent through improvements in firm’s decisions, resulting in increased operational efficiencies. Innovation with AI has enabled small businesses to reduce operating expenses and increase revenues. The focused literature review reveals the driving forces for achieving operational excellence through AI are improvement in computing abilities of machines, development of data-based AI, advancements in deep learning, cloud computing, data management, and integration of AI in operations. The barriers are mainly cultural constraints, fear of the unknown, lack of employee skills, and strategic planning for adopting AI. The current paper presents an analysis of articles focused on AI adoption in production and operations. We selected articles published between 2015 and 2020. Our study contributes to the literature reviews on operational excellence, artificial intelligence, driving forces for AI, and AI barriers in achieving operational excellence. (shrink)

The paper notes the recent spread of business ethics courses in American higher education, observing that teachers trained in economics have not readily incorporated ethical notions or theory into regular courses, such as finance, management, accounting, and marketing. The presumed ethically neutral, value-free approach of economists, who dominate business courses, is increasingly inadequate to meet the needs of business managers – or of business students. Technological and political changes, creating an interdependent environment within which managers operate, have eroded older ethics (...) based on tradition and common backgrounds. They have also raised ethical issues of new orders of complexity. With corporate business managers finding ethical concerns more pressing matters than do many teachers, the paper offers some tentative answers to three questions about how to interest business students in ethical issues: What Approach to Business Ethics Gets student's Attention? What Is the Value of Simulations and Games? What Can Be Said About the Business System And Its Values?The answer to the first question is simulations and games. Case method analysis is serviceable, engaging students' intellect, but all too often without emotional involvement or self-revelation. Experiential learning through class-room games accomplish both engagement and involvement in ways that are exceedingly helpful to business students, who have had "less occasion for critical reflection on self and world than have others of their age.". (shrink)

Gardner’s theory of multiple intelligences has been further developed to focus on the research of human cognitive activities. Thus, the concept of emotional intelligence, which is the topic of the current paper, was introduced by John D. Mayer, Peter Salovey and ‎Daniel Goleman. General intelligence can be defined as the capacity to carry out abstract reasoning to understand meanings, to recognize the similarities and differences between two concepts and to make generalizations. Emotional intelligence is not a part of general intelligence. (...) Emotional intelligence can be defined as an ability of a human to perceive oneself and interact with others with the help of obtained and processed emotional information. Language acquisition is mediated by the necessity to communicate with others. Consequently, the ability to manage the process of communication is of utmost importance in learning a language. Virtual learning environment reduces dramatically the immediate interaction of the participants of the process of education. It undoubtedly affects the process of acquisition and demands to reconsider the distribution of different learning activities. (shrink)

Artificial intelligence era challenges the use and functions of emotion in college students and the students’ college life is often experienced as an emotional rollercoaster, negative and positive emotion can affect the emotional outcomes, but we know very little about how students can ride it most effectively to increase their creativity. We introduce frustration tolerance as a mediator and emotion regulation as a moderator to investigate the mechanism of creativity improvement under negative emotion. Drawing on (...) a sample of 283 students from professional music colleges or music major in normal universities, we find that negative emotion are generally associated with a lower creativity, while frustration tolerance can mediate the relationship between negative emotion and creativity, but these effects depend on the emotion regulation. Cognitive reappraisal exerts a negative effect on the relationship between negative emotion and creativity, while expressive suppression has the opposite effect. Our study contributes to the literatures on student’s emotions and creativity in music education and to the emotion regulation literature. (shrink)

This article questions the effects on urban research dynamics of the Big Data and AI turn in urban management. Increasing access to large datasets collected in real time could make certain mathematical models developed in research fields related to the management of urban systems obsolete. These ongoing evolutions are the subject of numerous works whose main angle of reflection is the future of cities rather than the transformations at work in the academic field. Our article proposes grasp the scientific dynamics (...) in areas of research related to two urban systems: transportation and water. The article demonstrates the importance of grasping these dynamics if we want to be able to apprehend what the urban management of tomorrow's cities will be like. To analyse these research areas’ dynamics, we use two complementary materials: bibliometric data and interviews. The interviews conducted in 2018 with academics and higher education officials in Paris and Edinburgh suggest avenues for hybridization between traditional modelling approaches and research in machine learning, artificial intelligence and Big Data. The bibliometric analysis highlight the trends at work: it shows that traffic flow as well as transportation studies are focussing more and more on AI and Big Data and that traffic flow studies are arousing a growing interest among computer scientists, while, so far, this interest is less pronounced in the water research area, and more especially regarding water quality. The differences observed between research on transportation and that on water confirm the multifaceted nature of the developments at work and encourage us to reject overly hasty and simplistic generalisations about the transformations underway. (shrink)

This groundbreaking proceedings volume explores the integration of Artificial Intelligence (AI) across key domains—healthcare, finance, education, robotics, industrial and other engineering applications —unveiling its transformative potential and practical implications. With a multidisciplinary lens, it transcends technical aspects, fostering a comprehensive understanding while bridging theory and practice. Approaching the subject matter with depth, the book combines theoretical foundations with real-world case studies, empowering researchers, professionals, and enthusiasts with the knowledge and tools to effectively harness AI. Encompassing diverse AI topics—machine learning, (...) natural language processing, computer vision, data analytics and supervisory control — the volume showcases state-of-the-art techniques propelling AI advancements. Structured into four parts: Part 1: Artificial Intelligence (AI), explores evolving deep neural networks, reinforcement learning, and explainable AI, providing a deep dive into the technical foundations of AI advancements. Part 2: Robotics and Control Systems, delves into the integration of AI in robotics and automatic control, addressing supervisory control, automated robotic movement coordination, anomaly detection, dynamic programming, and fault tolerance, offering insights into the evolving landscape of intelligent automation. Part 3: AI and Society, examines the societal impact of AI through chapters on ethical considerations, economic growth, environmental engagements, and hazard management, providing a holistic perspective on AI's role in shaping society. Part 4: PhD Symposium, presents the future of AI through cutting-edge research, covering legal and ethical dimensions, privacy considerations, and computationally efficient solutions, offering a glimpse into the next generation of AI advancements. Catering to a diverse audience—from industry leaders to students—the volume consolidates the expertise of renowned professionals, serving as a comprehensive resource for navigating the ever-evolving AI landscape. An essential reference for those staying at the forefront of AI developments. (shrink)