Reimagining Descartes’ philosophy of autodidacticism in the postdigital education

As artificial intelligence (AI) is becoming more integrated into the field of healthcare, medical students need to learn foundational AI literacy. Yet, traditional, descriptive teaching methods of AI topics are often ineffective in engaging the learners. This article introduces a new application of cinema to teaching AI concepts in medical education. With meticulously chosen movie clips from "Enthiran (Tamil)/Robot (Hindi)/Robo (Telugu)" movie, the students were introduced to the primary differences between artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI). This method triggered encouraging responses from students, with learners indicating greater conceptual clarity and heightened interest. Film as an emotive and visual medium not only makes difficult concepts easy to understand but also encourages curiosity, ethical consideration, and higher order thought. This pedagogic intervention demonstrates how narrative-based learning can make abstract AI systems more relatable and clinically relevant for future physicians. Beyond technical content, the method can offer opportunities to cultivate critical engagement with ethical and practical dimensions of AI in healthcare. Integrating film into AI instruction could bridge the gap between theoretical knowledge and clinical application, offering a compelling pathway to enrich medical education in a rapidly evolving digital age.NEW & NOTEWORTHY This article introduces a new learning strategy that employs film to instruct artificial intelligence (AI) principles in medical education. By introducing clips the from "Enthiran (Tamil)/Robot (Hindi)/Robo (Telugu)" movie to clarify artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI), the approach converted passive learning into an emotionally evocative and intellectually stimulating experience. Students experienced enhanced comprehension and increased interest in artificial intelligence. This narrative-driven, visually oriented process promises to incorporate technical and ethical AI literacy into medical curricula with enduring relevance and impact.

Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review

Editorial

This chapter critically examines the relationship between increased artificial intelligence (AI) use and the decline of reflective thinking in contemporary society. Drawing on philosophical and cognitive theories, it explores how the acceleration of information production through AI tools influences human behavior, decision-making, and knowledge construction. The discussion emphasizes the tension between efficiency and depth, questioning whether reliance on AI hampers critical reflection and intellectual autonomy. It investigates the implications of these shifts within educational contexts, considering how emerging technologies may alter pedagogical practices, ethical standards, and cognitive engagement. The chapter highlights the importance of fostering awareness about the potential consequences of pervasive AI integration, advocating for strategies that balance technological advancement with the preservation of reflective capacities. By analyzing the societal and educational impacts of AI-driven transformation, the chapter provides insights into the evolving nature of human action in an era increasingly mediated by intelligent systems.

This chapter explores the complex relationship between artificial intelligence (AI) and critical thinking skills in education. It examines how AI is reshaping learning and decision-making, while emphasizing critical thinking skills as essential for navigating these changes. AI offers opportunities through personalized learning and immersive scenarios, but faces limitations like algorithmic opacity and data bias that require ethical integration into education. The chapter addresses key areas: critical thinking's role in the AI era, AI-critical thinking interactions, challenges posed by AI, and strategies for developing critical thinking skills. It focuses on pedagogical approaches fostering critical reflection and intellectual autonomy. The text advocates for a systemic approach where AI complements human cognitive capacities rather than replacing them. Through synergy between critical thinking and AI, educators can prepare learners for 21st-century challenges with discernment and responsibility.

Purpose This study aims to develop a theoretical framework that marketing practitioners and scholars can adopt to enhance their understanding of how firms can effectively deploy and use digital human avatars as part of their global digital marketing strategy. By doing so, we inform investors of ongoing digital transformations of marketing practices that will equip marketeers to provide scalable, tailored, reliable and relevant digital self-service interactions to users, consequently improving the user/customer experience. Design/methodology/approach Thematic analysis was used to discover factors to enable the successful implementation of digital human avatars, drawing on in-depth interviews with fourteen executives of digital human avatars developer companies worldwide and analysis of ten podcasts and webinars with artificial intelligence (AI) experts. Findings Digital human avatars revitalise the international dynamic marketing capabilities (IDMCs) of firms by integrating advanced technologies that transform user interactions, improve engagement and facilitate knowledge acquisition, dissemination and usage across various sectors and business units globally. This integration promotes a dynamic approach to international brands, customer relationships and marketing knowledge management capabilities, offering profound value to users and firms. Research limitations/implications Our first limitation is a lack of diversity in data sources. As digital human avatars are an emerging field, we had to limit our study to 14 experts in AI and 10 podcasts. While this method provides deep insights into the perspectives of those directly involved in the development and implementation of digital human avatars, it may not capture the views of end-users or consumers who interact with these avatars, which can be an avenue for further research. Our second limitation is the potential bias in the interpretation of our interview data and podcasts. This study’s approach to data analysis, where themes are derived from the data itself, carries a risk of subjective interpretation by the researchers. Future studies are encouraged to investigate the impact of digital human avatars across different organisational contexts and ecosystems, especially focusing on how these technologies are integrated and perceived in various international markets. Practical implications The novel framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies. Social implications The adoption of digital human avatars can alleviate loneliest elderly and vulnerable people by being a companion. The human-like characteristics can impact sense of presence and attachment. Originality/value The novelty of our study lies in exploring the characteristics of technologies and practical factors that maximise the successful adoption of digital human avatars. We advance and contribute to the emerging theory of avatar marketing, IDMCs and absorptive capacity by demonstrating how digital human avatars could be adopted as part of a firm’s global digital marketing strategy. We focus specifically on six dimensions: outcomes and benefits, enhancements and capabilities, applications and domains, future implications, foundational elements and challenges and considerations. This framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.

Purpose The purpose of this paper is to introduce the artificial intelligence (AI) Citizenship Framework, a model that equips teachers and school library professionals with the tools to develop AI literacy and citizenship in students. As AI becomes increasingly prevalent, it is essential to prepare students for an AI-driven future. The framework aims to foster foundational knowledge of AI, critical thinking and ethical decision-making, empowering students to engage responsibly with AI technologies. By providing a structured approach to AI literacy, the framework helps educators integrate AI concepts into their lessons, ensuring students develop the skills needed to navigate and contribute to an AI-driven society. Design/methodology/approach This paper presents a theoretical framework, developed from the author’s experience as an information and digital literacy coach and teacher librarian across Asia, the Middle East and Europe. The AI Citizenship Framework was created without following specific empirical methodologies, drawing instead on practical insights and educational needs observed in diverse contexts. It outlines a scope and sequence for integrating AI literacy into school curricula. The framework’s components build on existing pedagogical practices while emphasising critical, ethical and responsible AI engagement. By providing a structure for AI education, it serves as a practical resource for school librarians and educators. Findings While no empirical data was collected for this theoretical paper, the AI Citizenship Framework offers a structured approach for school librarians and educators to introduce and develop AI literacy. It has the potential to influence AI education by fostering critical and ethical awareness among students, empowering them to participate responsibly in an AI-driven world. The framework’s practical application can be expanded beyond school librarians to include classroom teachers, offering a comprehensive model adaptable to various educational settings. Its real-world implementation could enhance students’ readiness to engage with AI technologies, providing long-term benefits for both educational institutions and the broader society. Research limitations/implications One limitation of the AI Citizenship Framework is that it has not yet been empirically validated. Future research could focus on testing its practical effectiveness in real-world settings, offering insights that may inform refinements and adaptations to better support school librarians and educators in fostering AI literacy and AI citizenship. Practical implications The practical implication of the AI Citizenship Framework is its application in educational settings to equip students with AI literacy and responsible citizenship skills. School library professionals and teachers can use the framework to integrate AI concepts into curricula, fostering critical thinking, ethical understanding and informed decision-making about AI technologies. The framework provides ready-to-use curriculum plans, enabling educators to prepare students for an AI-driven world. Its adaptability also allows classroom teachers to lead AI literacy initiatives, making it a versatile tool for embedding AI education across subjects and promoting responsible use and engagement with AI technologies in real-world contexts. Originality/value The originality and value of the AI Citizenship Framework lie in its approach to integrate AI literacy into educational contexts, specifically tailored for teacher librarians and school librarians. To the best of the authors’ knowledge, it is the first framework that comprehensively addresses the need for AI literacy from an ethical, critical and societal perspective, while also promoting active participation and leadership in AI governance. The framework equips educators with practical tools and curriculum plans, fostering responsible AI use and engagement. Its adaptable structure ensures it can be implemented by classroom teachers as well, adding significant value to AI education across disciplines and age groups.

This article critically examines the impacts of artificial intelligence (AI) on education, highlighting how algorithmic mediation can compromise students’ intellectual autonomy and critical thinking. The analysis reveals that adaptive platforms, automated assessment systems, and generative tools, while promising efficiency and personalization, often reduce learning to standardized processes, limiting the capacity for autonomous judgment and the construction of meaningful knowledge. The erosion of autonomy manifests itself in student passivity induced by predefined learning paths, while dependence on generative AI atrophies original argumentation. Furthermore, algorithms reproduce cultural biases and prioritize quantifiable metrics over qualitative dimensions of education. As alternatives, we propose active teacher mediation, where the teacher acts as a critical filter of algorithmic content, and hybrid models that preserve student agency. We also defend the need for ethical regulation, with transparency in algorithmic criteria and protection of educational data. The paper concludes that AI in education requires a delicate balance: if adopted uncritically, it can reinforce inequalities and impoverish human development; if integrated with solid pedagogical foundations, it can broaden access without sacrificing intellectual depth. Future research should investigate the long-term cognitive effects and develop truly inclusive systems.

The growing use of Artificial Intelligence (AI) technologies in educational settings has prompted apprehensions over the decline of critical thinking, academic integrity, and authentic student involvement. Although AI provides assistance in research and writing, unthinking and excessive dependency on these technologies has resulted in shallow learning and a decline in intellectual autonomy. This paper examines the significance of reinstalling conventional sit-in exams as a remedy for the excessive reliance on AI in higher education, fostering genuine evaluation and academic integrity. A systematic review according to PRISMA guidelines was performed to guarantee a meticulous and transparent procedure of data gathering and analysis. Relevant literature was located using an extensive search across five major electronic databases such as Google Scholar, ACM Digital Library, IEEE Xplore, Scopus, and Web of Science, utilizing Boolean operators and topic-specific keywords. The review included peer-reviewed journal articles, conference proceedings, and book chapters published in English from 2018 to 2025. Inclusion and exclusion criteria were used to narrow the findings, culminating in the final selection of 252 empirical and theoretical sources for comprehensive examination. Data extraction used a standardised form, and theme synthesis was executed to discern predominant patterns and findings. The finding evaluation disclosed a prevalent and escalating apprehension about students' augmented utilisation of AI tools for academic tasks, often to the detriment of cultivating autonomous analytical and writing abilities. Conventional sit-in examinations were continuously emphasised in the literature as an effective means to assess authentic student knowledge and prevent academic misconduct. The results highlighted the insufficiency of current online and take-home evaluation methods in identifying AI-generated content. Identified themes included the influence of AI on learning behaviour, the constraints of existing digital assessments, and the resurgence of proctored in-person exams as a quality assurance measure. The integration of evidence indicates that conventional sit-in examinations are essential for maintaining academic standards. In-person examinations promote self-discipline, enhance engagement with course material, and reduce the potential for technological misconduct, unlike remote or take-home evaluations. Although AI may assist in certain facets of learning, it cannot replace the evaluative significance of conventional evaluations that measure cognitive recall, synthesis, and application. The research underscores the need for hybrid assessment frameworks that integrate AI-assisted formative instruments with summative in-person assessments to protect educational integrity. In conclusion, the systematic evaluation determines that reintroducing conventional sit-in examinations is a timely and effective measure against the over reliance on AI in academia. By emphasising academic rigour and personal responsibility, these examinations guarantee that learning results accurately represent genuine student skills. Institutions must implement equitable policies that include technological innovation while preserving the core objectives of higher education—specifically, critical thinking, academic integrity, and intellectual development.

The emergence of artificial intelligence (AI) has transformed higher education, creating both opportunities and challenges in cultivating students’ critical thinking skills. This study integrates quantitative bibliometric analysis and qualitative systematic literature review (SLR) to map global research trends and identify how critical thinking is conceptualized, constructed, and developed in the AI era. Scopus served as the primary data source, limited to publications from 2022 to 2024, retrieved on February 8, 2025. Bibliometric analysis using Biblioshiny R and VOSviewer followed five stages—design, data collection, analysis, visualization, and interpretation—while the SLR employed a deductive thematic approach consistent with PRISMA guidelines. A total of 322 documents were analyzed bibliometrically, and 34 were included in the qualitative synthesis. Results show that Education Sciences and Cogent Education are the most productive journals, whereas Education and Information Technologies have the highest citation impact. Several influential documents and authors have shaped global discussions on AI adoption in higher education and its relationship to critical thinking. Thematic mapping identified five major research clusters: pedagogical integration, ethical and evaluative practices, technical and application-oriented AI models, institutional accountability, and socio-technical systems thinking. Conceptually, critical thinking is understood as a reflective, evaluative, and metacognitive reasoning process grounded in intellectual autonomy and ethical judgment. Across the reviewed literature, strategies for fostering critical thinking converge into three integrated approaches: ethical curriculum integration, pedagogical and assessment redesign, and reflective human–AI collaboration. Collectively, these strategies ensure that AI strengthens rather than replaces human reasoning in higher education.

This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and a speculative AI 4.0 (Conscious AI). Each AI generation is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 accompanied breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. AI 2.0 is built on these foundations through real-time decision-making in digital environments, leveraging reinforcement learning and adaptive planning for agentic AI applications. AI 3.0 extended intelligence into physical contexts, integrating robotics, autonomous vehicles, and sensor-fused control systems to act in uncertain real-world settings. Building on these developments, the proposed AI 4.0 puts forward the bold vision of self-directed AI capable of setting its own goals, orchestrating complex training regimens, and possibly exhibiting elements of machine consciousness. This paper traces the historical foundations of AI across roughly 70 years, mapping how changes in technological bottlenecks from algorithmic innovation to high-performance computing to specialized data have stimulated each generational leap. It further highlights the ongoing synergies among AI 1.0, 2.0, 3.0, and 4.0, and explores the ethical, regulatory, and philosophical challenges that arise when artificial systems approach (or aspire to) human-like autonomy. Ultimately, understanding these evolutions and their interdependencies is pivotal for guiding future research, crafting responsible governance, and ensuring that AI’s transformative potential benefits society.

Problem statement. We live in an era of rapid technological development. Technologies capable of processing a lot of information. Therefore, there is a problem of improving information systems that allow processing information using modern computer technologies. Technologies capable of reproducing the thought processes of the human brain and directing them to the creation and processing of various computer programs, as well as intelligent machines that will completely replace and simplify human work. Namely, the application of artificial intelligence technologies. The purpose of the article is to determine the prerequisites for the emergence of artificial intelligence technologies, consider the advantages and debatable issues regarding the areas of application and limitations of artificial intelligence technologies in everyday human life. Conclusions. Artificial intelligence is one of the most important branches of modern technology. This industry is very young, but the pace of its development is striking every year. It is an integral part of the development of technology in the future and is able to open up new opportunities in many areas of society. Artificial intelligence technologies greatly simplify human work and lead to the development of human activity. The main thing is that technology does not completely replace a person, but only helps to perform complex functions. So, humanity has encountered a technology that radically changes the world. Artificial intelligence gives a person additional opportunity, and makes him more knowledgeable and responsible. As Nick Bostrom noted in his book “Artificial Intelligence. Stages. Threats Strategies” − about the inevitability of the development of both artificial and human intelligence. But in what direction will this development be directed. Will there be a balance between benefits and risks, or is it a leap into an unknown future. However, we must remember that today we are at the beginning of what artificial intelligence can achieve.

Recently, there is a lot of discussion in the architectural world about the impact that artificial intelligence image generation models will have on architectural design. Artificial intelligence uses information processing theory to analyze and model human cognitive abilities. According to information processing theory, architectural design is an information processing activity that establishes reasonable concepts to solve spatial problems and processes visual and conceptual information that corresponds to them. In this study, the five information processing processes of Akin's (1986) information processing model, DIPS (design information processing system), 'Information acquisition', 'Information representation', 'Information projection', 'Information confirmation', and 'Regulation of control'. The characteristics of architectural design thinking types, ‘convergent thinking’, ‘divergent thinking’, and ‘analogical thinking’ were examined. Through this, I identified the characteristics of representative design-related artificial intelligence NLP, GAN, and DNN and proposed an architectural design information processing model using artificial intelligence. The purpose of this paper is to examine the architectural design information processing model and compare the characteristics of the thinking style that occurs in the process with the characteristics of the artificial intelligence model to analyze the possibility of using artificial intelligence in architectural design and to provide architectural design information using artificial intelligence. The goal is to propose a processing model.

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable