AI-enhanced gamification in education: an integrative review of trends, impacts, and corrective role potential

The dawn of artificial intelligence (AI) signifies a pivotal shift in medical and dental education. Integrating AI into the curriculum modernizes learning and equips future healthcare professionals with crucial tools for the 21st century. The COVID-19 pandemic revealed the limitations of conventional educational models, necessitating rapid adaptation to remote and online learning environments. This disruption expedited the transition to digital platforms, laying the foundation for further integration of technology, including AI, into medical education. What began as an emergency response has now become a permanent feature of the educational landscape, evolving from static textbooks to dynamic digital platforms that offer greater accessibility, inclusivity, and personalization of learning experiences.1 In the AI era, it is insufficient to merely digitize the curriculum; a comprehensive transformation is essential. The digital curriculum opens new avenues for interactive learning environments, simulation-based practices, and adaptive learning algorithms that respond to the individual needs of students. AI-driven tools such as virtual patient simulations, diagnostic decision-making platforms, and predictive analytics have the potential to revolutionize how medical students learn, practice, and apply their knowledge in clinical settings.2 These innovations allow for an enhanced learning experience where students can interact with realistic patient cases and make informed decisions, fostering a deeper understanding of clinical practice. One of the most promising applications of AI in medical education is its role as an educational partner. AI-powered platforms can function as personalized tutors, providing real-time feedback, adjusting learning modules based on student performance, and even predicting areas where additional support may be required.3 Adaptive learning systems can analyze the learner’s pace and comprehension, offering tailored resources to bridge knowledge gaps. This personalized approach to education ensures that no student is left behind, addressing one of the longstanding challenges of traditional, one-size-fits-all curricula. Additionally, AI can enhance clinical reasoning through simulation and data-driven case scenarios. By analyzing patterns in patient data, AI algorithms can help medical students gain deeper insights into complex clinical decision-making processes. This data-driven approach can significantly improve learners’ ability to diagnose and plan treatments, thereby improving clinical outcomes. While AI and digital tools offer substantial benefits, the role of educators remains essential in this new educational paradigm. Rather than replacing teachers, AI will augment their roles, allowing them to focus on mentorship, critical thinking, and the ethical dimensions of healthcare.4 Educators will need to reimagine their roles, becoming facilitators of learning who guide students in interpreting and applying AI-generated data in clinical settings. As AI takes on administrative tasks such as grading, educators can dedicate more time to meaningful interactions with students.5 However, this shift toward AI-driven curricula also requires significant investment in faculty development. Educators must be trained in the use of AI tools and possess a thorough understanding of their applications to ensure that AI is used responsibly and effectively in shaping future healthcare professionals. As AI becomes more integrated into medical education, addressing the ethical challenges associated with this technology becomes crucial. While AI-driven tools hold great promise, they must be designed and deployed with an acute awareness of biases, data privacy concerns, and the risk of over-reliance on algorithms in clinical decision-making.6 The digital curriculum must provide students with technical skills and a strong ethical foundation for AI use in healthcare. Students must be trained to critically evaluate AI outputs, understand their limitations, and ensure that human judgment remains central to patient care. Transforming medical curricula in the AI era is not without challenges. Digital divides, access to technology, and the initial cost of AI-driven platforms may pose barriers to widespread adoption. Institutions must ensure equitable access to resources for all students, regardless of their geographic or socioeconomic backgrounds. Moreover, regulatory bodies such as the Higher Education Commission (HEC) and the Pakistan Medical and Dental Council (PMDC) must revise standards to accommodate these technological advancements. In conclusion, the transformation of medical and dental curricula into a digital, AI-enhanced model represents not only a modernization of education but also a fundamental shift in preparing future healthcare professionals. By embracing AI as an educational partner, medical institutions can create personalized, data-driven learning environments that equip students with the skills and knowledge needed to thrive in an increasingly complex healthcare landscape. The integration of AI into the curriculum offers an opportunity to empower the next generation of doctors, enabling them to navigate future challenges with confidence and competence. Now is the time for this transformation, and it is a journey that we must embark on collectively to ensure the future of education, healthcare, and patient care.

ImportanceInterest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.ObservationsWhile AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people’s daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model’s behavior. Prompts such as “Write this note for a specialist consultant” and “Write this note for the patient’s mother” will produce markedly different content.Conclusions and RelevanceFoundation models and generative AI represent a major revolution in AI’s capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

This article addresses the pressing issue of integrating artificial intelligence into the training of future teachers within vocational education. Gamification, a relatively new didactic tool arising from artificial intelligence technologies, is increasingly utilized in higher education. In Russia, this technology is currently in a testing phase, necessitating comprehensive research into the advantages and limitations of gamification in the professional training of future educators. The primary goal of this research is to synthesize theoretical and methodological approaches to employing gamification in higher education for the preparation of future teachers. The study's objectives include summarizing key approaches to the use of AI in higher education and identifying the benefits and constraints of gamification in the vocational training of future educators. The research methodology encompasses a range of general scientific methods (analysis, synthesis, induction, deduction) alongside specialized methods (historiographic analysis of scientific literature, factual analysis, and comparative analysis). The theoretical foundations for the use of AI and gamification in the educational processes of pedagogical universities were examined. The study analyzed the principal approaches to integrating artificial intelligence and gamification in the subject training of future vocational education teachers. The advantages and disadvantages of these technologies within the educational framework of pedagogical universities were evaluated. The author concludes that the primary benefits of AI and gamification in higher education, and specifically in pedagogical universities, lie in their capacity to facilitate an individualized approach to the subject training of future vocational education teachers. Conversely, a significant drawback of gamification in university education is the insufficient exploration of the ethical implications of AI technologies in the vocational training of future teachers. The scientific novelty of this study rests in its advocacy for revising curricula to incorporate gamification elements as an effective strategy for professional teacher training.

We are writing this letter to congratulate you on the paper ‘Nurses’ perceptions of artificial intelligence (AI) integration into practice: An integrative review’ by Lora and Foran, recently published in your journal. In this paper the authors synthesise remarkable, valuable and challenging aspects of the new era of artificial intelligence (AI) in the clinical practice of nursing. AI has been introduced into the most diverse areas of knowledge, opening a vast range of possibilities. This integrative review makes a timely and relevant contribution to the growing body of literature on the intersection between nursing and AI. The authors show that the practice of nursing, especially in the perioperative environment, is no exception.

Purpose: The general objective of this study was to explore Intellectual Property Rights in the era of Artificial Intelligence. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to Intellectual Property Rights in the era of Artificial Intelligence. Preliminary empirical review revealed that the era of Artificial Intelligence (AI) has significantly transformed the landscape of Intellectual Property Rights (IPR), presenting both opportunities and challenges. It highlighted that traditional IP laws are increasingly inadequate to address the complexities introduced by AI-generated content, necessitating a rethinking of existing frameworks. The study emphasized the need for recognizing AI's role in the creation of new works and inventions and the importance of developing balanced approaches to protect both human and AI contributions. Ethical considerations, such as accountability, transparency, and fairness, were also deemed crucial in ensuring responsible AI use. Overall, the study called for a comprehensive and proactive approach to integrate AI into IPR, ensuring robust protections while fostering innovation. Unique Contribution to Theory, Practice and Policy: The Technological Determinism Theory, Innovation Diffusion Theory and Legal Realism Theory may be used to anchor future studies on Intellectual Property Rights in the era of Artificial Intelligence. The study recommended revising existing IP laws to explicitly include AI-generated content and inventions, clarifying criteria for authorship and inventorship. It suggested expanding theoretical frameworks to accommodate AI contributions, emphasizing the collaborative nature of human and AI creativity. Practical measures, such as enhanced cybersecurity and legal safeguards for AI-generated trade secrets, were advised. Policy-wise, the study advocated for international cooperation to harmonize IP laws concerning AI. Developing ethical guidelines for responsible AI use and implementing education programs to inform stakeholders about AI and IP implications were also recommended. These measures aimed to create a balanced IP framework supporting innovation while protecting the rights of all stakeholders.

The rise of artificial intelligence (AI) is transforming educational practices, particularly in assessment. While AI may support the students in idea generation and summarization of source materials, it also introduces challenges related to content validity, academic integrity, and the development of critical thinking skills. Educators need strategies to navigate these complexities and maintain rigorous, ethical assessments that promote higher order cognitive skills. This article provides practical guidance for educators on designing take-home assessments (e.g. research-based assignments) in the AI era. This guidance was developed through a collaborative, consensus-driven process involving a consortium of three educators with diverse academic backgrounds, career stages, and perspectives on AI in education. Members, holding experience in higher education across the United Kingdom, United States of America, Australia, and Middle East and North Africa regions, brought varied insights into AI's role in education. The team engaged in an iterative process of refining recommendations through biweekly virtual meetings and offline discussions. Four key recommendations are presented 1) codeveloping AI literacy among students and educators, 2) designing assessments that prioritize process over output, 3) validating learning through AI-free assessments, and 4) preparing students for AI-enhanced workplaces by developing AI communication skills and promoting human-AI collaboration. These strategies emphasize ethical AI use, personalized feedback, and creativity. By adopting these approaches, educators can balance the benefits and risks of AI in assessments, fostering authentic learning while preparing students for the challenges of an AI-driven world.NEW & NOTEWORTHY This paper presents a framework to effectively design take-home assessments in the generative artificial intelligence (AI) era with four key recommendations to navigate the challenges and opportunities posed by generative AI. From codeveloping AI literacy to fostering human-AI collaboration, the strategies empower educators to promote authentic learning, critical thinking, and ethical AI use. Adaptable to various contexts, these insights help prepare students for an AI-driven future while maintaining academic rigor and integrity.

The advent of the artificial intelligence (AI) era presents significant challenges to traditional education, necessitating innovative approaches to teaching methods. Integrating artificial intelligence into classroom instruction to enhance course quality has become a pressing concern. This study explores the design of macroeconomics courses in the context of artificial intelligence, addressing the limitations of traditional teaching methods and highlighting the potential benefits of AI integration. These advantages include personalized learning experiences, increased teaching efficiency, enhanced interactivity, real-time feedback, and access to expanded teaching resources. Building on these insights, the research proposes a comprehensive framework for innovating macroeconomics courses design. First, it emphasizes content innovation, which involves integrating AI-related economic phenomena and frontier developments in the discipline. Second, it focuses on methodological innovation, leveraging AI-powered tools to facilitate teaching and incorporating practical, hands-on learning experiences. Finally, it advocates for assessment reform, emphasizing process-oriented evaluations and introducing diverse, multi-dimensional assessment methods. These innovative strategies are shown to enhance the quality of macroeconomics education, improve student learning outcomes, and better align educational programs with the demands of a rapidly evolving economy in the AI era.

Posthuman theorisation provides us with the conceptual tools to analyse and to understand how sustainable learning environments (SuLE) are created through adaptive learning (AL) as a form of artificial intelligence (AI) and as an aspect of a broader collective of relationalities. In this study our focus is on how pregnant teenagers relate to the curriculum, one another, other learners, parents, teachers, schools, communities, and non-human and more-than-human entities as they learn. Their condition currently makes them vulnerable and places them in less powerful positions to influence their learning in ways that align with their abilities and modes of being. The Posthumanist lens assists moves away from socialised gender, racial or generally underclass categories and dispositions. It enables us to situate pregnant teenagers’ feminine subjectivities beyond Humanism’s representations of this demographic as bearing-stigma, facing exclusion and marginalisation. This mode of seeing enables the possibility of re/imagining the pregnant teenager’s experiences through modes of being in which participation in networks and collaborations through adaptive learning, among others, draw on pedagogic technologies of change. We argue for a dissolving of Humanist barriers that define, stigmatise and burden the pregnant teenagers as they are fully integrated in their relationalities as learners in AI learning networks. Access to AL and similar software and gadgets need to be massified and opened up for use by all, irrespective of gender, socio-economic status, religion or any marginalising marker.

The advent of the era of artificial intelligence makes it possible for administrative subjects to use intelligent machines and systems to engage in administrative activities. Among them, the administrative discretion, which is the core of administrative law, is particularly concerned about the use of artificial intelligence. In the era of weak artificial intelligence, intelligent administrative discretion has been widely used in all aspects of administrative law enforcement, but there is a phenomenon that administrative subjects are negligent in exercising discretion. Looking forward to the era of strong artificial intelligence, artificial intelligence machines or systems may have the ability and power to independently exercise administrative discretion, but they cannot become the real administrative discretion subject. Intelligent administrative discretion is conducive to administrative efficiency and guarantees the fairness of administrative behavior, but it also faces legal risks such as unfair results of discretion, opaque algorithm settings, and weakening of government functions. Only by strengthening the legal basis, protecting the rights of the counterparty, improving the accuracy of the algorithm, and improving the status of the administrative subject can the administrative discretionary behavior under the background of artificial intelligence be effectively regulated.

Focus issue: Artificial intelligence in medical physics.

The social sciences have long relied on comparative work as the foundation upon which we understand the complexities of human behavior and society. However, as we go deeper into the era of artificial intelligence (AI), it becomes imperative to move beyond mere comparison (e.g., how AI compares to humans across a range of tasks) to establish a visionary agenda for AI as collaborative partners in the pursuit of knowledge and scientific inquiry. This paper articulates an agenda that envisions AI models as the preeminent scientific collaborators. We advocate for the profound notion that our thinking should evolve to anticipate, and include, AI models as one of the most impactful tools in the social scientist's toolbox, offering assistance and collaboration with low-level tasks (e.g., analysis and interpretation of research findings) and high-level tasks (e.g., the discovery of new academic frontiers) alike. This transformation requires us to imagine AI's possible/probable roles in the research process. We defend the inevitable benefits of AI as knowledge generators and research collaborators—agents who facilitate the scientific journey, aiming to make complex human issues more tractable and comprehensible. We foresee AI tools acting as co-researchers, contributing to research proposals and driving breakthrough discoveries. Ethical considerations are paramount, encompassing democratizing access to AI tools, fostering interdisciplinary collaborations, ensuring transparency, fairness, and privacy in AI-driven research, and addressing limitations and biases in large language models. Embracing AI as collaborative partners will revolutionize the landscape of social sciences, enabling innovative, inclusive, and ethically sound research practices.

In the era of Industrial Revolution 4.0, technology, especially artificial intelligence (AI), has brought major changes to various aspects of life, including education. Social science education now faces challenges in maintaining moral and ethical values ​​amidst the rapid adoption of technology. This research aims to explore how values ​​and technology can go hand in hand in social science education in the AI ​​era. Using a qualitative approach and literature analysis, this research finds that AI has the potential to enrich social science learning through personalized learning and in-depth data analysis. However, the integration of these technologies also poses risks, such as algorithm bias and reduced human interaction. Therefore, it is important to maintain a balance between the application of technology and the cultivation of human values, with strategies that prioritize the development of critical and ethical thinking skills. It is hoped that this research can provide guidance for educators in integrating AI into the social science curriculum without ignoring essential humanist aspects. Keywords: Values, Technology, Balance, Social Science Education, Era of Artificial Intelligence

With the rapid development of artificial intelligence (AI) technology, its application in higher education has gradually shifted from traditional teaching aids to deeper levels of interactive learning and emotional connection support. AI can enhance teaching efficiency, personalized learning, and real-time feedback; however, in areas such as emotional communication and teacher-student interaction, AI still cannot fully replace the role of teachers. This study aims to explore the transformation of teacher-student relationships in the era of AI, analyze the impact of AI technology on teaching interaction, emotional support, and teacher-student trust, and propose strategies to address these challenges. The research findings indicate that while AI has significant advantages in improving educational efficiency, it has limitations in interpersonal emotional support and the transformation of the teacher’s role. To ensure the comprehensiveness and humanization of education, educators should strengthen emotional care and improve students’ emotional literacy in the use of AI, and implement transparent data management and privacy protection measures to enhance teacher-student trust. The study also suggests that by enhancing teacher-student trust, strengthening emotional support, and increasing transparency, educators can effectively address the challenges of teacher-student relationships in the AI era. This research provides theoretical support and practical guidance for the integration of AI technology with educational humanistic care, promoting more comprehensive, personalized, and humane educational development.

This article presents a philosophical study of the coexistence of humans and algorithms in the age of artificial intelligence, identifying the main directions of humanistic understanding of technological development. Algorithms and AI systems are beginning to determine not only the ways of communication, work, or consumption, but also the very logic of thinking, perception, and decision-making. In this context, a philosophical analysis of the coexistence of humans and algorithms becomes necessary for understanding the limits of human freedom, responsibility, and creativity. The relevance of the topic is due to the fact that modern civilization has entered a new phase of its development, an era of algorithmic thinking and artificial intelligence (AI), when digital systems are becoming not only a tool but also a factor in the formation of social, cultural, and cognitive reality. In an age when almost every human action is accompanied by algorithmic data processing, the question arises: does a person remain the subject of their own life, or are they gradually turning into an element of the information chain? The purpose of the article is to conduct a philosophical study of the coexistence of humans and algorithms in the era of artificial intelligence, as well as to identify the main directions of humanistic understanding of technological development. A refined philosophical definition of the concepts of “algorithm” and “AI era” is proposed; the role of digital humanism as the basis for harmonious interaction between humans and technologies is explored; conclusions are drawn regarding possible paths for the development of the coexistence of humans and algorithms in the era of artificial intelligence. The conclusions emphasize that the coexistence of humans and algorithms in the digital age should be understood as an open and dynamic process, the outcome of which depends on society's ability to combine technological progress with humanistic content. Only if digital humanism becomes a thing in practice, culture, and politics can we make sure that tech helps people be more free, creative, and dignified. Thus, the coexistence of humans and algorithms in the digital age should be understood as an open and dynamic process, the outcome of which depends on society's ability to combine technological progress with humanistic content. Only with the establishment of digital humanism as a practice, culture, and policy can we ensure a future in which technology serves to expand human freedom, creativity, and dignity.

Artificial intelligence (AI) has breached creativity research. The advancements of creative AI systems dispute the common definitions of creativity that have traditionally focused on five elements: actor, process, outcome, domain, and space. Moreover, creative workers, such as scientists and artists, increasingly use AI in their creative processes, and the concept of co-creativity has emerged to describe blended human–AI creativity. These issues evoke the question of whether creativity requires redefinition in the era of AI. Currently, co-creativity is mostly studied within the framework of computer science in pre-organized laboratory settings. This study contributes from a human scientific perspective with 52 interviews of Finland-based computer scientists and new media artists who use AI in their work. The results suggest scientists and artists use similar elements to define creativity. However, the role of AI differs between the scientific and artistic creative processes. Scientists need AI to produce accurate and trustworthy outcomes, whereas artists use AI to explore and play. Unlike the scientists, some artists also considered their work with AI co-creative. We suggest that co-creativity can explain the contemporary creative processes in the era of AI and should be the focal point of future creativity research.