The implications of generative artificial intelligence for mathematics education

Generative AI (GenAI) is a disruptive technology likely to generate a major impact on faculty and learners in medical education. This work aims to measure the perception of GenAI among medical educators and to gain insights into its major advantages and concerns in medical education. A survey invitation was distributed to medical education faculty of colleges of allopathic and osteopathic medicine within a single university during the fall of 2023. The survey comprised 12 items, among those assessing the role of GenAI for students and educators, the need to modify teaching approaches, GenAI's perceived advantages, applications of GenAI in the educational context, and the concerns, challenges, and trustworthiness associated with GenAI. Responses were obtained from 48 faculty. They showed a positive attitude toward GenAI and disagreed on GenAI having a very negative effect on either the students' or faculty's educational experience. Eighty-five percent of our medical schools' faculty responded to had heard about GenAI, while 42% had not used it at all. Generating text (33%), automating repetitive tasks (19%), and creating multimedia content (17%) were some of the common utilizations of GenAI by school faculty. The majority agreed that GenAI is likely to change its role as an educator. A perceived advantage of GenAI in conducting more effective background research was reported by 54% of faculty. The greatest perceived strengths of GenAI were the ability to conduct more efficient research, task automation, and increased content accessibility. The faculty's major concerns were cheating in home assignments in assessment (97%), tendency for blunder and false information (95%), lack of context (86%), and removal of human interaction in important feedback processes (83%). The majority of the faculty agrees on the lack of guidelines for safe use of GenAI from both a governmental and an institutional policy. The main perceived challenges were cheating, the tendency of GenAI to make errors, and privacy concerns.The faculty recognized the potential impact of GenAI in medical education. Careful deliberation of the pros and cons of GenAI is needed for its effective integration into medical education. There is general agreement that plagiarism and lack of regulations are two major areas of concern. Consensus-based guidelines at the institutional and/or national level need to start to be implemented to govern the appropriate use of GenAI while maintaining ethics and transparency. Faculty responses reflect an optimistic and favorable outlook on GenAI's impact on student learning.

Claims that GPT-4 can outperform more than 90% of human test-takers in the US Uniform Bar Examination have sparked heated debates about the impact of Generative AI (GenAI) on legal education, academic integrity, and the future of legal practice. Yet GenAI’s capabilities in broader legal examination contexts – including in jurisdictions outside the US – are unclear. This study addresses this gap by evaluating GenAI’s performance against students who took the ‘Criminal Law’ final exam at an Australian law school in Spring 2023. Various AI models and prompt engineering techniques were used to generate 10 distinct answers to the exam question. Five criminal law tutors, unaware of AI involvement, graded a mix of AI-generated and student responses. Then, the tutors were briefed on the AI-generated papers they marked and engaged in reflective semi-structured interviews. The study found that GenAI performed below the student average in questions that required detailed legal and critical analysis. However, all GenAI papers performed better than students in open-ended questions and essay writing tasks. These results provide a benchmark for the capabilities and limitations of GenAI in higher education and provide insights into the potential implications of its application to legal assessments and education, curriculum development, and the future workforce.

ABSTRACTThe widespread applications of generative AI (GenAI) have sparked significant interest, with many organisations eager to leverage its transformative potential. Rather than focusing on individual organisations, this study examines GenAI integration within enterprise platforms, which are extensively adopted by many organisations and thus amplify both the benefits and risks of GenAI. We offer targeted recommendations for enterprise platform owners and their complementors, addressing challenges they face when integrating GenAI into these platforms. Drawing on a case study of Salesforce's experience, we recommend actions in three foundational areas – platform capability, architecture and governance – ensuring that our guidance is broadly applicable across enterprise platforms. In platform capability, we advise developing a unified GenAI stack built on existing platform services, offering generic and industry‐specific GenAI use cases to accelerate customer adoption and providing tools for customisation and creation of new use cases to enhance GenAI's transformational impact. For platform architecture, we recommend adding new layers for accommodating diverse GenAI foundation models and creating a trusted environment for secure data access, privacy and content monitoring. We also recommend implementing a prompt architecture to improve content relevance and accuracy. In platform governance, we recommend establishing new mechanisms to mitigate GenAI risks. Partnerships with GenAI providers and proactive investments in GenAI are essential to retain critical GenAI technologies. Personalised consultancy and training along with joint design and implementation with platform customers are also recommended. These combined actions, pursued in parallel across capability, architecture and governance, form a sustainable roadmap for GenAI integration in enterprise platforms.

PurposeMost recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation.Design/methodology/approachFindings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000.Findings1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovationResearch limitations/implicationsWe are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure.Practical implicationsBusiness application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments.Social implicationsTo alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial.Originality/valueThis research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain.

This research looks at the potential effects of generative artificial intelligence AI on the country's media landscape. Given their pervasiveness, it aims to reveal how AI-powered technologies in media content creation, distribution, and personalisation contribute to the overall process of national progress. Using well-designed questionnaires, the study quantitatively collects data from media professionals, techies, and communication scholars in large cities throughout China. Using statistical tools such as structural equation modelling and regression analysis, one investigated the interplay between the rate of modernisation, the effects of national development, and AI-driven media innovation. Media indices of generative AI demonstrate a clear positive correlation with the effect of modernism and national development programs. As China strives to digitally change its communication infrastructure and increase its cultural influence, technological prowess, and media production, generative AI is playing an increasingly crucial role. This study shows that AI in media may lead to more dynamic stories, practical audience participation, and worldwide outreach, all thanks to modernist techniques. There is no part of this that does not contribute to the advancement of national development goals. The results provide policymakers, media outlets, and AI developers with valuable information for formulating strategies to integrate AI with sustainable development objectives. Via an experimental interaction between generative AI and national development perceived via a modernist lens, this study provides a framework for future research on new media technologies and national change. The discussion of the societal potential presented by AI may now begin.

Generative AI (GenAI) use is increasing across society in many different industries. Despite widespread adoption in workplaces, there is little consensus on the scope of its benefits and challenges at the level of most industries. Universities are being called upon to equip graduates with important knowledge and skills using GenAI for their professional contexts. Higher education, however, faces issues in effectively and sustainability embedding a use of GenAI in the student experience, which requires adjustments to learning and teaching activities, assessment, and learning outcomes and in access to useful GenAI platforms relevant to the various disciplines. As pedagogical models, ethical debates, and technologies continue to develop in this space, university teachers’ experiences of teaching with GenAI have yet to be explored in detail. Adopting a phenomenographic perspective, this study examines university teachers’ conceptions, perceptions, and approaches to using GenAI in teaching. Leveraging semi-structured interviews with 30 teaching academics, variations of teaching using GenAI were identified. Quantitative analysis was also conducted to capture the associations between these variations. By exploring the qualitative differences between these variations, a nuanced and important contribution to the GenAI discussion from the understanding of university teachers is uncovered. The results show that some ways of understanding and teaching with GenAI are more likely to help students develop effective knowledge and skills for the workplace than others. The findings also offer education leaders evidence to help design effective support for teachers using GenAI to innovate in the student experience. Through investigating the university teacher experience of GenAI, this research adds to the growing debate on the GenAI enabled benefits and challenges that are set to shape the higher education sector.

BackgroundGenerative artificial intelligence (generative AI) has revolutionized the healthcare system for medical students and their educational process worldwide. Several studies assessed its efficacy in improving medical studies learning, and others assessed medical students’ knowledge and attitudes about generative AI. So, we aim to determine the knowledge, attitudes, and practices of generative AI among medical students in Egypt.MethodsIn 2024, a cross-sectional study using a semi-structured questionnaire was performed, targeting 423 students as a convenient sample from 10 selected Egyptian universities by simple random sampling. The questionnaire was distributed via online platforms such as WhatsApp, Facebook, X, Telegram, and Messenger. Data about sociodemographic characteristics, students’ general knowledge about generative AI and its limitations, and their attitudes and practices, respectively, were collected.ResultsAbout 2\\3 (61.5%) of the studied students had satisfactory knowledge about generative AI and (44.7%) had a positive attitude. Males demonstrated significantly higher knowledge than females (69.3% vs. 55%, P-value = 0.003). Knowledge level also differs significantly by university (P-value < 0.001), with Suez-Canal and 6th October university students achieving the highest satisfactory scores. Key significant predictors that affect knowledge level include gender, university and study phase according to binary logistic regression results. Furthermore, there is a positive correlation between satisfactory knowledge and Practice level (r = 0.303, P < 0.001). Finally, most students use generative AI to check Grammar and prepare their homework or assignments.Conclusion and recommendationsThere is moderate knowledge and positive attitude about AI that were reflected in medical students’ practice. However, knowledge about AI influenced by gender, university and study phase. Importantly, better knowledge significantly correlates with greater generative AI practice. Offering specialized courses or faculty modules about AI enhance awareness among Egyptian medical students. Such initiatives will enable them to gain knowledge and apply it effectively and wisely.

Generative AI, a subset of artificial intelligence, is dedicated to the development of models that are capable of producing new content, including text, imagery, audio, and entire virtual worlds. Generative AI is capable of replicating human creativity and generating outputs that were previously considered to be exclusively the domain of human intelligence by utilizing sophisticated machine learning techniques, including deep learning and neural networks. AI models in the music industry are capable of composing music in a variety of genres, providing musicians and producers with new tools. Generative AI has the potential to stimulate innovation and creativity in a variety of disciplines as technology continues to develop. This research article delves deep into the various aspects of Generative AI namely history and core concepts, privacy and security, types of Generative AI models and their applications, NLP models and finally on Responsible AI.

AI models, particularly generative AI and large language models, reshape digital information ecosystems by curating and amplifying content through user engagement metrics. Despite their capacity to reduce bias and promote inclusivity, these models simultaneously amplify cognitive biases, entrench filter bubbles, and spread misinformation. The intensification of human–machine interaction and hyper-industrialization complicates this further, as large language models increasingly mediate how information is produced and consumed. Socio-technical agency describes how AI systems co-construct human behavior and societal norms through their design, yet their effects remain understudied in regions with limited technological infrastructure. This paper investigates AI’s influence on information dissemination, cognitive biases, and user agency across digital media environments in key regions of the Global South. Drawing on qualitative interviews and a survey of 580 media technology users in South Africa, Indonesia, India, the Philippines, and Brazil it examines how generative AI affects emotional engagement, exposure to content, and perceptions of digital truth. Framed by media ecology theory, the study evaluates AI as a cognitive extension that can both reinforce and challenge digital biases. The study proposes strategies for using generative AI to support information integrity while addressing the risks of polarization and exclusion. By centering perspectives from regions in the Global South, it contributes to more equitable discourse on AI governance, advocating regulatory and design solutions responsive to diverse media ecologies.

This paper examines the implications of generative AI (GenAI) emulating human expression, i.e. human communication and human creative expression. While GenAI seems to offer benefits such as increased efficiency and productivity, its use raises significant practical and conceptual concerns: GenAI comes with the increased efforts of prompting, verification and editing, and causes the deskilling of its users. It also comes at a monetary cost and causes various ethical issues e.g., a lack of authenticity. We further show that GenAI’s fundamental issue is that it is by design not able to output human expression but only human-like expression. Using AI for tasks that are fundamentally about communicating is replacing communication with something that is not communication. Finally, we show that the consequences of the use of GenAI cannot be avoided on an individual level by those individuals avoiding the use of GenAI and will necessarily lead to an erosion of human expression in general. This is because GenAI: will lead to a distrust in human expression when the authenticity of authorship over time becomes unclear; will cause a devaluation of human expression when human expression can be mimicked with less effort by GenAI; and, will discourage human expression altogether when GenAI has set the bar too high.

Cyberattack sophistication is rapidly increasing, leading to a decline in the effectiveness of traditional signature-based security solutions. Consequently, intelligent and adaptive security solutions are now being employed that are powered by generative artificial intelligence. Numerous review papers have been conducted on these solutions, but they focus on particular aspects of cybersecurity, providing not a holistic view of all areas. This paper attempts to investigate the applications of generative AI in cyberdefense by reviewing 113 academic papers clustered according to cyberdefense domains indirectly referring to such generative AI models. The review illustrated generative AI’s effectiveness in transferring security from the reactive security model to a proactive one by greatly increasing the detection of new threats and inducing synthetic data which enhances the quality of training, with numerous papers achieving performance accuracy exceeding 95%. There are uniterable issues to overcome however, including the computing costs, model instability and reliance on data. A new multi-dimensional taxonomical methodology and comparative analysis was undertaken on the various generative AI architectures with a strategic roadmap given to guide and orientate further research towards the adoption of autonomous, effective, resilient and reliable cyberdefenses.

In a state of a full-scale war, educational institutions in Ukraine were forced to switch to distance learning. This transformation of the educational space contributed to the rapid introduction of digital technologies, which are actively integrated into the educational process, and therefore have significantly changed the methodological approaches and techniques used to provide language training to foreign students at the initial stage of learning. For quite some time now, information and communication technologies have been part of the educational process, having improved and diversified to the point that in any Ukrainian higher education institution, preparatory departments and faculties have been using all available equipment (from a light projector to a multimedia board), software (from simple editors integrated into MS Office to multifunctional MLS like hot potatoes and Moodle), various Internet resources (e.g. virtual boards, live worksheets) and educational game platforms (like Kahoot!, Quizzes etc.) to ensure the basic principles of learning – systematicity, consistency and visualization – to increase its efficiency and effectiveness, as well as the students’ motivation. However, in the early 2020s, a new trend in the development of technologies took place, which was marked by the emergence and lightning-fast spread of Generative Artificial Intelligence (otherwise known as GenAI). This article focuses on its role in education and everyday life, during which the authors will try to prove the effectiveness, feasibility and advantages of using AI in language training for foreign citizens at the initial stage of learning. After all, although the use of artificial intelligence in the educational system is a relatively new direction, it has significant potential. To truly understand its capabilities, it is important to understand the essence of artificial intelligence and the principles of its operation, as well as to be capable of working with it. Accordingly, this article considers the key definitions of artificial intelligence and its philosophical interpretations. The areas of this advanced technology application are also to be discussed. Particular attention is paid to promising aspects of using artificial intelligence in language education, in particular USL (Ukraininan as a Second Language) training, such as personalized and adaptive learning, knowledge assessment, intermediate testing, the use of gaming technologies (or gamification of the educational process), the development of other innovative approaches using programs and applications that are powered by artificial intelligence algorithms, significantly simplifying the learning process for both teachers and students. Key words: artificial intelligence, automated assessment, chat-bot, educational space, gamification, generative AI, personalization.

The TrollLabs Open dataset includes comprehensive information that offers a comparison of design practices and outcomes between human participants and Generative AI during a hackathon event. The dataset was curated through the running of a prototyping hackathon designed to assess the abilities and performance of generative AI, specifically ChatGPT, in the early stages of engineering design. This assessment involved comparing ChatGPT's performance to that of experienced engineering students in a hackathon setting, where participants competed by making a prototype that fires a NERF dart as far as possible. In this setup, all ideas, concepts, strategies, and actions undertaken by the AI-controlled team were autonomously generated by the ChatGPT, without human intervention or guidance, but implemented by two participants. Five self-directed baseline teams competed against the AI team. The dataset comprises 116 prototype entries and 433 edges (connection) that enable comparative analysis of design practices and performance between the team instructed solely by generative AI and baseline teams of experienced engineering design students. Prototypes and their attribute data were captured using Pro2booth, an online prototype capture platform running on participants' phones and computers. The dataset includes a transcript of the conversation between ChatGPT and the team responsible for implementing its recommendations, featuring 97 exchanges of prompts and responses. It contains the initial prompt used to instruct the AI, the objective and rules of the hackathon and the objective performance of teams, showing the ChatGPT team finishing 2nd among six teams. To the authors' knowledge, the TrollLabs Open dataset is the first and only open resource that directly compares the performance of generative AI with human teams in an engineering design context. Thus, it is intended to be a valuable resource to design researchers, engineering and design students, educators, and industry professionals seeking to find strategies for implementing generative AI tools in their design processes. By offering a comprehensive data collection, the dataset enables external researchers to conduct in-depth analyses that could highlight the practical implications of integrating generative AI in design practices, possibly providing an overview of its limitations and presenting recommendations for improved integration in the design process.

The emergence of generative AI (GenAI), such as ChatGPT, in education reconceptualizes the realm and is novel to researchers and practitioners alike. Over the past few years, systematic reviews of the impact of GenAI on education have increased, focusing on language education and general education. Such reviews may overlook other integrated disciplines. This impact can be reflected in student engagement (learning outcomes) and teacher perspectives. In response, this review aims to investigate the impact of integrating GenAI on student engagement and teacher‐perceived support in science, technology, engineering, art, and mathematics (STEAM) education. It used a thematic analysis approach to examine relevant articles published over the past 5 years (2020–2024). The findings suggest 11 constructs on how GenAI tools affect the development of student cognitive, behavioral, and emotional engagement. They also suggest three themes about how STEAM teachers felt about GenAI tools—attitude, pedagogy, and 21st‐century skills. We used the findings to suggest recommendations for future directions of GenAI research.

The emergence of large language models (LLMs) has ushered in a new era of generative AI, exhibiting broad human-level capabilities and achieving meaningful progress toward Artificial General Intelligence (AGI). To realize AGI that truly benefits each individual, one next key direction for generative AI is to develop personal intelligence -understanding individuals and enhancing personal thinking, planning, and life experiences through personalization. Although evolving generative AI toward personal intelligence is emerging, it has attracted considerable interest from both research institutions and industry, even becoming a strategic direction for many companies. This workshop aims to provide a platform for discussing innovative ideas that facilitate personal intelligence with generative AI, 1) envisioning the future of personal intelligence powered by generative AI, 2) item establishing benchmarks for personal intelligence tasks, 3) advancing user modeling/personalization techniques for generative models/agents, 4) enhancing real-world applications, and 4) ensuring privacy and trustworthiness. This workshop aims to deepen understanding, accelerate progress, and support the transformative development of personal intelligence. The website for the workshop is: https://generative-rec.github.io/workshop-www25.