Re-Imagining Marketing Education for Career Readiness in the GenAI Era

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.

This study investigates the potential of small marketing firms to disrupt the market by adopting generative AI technology and the theory of disruptive innovation. The study employs a qualitative approach, combining a comprehensive literature review with in-depth interviews with leaders of small marketing firms. The research findings position generative and conversational AI as the next technological evolution, succeeding the internet and mobile/social era. It is the first study applying the theory of disruptive innovation to generative AI use in small marketing firms, presenting a positive outlook toward integrating generative AI into marketing operations. The study contributes to the emerging knowledge of AI in marketing, offering practical implications for scholars and practitioners to advance this field.

Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late 2022. Tools like ChatGPT, Dall-E, or Midjourney have democratized access to Large Language Models, enabling the creation of human-like content. However, the concept 'Generative Artificial Intelligence lacks a universally accepted definition, leading to potential misunderstandings. While a model that produces any output can be technically seen as generative, the Artificial Intelligent research community often reserves the term for complex models that generate high-quality, human-like material. This paper presents a literature mapping of AI-driven content generation, analyzing 631 solutions published over the last five years to better understand and characterize the Generative Artificial Intelligence landscape. Our findings suggest a dichotomy in the understanding and application of the term "Generative AI". While the broader public often interprets "Generative AI" as AI-driven creation of tangible content, the AI research community mainly discusses generative implementations with an emphasis on the models in use, without explicitly categorizing their work under the term "Generative AI".

Generative Artificial Intelligence or Generative AI is an innovation with a terrible name.First, generative means something that originates, produces, or even reproduces.Next, artificial Intelligence is defined as intelligence exhibited by machines (machine intelligence).So, if the two are put together the result is a machine that originates intelligence—in other words, a thinking machine. Hmmm!Generative artificial intelligence is typically defined as a technology capable of generating text, images, videos, or other data, usually in response to prompts.At best the name generative AI is confusing, and to many it is scary.Everett Rogers in his classic book Diffusion of Innovations (2003) states that “the name given to an innovation often affects its perceived compatibility, and therefore its rate of adoption (p. 250).”Certainly, Generative AI is an innovation—it is new and is probably the innovation of this decade.Everett Rogers defined Innovation as “an idea, practice or object that is perceived as new by an individual or other unit of adoption (p. 12)”.What does all this mean to distance educators?Maybe first we should learn more about Generative AI. So, just for the fun of it, the written sections above were entered into Gemini AI, a generative AI tool, and a competitor to ChatGPT.Here is what Gemini produced:You’re right, “Generative AI” while descriptive, and can be misleading.Here’s a breakdown of why it might not be the best term and some suggestions for alternatives:The Problems with “Generative AI”The best term will depend on the context. “Machine Learning for Content Creation” might be more suitable for a technical audience, while “Creative AI” could be better for a general audience.This revision process took about 5 seconds, and by any measure the results were impressive—the Generative AI analysis seemed insightful and accurate.Rogers recommends that potential innovation users should learn about the innovation by studying its attributes—there are five—relative advantage, compatibility, complexity, trialability, and observability.First is the idea of relative advantage—defined as the degree to which an innovation is perceived as being better than the idea it replaces. Relative advantage is often expressed as the innovation’s economic profitability or its ability to convey social positions.The next characteristic of an innovation is its compatibility, which is the degree to which the innovation is consistent with existing values, past experiences, or user needs. Innovations can be either compatible or incompatible.Next is complexity, explained as whether the innovation is perceived as difficult or easy to use.A complex innovation, as perceived by potential adopters, can significantly hinder adoption.Trialability is the ability to experiment with an innovation before adoption. If an innovation can be easily tried it will be likely to have a rapid rate of adoption or rejection.Observability is the visibility of applying the innovation. Observability means seeing the results of using the innovation. High observability promotes faster decisions about adoption.In other words, does Generative AI allow us to do things better? Next, are the results of use compatible with what the user needs or wants? Third, is Generative AI easy or difficult to use, and can we quickly try it out? Finally, can we see the results when Generative AI is used?Computer scientists argue that Generative AI is better, compatible, easier, not complex, and results are clear—maybe this is true, but the name is still horrible.Generative artificial intelligence sounds intimidating and threatening. Perhaps ‘creative artificial intelligence’ would be more inviting—perhaps not!Distance educators should understand, study, and evaluate Generative Artificial intelligence and write about it—perhaps someone from the U.S. Distance Learning Association could “coin” a new name.(Distance Learning would love to publish manuscripts on this and other related topics.)And finally, Galen said “The chief merit of language is clearness, and we know that nothing detracts so much from this as do unfamiliar terms.”NOTE: Rewrites of the second half of this column using personal intelligence (PI) took 5 tries and 4 hours—a relative advantage?

Generative AI (GenAI) tools are transforming health professions education, offering opportunities to enhance faculty development (FD). Faculty developers are uniquely positioned to integrate GenAI into practice to address resource constraints, improve accessibility, and foster equity across diverse educational contexts. This Applied Insights article offers a perspective on how GenAI can be leveraged as a co-developer in FD by drawing on emerging literature and discussion points from a workshop at the 8th International Faculty Development Conference in the Health Professions. The applied insights are structured around key phases of FD: planning, content creation, delivery, and evaluation. They include actionable strategies for using GenAI in needs assessment, multilingual and culturally relevant resource creation, personalized learning plans, and when providing feedback and mentorship. Each insight is rooted in pedagogical rationale, evidence, and strategies to address ethical and practical challenges, with an emphasis on human oversight, contextual relevance, and continuous evaluation of GenAI's impact. By considering these insights, faculty developers can harness GenAI to co-design educational materials, extend their reach through innovative formats, and maintain ethical and equity-driven educational practices. This article highlights the transformative potential of GenAI in FD when thoughtfully integrated. GenAI can empower faculty developers to enhance the quality and inclusivity of HPE while safeguarding educational standards.

The study examines the transformative potential impact of Generative AI (GAI) on society, media, and media education, focusing on the challenges and opportunities these advancements bring. GAI technologies, particularly large language models (LLMs) like GPT-4, are revolutionizing content creation, platforms, and interaction within the media landscape. This radical shift is generating both innovative educational methodologies and challenges in maintaining academic integrity and the quality of learning. The study aims to provide a comprehensive understanding of how GAI impacts media education by reshaping the content and traditional practices of media-related higher education. The research delves into three main questions: the nature of GAI as an innovation, its effect on media research and knowledge acquisition, and its implications for media education. It introduces critical concepts such as radical uncertainty, which refers to the unpredictable outcomes and impacts of GAI, making traditional forecasting and planning challenging. The paper utilizes McLuhan’s tetrad to analyze GAI’s role in media, questioning what it enhances or obsoletes, retrieves, or reverses when pushed to extremes. This theoretical approach helps in understanding the multifaceted influence of GAI on media practices and education. Overall, the research underscores the dual-edged nature of GAI in media education, where it presents significant enhancements in learning and content creation while simultaneously posing risks related to misinformation, academic integrity, and the dilution of human-centered educational practices. The study calls for a balanced approach to integrating GAI in media education, advocating for preparedness against its potential drawbacks while leveraging its capabilities to revolutionize educational paradigms.

The significance of ongoing study and development of AI technology is emphasized as this chapter explores the part and utility of generative AI in medical education and training, examines the difficulties it encounters, and systems unborn development patterns in the medical field. We can have a better understanding of how generative AI is impacting medical education going forward and offering fresh styles for training healthcare workers by reading this thorough review. A branch of artificial intelligence called” generative AI” is concerned with creating systems that can produce original and cultural labors, including textbooks, music, plates, and more. These systems may produce content that mimics mortal-generated content on their own by exercising deep literacy ways, particularly generative models. The interesting field of generative artificial intelligence focuses on creating systems that can singly produce original, creative content. It makes it possible for machines to perform creative and imaginative tasks in addition to further conventional bones. By exercising generative models and deep literacy approaches, these systems may induce innovative labors that nearly mimic mortal-generated content, including literature, music, prints, and more. This system makes it possible to produce creative and original content, making it an effective tool for various uses. Generative models are central to the idea of generative AI. Generative AI enables machines to autonomously induce creative content, similar as images, music, textbooks, and more. This addresses the need for new and different content in colorful disciplines, including art, entertainment, design, and marketing. Generative AI opens new possibilities for creative expression and expands the boundaries of mortal imagination. The possibilities for creative expression are increased, and the limits of mortal imagination are pushed by generative AI. Medical training serves as a means of guaranteeing that the performance of the mortal pool is observed in a realistic and secure setting. Their use of generative AI to produce virtual cases to instruct medical scholars. These realistic clinical scenarios in the simulations were designed to help medical professionals and students make better diagnoses and treatment-related decisions.

This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that GenAI is not ideologically and culturally neutral. Instead, it reflects worldviews that can reinforce existing biases and marginalize diverse voices. Furthermore, as the use of GenAI reshapes education, it risks eroding essential human elements—creativity, critical thinking, and empathy—and could displace meaningful human interactions with algorithmic solutions. This manifesto calls for robust, evidence-based research and conscious decision-making to ensure that GenAI enhances, rather than diminishes, human agency and ethical responsibility in 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.

Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards.

The study examines the role of Generative Artificial Intelligence (GenAI) integration and the SWAYAM MOOC learning platform in enhancing career opportunities among management students by aligning higher education with contemporary industry requirements. The research is grounded in the growing need for technologically enabled, industry-relevant, and ethically responsible management education in the era of digital transformation. The study particularly focuses on how Generative AI supports skill development and ethical decision-making, while SWAYAM contributes to structured, certification-oriented, and industry-aligned learning. The empirical investigation was conducted using a sample of 208 MBA students from Bangalore, a major educational and technological hub in India, making it an ideal location to study the impact of advanced digital learning tools in management education. Data were collected through a structured questionnaire measuring Generative AI integration, SWAYAM learning effectiveness, and Career Opportunities. The analysis was carried out using PLS-SEM to test the reliability, validity, and structural relationships among the constructs. The measurement model demonstrated strong reliability and validity, with Career Opportunities and Generative AI integration showing high internal consistency and adequate convergent validity. Although SWAYAM learning exhibited slightly lower convergent validity, its reliability remained within acceptable limits, confirming its suitability for further analysis. The structural model results revealed that both Generative AI integration and SWAYAM learning have a statistically significant and positive impact on Career Opportunities. Specifically, Generative AI showed a moderate yet significant influence (β = 0.201, p = 0.001), while SWAYAM learning exerted a strong and dominant effect (β = 0.630, p < 0.001) on enhancing employability, job readiness, and career advancement prospects. The findings indicate that SWAYAM plays a pivotal role in improving career outcomes through industry-relevant content, certification value, and flexible learning, while Generative AI acts as a complementary enabler that enhances personalized learning, analytical capabilities, and ethical decision-making competencies. Together, they form a powerful ecosystem that bridges the gap between higher education and industry expectations. This study contributes to the literature on technology-enabled management education by providing empirical evidence from Bangalore-based MBA students and offers practical implications for curriculum designers, academic institutions, and policymakers. It highlights the importance of integrating Generative AI and national digital learning platforms like SWAYAM to build a future-ready, ethically aware, and industry-aligned management workforce.

This study explores the role of generative AI (GenAI) in providing formative feedback in children's digital learning experiences, specifically in the context of mathematics education. Using multimodal data, the research compares AI‐generated feedback with feedback from human instructors, focusing on its impact on children's learning outcomes. Children engaged with a digital body‐scale number line to learn addition and subtraction of positive and negative integers through embodied interaction. The study followed a between‐group design, with one group receiving feedback from a human instructor and the other from GenAI. Eye‐tracking data and system logs were used to evaluate student's information processing behaviour and cognitive load. The results revealed that while task‐based performance did not differ significantly between conditions, the GenAI feedback condition demonstrated lower cognitive load and students show different visual information processing strategies among the two conditions. The findings provide empirical support for the potential of GenAI to complement traditional teaching by providing structured and adaptive feedback that supports efficient learning. The study underscores the importance of hybrid intelligence approaches that integrate human and AI feedback to enhance learning through synergistic feedback. This research offers valuable insights for educators, developers and researchers aiming to design hybrid AI‐human educational environments that promote effective learning outcomes. Practitioner notes What is already known about this topic? Embodied learning approaches have been shown to facilitate deeper cognitive processing by engaging students physically with learning materials, which is especially beneficial in abstract subjects like mathematics. GenAI has the potential to enhance educational experiences through personalized feedback, making it crucial for fostering student understanding and engagement. Previous research indicates that hybrid intelligence that combines AI with human instructors can contribute to improved educational outcomes. What this paper adds? This study empirically examines the effectiveness of GenAI‐generated feedback when compared to human instructor feedback in the context of a multisensory environment (MSE) for math learning. Findings from system logs and eye‐tracking analysis reveal that GenAI feedback can support learning effectively, particularly in helping students manage their cognitive load. The research uncovers that GenAI and teacher feedback lead to different information processing strategies. These findings provide actionable insights into how feedback modality influences cognitive engagement. Implications for practice and/or policy The integration of GenAI into educational settings presents an opportunity to enhance traditional teaching methods, enabling an adaptive learning environment that leverages the strengths of both AI and human feedback. Future educational practices should explore hybrid models that incorporate both AI and human feedback to create inclusive and effective learning experiences, adapting to the diverse needs of learners. Policymakers should establish guidelines and frameworks to facilitate the ethical and equitable adoption of GenAI technologies for learning. This includes addressing issues of trust, transparency and accessibility to ensure that GenAI systems are effectively supporting, rather than replacing, human instructors.

Generative AI systems are increasingly present in our daily lives, helping us make crucial decisions. They use machine learning algorithms and tools, fed with millions of data collected from the web, producing entirely new information and generating variations. And this is not just limited to texts — it can produce images, audio, videos, even code, or new programming languages. There are several fields where generative AI can have a considerable impact in the coming years. In this context, the issues proposed in this chapter are: What is generative AI? What is prompt engineering? How to transform education using generative AI and prompt engineering in creating synthetic content? To respond to the research problem, the following objective will be achieved: Investigate how to transform education using generative AI and prompt engineering in the creation of synthetic content. It is concluded that generative AI tools can also help create more efficient exercises. Teachers and educators can use technology to create instructional materials and present summaries of concepts.

Generative AI, through the efficient production of various forms of material, has the ability to completely transform the way that course content is developed. The use of generative AI in the creation of course content is crucial since it may further optimize the procedure, lessen the strain for teachers, and provide personalized, excellent, and captivating learning resources. The study aims to demonstrate how generative AI is included in the creation of course materials. Additionally, it suggests the use of a new framework called "Generative AI for Instructional Development and Education," or "GAIDE," which further makes use of AI's potential to improve educational outcomes. The use of generative AI in this framework demonstrated in particular how effective and useful these integrations were for creating instructional content and how applicable they were for creating, modifying, and evaluating the content. A mixed-methods strategy is being used in this project, which integrates quantitative and qualitative data collection techniques. The study's sample was primarily made up of about 75 Indian volunteers. The evaluation metrics for this study primarily consist of structured questionnaires and analysis using SPSS software for quantitative data analysis. Thematic analysis is used to evaluate secondary qualitative data in order to identify numerous recurrent themes and various patterns. As per the findings of this study, the applicability of generative AI is emergent and progressive for course content generation through stages of prompts and refinement, while the impact is held strongly through the efficiency delivered in faster and more extensive means for generating and refining quality course content through generative AI.

Generative AI tools like GitHub Copilot are transforming software development by providing intelligent code assistance, enhancing developer productivity, and introducing innovative approaches to secure software development. This paper explores the role of generative AI in improving secure coding practices and reducing vulnerabilities in software. It examines how AI-powered coding tools assist developers in adhering to security standards, detecting and mitigating potential vulnerabilities, and fostering secure development practices. The analysis highlights the benefits of integrating generative AI into the Secure Software Development Lifecycle (SSDLC), such as automated code suggestions, real-time feedback on potential security issues, and adherence to secure coding patterns. However, the paper also delves into the limitations and challenges of relying on generative AI, including risks related to biased training data, lack of contextual understanding, and potential for introducing insecure code snippets. Finally, it discusses future directions for enhancing generative AI tools to better address security challenges, emphasizing the need for transparency, explainability, and continuous improvement in their security-focused capabilities. By bridging the gap between generative AI and secure software development, this paper provides actionable insights into leveraging these technologies to build resilient, secure, and high-quality software.