⁠AI vs Human Creativity: Are Machines Generating Better Ideas?

This research explores the impact of artificial intelligence (AI) on graphic design students at University, specifically focusing on motion design and animated characters. The study aims to investigate the relationship between AI awareness, usage, and creativity enhancement among design students, as well as to assess how AI tools influence their design processes. The population for this study consisted of graphic design students from five Egyptian universities, with a sample of 156 students from 6 University. The research employed a mixed-methods approach, combining both quantitative and qualitative data collection methods. A structured questionnaire was administered to collect data on students' AI awareness, usage, and perceived impact on creativity, while interviews were conducted to gain deeper insights into their experiences. The statistical analysis included correlation, regression, and ANOVA tests to examine the relationships between key variables. The results revealed a significant positive correlation between AI awareness and usage, with higher levels of awareness leading to more frequent use of AI tools. Regression analysis indicated that both AI awareness and usage significantly predicted students' perceptions of enhanced creativity in motion design and animation. Additionally, ANOVA results showed significant differences in AI usage across academic years, with advanced students (3rd and 4th years) reporting higher usage of AI tools compared to first- and second-year students. Based on the findings, the study recommends that universities integrate AI tools into the curriculum from the first year, offer specialized workshops to enhance AI skills, provide access to AI software, and foster collaboration between design and AI experts. Additionally, ethical concerns regarding AI's role in creative work should be addressed, and future research should explore the long-term effects of AI on students' careers in design. This research underscores the importance of AI in shaping the future of graphic design education and creative industries.

AI-driven maternal and child healthcare nursing education: Network analysis of self-efficacy and usage demands.

This study examines the relationship between artificial intelligence (AI) usage and creativity levels among students studying in faculties of fine arts. With the rapid development of generative AI technologies, artistic production processes and educational practices in art and design disciplines have been significantly transformed. In this context, understanding how students use AI tools and how these tools relate to their creativity has become an important research topic in art education. The research was designed using a relational survey model within the general survey framework. The study group consisted of 201 undergraduate students studying in faculties of fine arts at universities in Türkiye. Data were collected using the Artificial Intelligence Usage Scale and the Kaufman Domains of Creativity Scale (K-DOCS). Descriptive statistics, independent samples t-test, one-way ANOVA, and regression analysis were used to analyze the data. The findings indicated that students’ AI usage levels were above the moderate level (M = 3.80). Students reported the highest creativity levels in the domains of artistic performance and artistic creativity, while academic and scientific/mechanical creativity were found at moderate levels. Gender comparisons revealed significant differences in academic creativity, scientific/mechanical creativity, and AI usage in favor of male students. Regarding class level, significant differences were found in academic and scientific/mechanical creativity, with upper-level students reporting higher levels of creativity. However, AI usage did not differ significantly across class levels. Regression analysis showed a positive and significant relationship between creativity and AI usage, with creativity explaining 10.2% of the variance in AI usage. Among the creativity domains, only academic creativity and scientific/mechanical creativity significantly predicted AI usage. Overall, the findings suggest that creativity in fine arts students is domain-specific and that different creativity domains relate to AI usage in distinct ways. While academic and scientific creativity appear to encourage the use of AI tools, artistic creativity does not significantly predict AI usage. These results highlight the importance of integrating AI literacy and ethical AI use into art education curricula to support students’ creative development in the digital age.

This study builds on the increasing relevance of technology integration in higher education, specifically in artificial intelligence (AI) usage in educational contexts. Background research highlights the limited exploration of AI training in educational programs, particularly within Latin America. AI has become increasingly pivotal in educational practices, influencing the development of competencies in various disciplines, including experimental sciences. This study aimed to describe the correlation between professional competencies in AI, AI usage, and digital resources among students in the experimental sciences education program at the National University of Chimborazo. Methodologically, a quantitative approach was employed, involving a structured survey distributed among 459 students. Data analysis was conducted using multiple regression models to establish predictive insights into AI usage. A multiple linear regression model was developed to predict AI usage among these students. The analysis revealed significant correlations between AI competencies, AI usage, and digital resources. The regression model highlighted that both AI competencies and digital resources are significant predictors of AI usage. These findings underscore the importance of developing AI competencies and providing access to digital resources to enhance the effective use of AI in educational practices. Limitations and future research directions are discussed.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

The author examines the problems of human creativity in the context of the emergence of modern artificial intelligence technologies. A scientific analysis of the areas of artificial intelligence application in the human creative process is carried out; it is shown that one of the most interesting and at the same time complex aspects of artificial intelligence is its ‘creative capabilities’ and its impact on the creative development of the individual. It is emphasised that the importance of analysing the artificial intelligence application in human creativity is due not only to the technological achievements of modern science, but also to the ethical, cultural and psychological dynamics that arise in the interaction between man and machine. The study and synthesis of scientific sources on creativity and the abilities of artificial intelligence allows us to conclude that today its application in the human creative process is possible in the following areas: artificial intelligence as a tool for ensuring the creative process, which accelerates the performance of routine tasks, provides quick feedback, and helps in collecting information; artificial intelligence as a method of expanding human creative abilities, i.e., performing tasks requiring divergent and convergent thinking or searching for and solving problems; artificial intelligence as a partner in human creative development, i.e., expanding the boundaries of imagination, fantasy, and problem vision; increasing motivation and interest in solving creative problems; influencing the formation and development of character traits and qualities of the human psyche necessary for productive creativity.

Artificial Intelligence (AI) is increasingly integrated into creativity and innovation processes in the modern world. However, concerns have been raised regarding its effects on human creativity. The automated content generation provided by AI, its guidance in problem-solving processes, and its facilitation of artistic production may negatively impact individuals' creative thinking capacities (Carr, 2020). By generating content through big data analysis and algorithms, AI may restrict human creativity. Particularly in the fields of art, writing, and design, the widespread use of AI-based tools may diminish individuals' abilities to generate original ideas. Some studies indicate that individuals may become excessively dependent on AI suggestions, thereby relegating their own creative processes to a secondary position (Kowalski, 2021). This phenomenon may lead to a decline in people's creative problem-solving skills and a reduction in innovative thinking. Moreover, the tendency of AI-generated content to become homogenized may result in a decrease in artistic and cultural diversity. AI systems learn from past data to produce content, which can confine creative processes within the patterns of the past (Smith & Anderson, 2022). One of the fundamental elements of creativity, individual and societal originality, may be compromised due to AI's repetitive nature. Finally, considering AI’s impact on problem-solving processes, it is suggested that individuals' critical thinking skills may deteriorate over time. The ability of AI to provide fast and accurate solutions may weaken people's habits of inquiry and reduce their capacity to develop innovative solutions (McCarthy, 2023). In this context, AI is emphasized not as a tool that supports creative processes but as a factor that may constrain them.

Purpose This study examines the extent to which artificial intelligence (AI) applications predict psychological flourishing and sustainable development among Fayoum University students. It further explores gender differences in AI usage, flourishing levels, and sustainability orientations, as well as the interrelationships among the three variables. Methods A quantitative correlational design was employed using validated scales measuring AI application use, psychological flourishing, and sustainable development. The sample consisted of 190 university students from Fayoum University (Egypt) there ages were between 20 and 25 years. The mean age was 22.54 years, with a standard deviation of 3.22 years. Data were analyzed using t-tests, Pearson’s correlation coefficients, and simple linear regression to test the study hypotheses. Results Findings revealed no significant gender differences in AI application use, while significant differences were found in psychological flourishing and sustainable development in favor of male students. Significant positive correlations emerged among AI use, flourishing, and sustainability. Regression analyses showed that AI application use moderately predicted psychological flourishing (R 2 = 0.098) but strongly predicted sustainable development (R 2 = 0.604), indicating that AI plays a substantial role in shaping students’ sustainability-related behaviors. Conclusions The study demonstrates that AI applications contribute meaningfully to enhancing students’ well-being and sustainability practices, with a stronger influence on sustainability outcomes. These findings underscore the growing relevance of AI-driven learning environments in promoting sustainable education and student development. Implications The results highlight the need for integrating AI-based tools into higher education policies, fostering responsible AI use, and developing institutional initiatives that enhance students’ well-being and sustainability competencies.

ABSTRACTThe integration of artificial intelligence (AI) into education has the potential to revolutionize how students engage in academic activities and tasks. This research empirically analyses the influence of AI on creative ideation within educational settings to validate AI's role in enhancing human creativity since creative tasks, which inherently rely on human intuition, emotion and divergent thinking, may be stifled by systematic AI tools. The study explores whether ChatGPT can aid the creative process or inadvertently limit human creativity with a mixed‐method approach consisting of a randomized controlled experiment with third‐year engineering students in which a total of 728 ideas were obtained, along with a structured survey. The results revealed a predominantly positive perception towards AI‐assisted ideation; nevertheless, concerns were raised about AI's influence on creativity and innovation. While no significant differences in ideation outcomes were observed between the groups that used AI and those that did not, significant differences emerged between students who had prior experience with ChatGPT and those who did not. Qualitative insights provided a nuanced view of student experiences on blended human–AI ideation processes, shedding light on its advantages and disadvantages in educational practices. This research also underscores critical considerations and potential risks associated with the adoption of AI, suggesting that while AI has a place in educational settings, its role should be carefully calibrated to support rather than stifle student creativity and innovation. From the findings, the study provides practical recommendations and best practices regarding the integration of AI tools in education.

The rapid advancement of artificial intelligence (AI) is transforming the advertising industry by enhancing precision, personalization, and operational efficiency. AI's ability to analyze consumer behavior in real time allows for highly targeted advertising, while machine learning algorithms optimize ad delivery and content generation. Despite these benefits, the integration of AI also introduces challenges such as data privacy concerns, algorithmic biases, and potential reductions in creative originality. This research explores the advancement of advertising and marketing under AI application patterns, intending to assess both the benefits and the obstacles presented by modern AI technologies. By analyzing present techniques and problems, the research seeks to offer recommendations for leveraging AI efficiently while dealing with arising issues and incorporating strategies such as the bolstering of data privacy protections, the rectification of algorithmic prejudices, the synchronization of artificial intelligence with human creativity, the enhancement of AI's cultural sensitivity and emotional intelligence, and the optimization of methodologies employed in evaluating advertising efficacy. The findings will certainly provide beneficial insights for industry experts, tech developers, and policymakers to navigate the progressing landscape and ensure lasting development in the AI-driven marketing field.

Artificial intelligence (AI) is significantly transforming the nursing profession, enhancing patient care, and shaping future nursing practice. Understanding nursing students' attitudes toward AI applications is crucial for its effective integration into clinical practice and education. To evaluate nursing students' attitudes toward AI in Palestine. A cross-sectional design was conducted among 325 nursing students. Due to logistical constraints, data were gathered via online surveys using the AI attitude scale. The research was conducted between February and March 2024 at Arab American University in Palestine. The results showed that the average attitudes toward using AI in nursing practice scores (M = 61.81; SD = 9.74) were significantly greater than the neutral score (p = .001). Nursing students have a positive attitude toward AI in terms of benefits and willingness to use it in nursing practice. However, nursing students have a neutral attitude toward the practical advantages of AI and exhibit a negative attitude toward the dangers of AI in nursing. Furthermore, gender, academic year, and purpose of AI had statistically significant differences in nursing students' attitudes toward AI (p = .034, .039, and 0.042 respectively). Female students showed higher levels of attitudes toward AI usage, while participants with master's degree participants had the lowest level of attitudes toward AI. Our findings demonstrate that nursing students have a positive attitude toward the integration of AI into nursing and healthcare practice, along with significant intentions to utilize the technology. The results highlight the need for AI-focused training within nursing curricula.

Artificial Intelligence (AI) is transforming research across multiple disciplines, including social science research. This study highlights the application and significance of AI in the social sciences by employing bibliometric analysis to map its usage in various research contexts. Building on the literature, the paper proposes a mixed-method model that examines key antecedents influencing user satisfaction with AI and the intention to use AI in the future. Data were collected from 191 social scientists, revealing that factors such as psychological aspects, professional associations, and future perspectives significantly impact satisfaction with AI usage. Furthermore, the findings indicate that satisfaction with AI directly affects the intention to use AI in future research. The study offers meaningful insights for both academia and industry, enhancing understanding of AI adoption and its implications in social science research.

This study examined the relationship between artificial intelligence (AI) usage and critical thinking skills among second-year BSED-English students. Utilizing a descriptive-correlational research design, data were collected from 89 respondents selected through Raosoft sampling. Statistical analysis revealed a moderate positive correlation between AI usage and critical thinking skills, suggesting that students who frequently engage with AI tend to exhibit stronger cognitive abilities. However, findings indicate that while AI is frequently used for basic academic tasks due to its accessibility, its potential to support deeper understanding remains underutilized, highlighting the need for more guided and meaningful integration in learning. Given these findings, educators should encourage proactive AI usage, integrate AI-driven formative assessments, and provide digital literacy training. Academic institutions should improve AI accessibility and develop AI literacy programs that emphasize cognitive skill development. Future research should explore AI tools that enhance students’ ability to comprehend complex texts, recognize relationships between ideas, and modify written structures. Optimizing AI integration in education may enhance cognitive development and learning experiences.

We are glad to introduce you that the 2021 3rd International Conference on Artificial Intelligence Technologies and Applications (ICAITA 2021) was successfully held on September 10-12, 2021. In light of worldwide travel restriction and the impact of COVID-19, ICAITA 2021 was carried out in the form of virtual conference to avoid personnel gatherings. Because most participants were still highly enthusiastic about participating in this conference, we chose to carry out ICAITA 2021 via online platform according to the original schedule instead of postponing it.ICAITA 2021 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence Technologies and Applications to a common forum. The primary goal of the conference is to promote research and developmental activities in Artificial Intelligence Technologies and Applications and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Artificial Intelligence Technologies and Applications and related areas.This scientific event brings together more than 100 national and international researchers in artificial intelligence technologies and applications. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.We were pleased to invite three distinguished experts to present their insightful speeches. Our first keynote speaker, Prof. Yau Kok Lim, from Sunway University, Malaysia. His research interests include Applied artificial intelligence, 5G networks, Cognitiveradio networks, Routing and clustering, Trust and reputation, Intelligent transportation system. And then we had Prof. Peter Sincak, from Technical University of Kosice, Slovakia. His research includes Artificial Intelligence and Intelligent Systems. Lastly, we were glad to invite Chinthaka Premachandra, from Shibaura Institute of Technology, Sri Lanka. His research interests include Artificial Intelligence, image processing and robotics. In the last part of the conference, all participants were invited to join in a WeChat group to discuss and explore the academic issues after the presentations. The online discussion was lasted for about 30-60 minutes. The first two parts were conducted via online collaboration tool, Zoom, while the online discussion was carried out through instant communication tool, WeChat. The online platform enabled all participants to join this grand academic event from their own home.We are glad to share with you that we still received lots of submissions from the conference during this special period. Hence, we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Artificial Intelligence Applications & Technologies, Computing and the Mind, Foundations of Artificial Intelligence and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.Lastly, we would like to express our sincere gratitude to the Chairman, the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy the gain some valuable knowledge from the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.The Committee of ICAITA 2021List of titles Committee member, General Conference Chair, Technical Program Committee Chair, Academic Committee Chair, Technical Program Committee Member, Academic Committee Member are available in this Pdf.

The article "Embracing Real AI: A Call to Action for Medical Physicists in Healthcare" urges medical physicists to prepare for the integration of artificial intelligence (AI) into healthcare practices, emphasizing their pivotal role in adapting to technological advancements. The authors advocate for embracing AI through advocacy, broadening perspectives, and enhancing coordination and communication. They propose an ABC strategy focusing on increasing educational initiatives, fostering interdisciplinary collaboration, and creating team collaboration to facilitate AI integration. The commentary highlights AI's potential in enhancing diagnostics, personalizing medicine, and automating routine tasks while addressing challenges such as data sharing and the role of federated learning. The article calls for medical physicists to lead in embracing AI, emphasizing continuous learning and collaboration to leverage its potential for improving healthcare and patient care. Medical physicists have consistently demonstrated strong interest in developing proficiency in the adoption of new technological advancements. The roots of the profession come from the radiation sciences, including radiation protection, radiation therapy, diagnostic imaging, and nuclear medicine.1 As science and technology continued to evolve, medical physicists' roles have extended into other non-radiation domains, such as non-ionizing-radiation-based imaging (ultrasound and magnetic resonance), molecular imaging, computer aided diagnosis (CAD), information technologies, and data science.2 In addition, medical physicists gradually have adopted increasingly more active roles in ensuring the professional education of other radiology/radiation oncology team members, maintaining high quality standards via quality assurance (QA) methods. They also play a major role in advising the hospital management on medical devices and software acquisition. The continuing expansion of these roles and responsibilities has put medical physicists on the forefront of embracing emerging technologies, making the profession one of the most technical and versatile in healthcare settings. Currently, as our field grows in importance, we medical physicists seek to continue to engage in significant ways to for increased contributions and roles in human health. This commentary/opinion urges medical physicists to prepare for their expanding roles in the field of AI and its implementation and oversight in clinical practice. Medical physicists must embrace "Real AI" to help integrate AI into healthcare practices. Conceptually we advocate for a strategy that involves Real AI through advocacy, broadening, and enhancing coordination/communication (an ABC strategy). In our current and future work medical physicists will use AI to automate routine tasks, allowing medical physicists to focus on more complex tasks. Furthermore, Medical Physics will use AI to enhance efficiency, safety, diagnostic and therapeutic applications, and for personalized medicine. However, as we have done in the past with other complex concepts (such as radiation), medical physicists need to be prepared for the potential risks and ethical dilemmas associated with AI, such as bias and lack of transparency. It will be important that Medical Physicists prepare for the rapidly changing AI landscape, and continue learning, gain hands-on experience, and collaborate with other AI experts in the healthcare environment. This paper aligns with the already approved guidance document developed by the AAPM in conjunction with International Atomic Energy Agency (IAEA)3 that discusses how medical physicists can ensure the effective implementation and management of AI systems. It is crucial for the Clinical Quality Management Program (CQMP) personnel to receive regular training and updates on relevant guidelines and legislation. Clear communication channels should be established with IT experts, vendors, and other stakeholders for smooth coordination.4 Comprehensive documentation should be developed to ensure compliance with contractual obligations and guidelines. The clinical team should be involved in acceptance testing and discussions, depending on the clinical purpose of the AI system.4 Protocols for data collection and curation should be established, along with the development of standardized validation datasets for performance evaluation.4 A system for monitoring updates to AI systems and models should be implemented, with the CQMP leading new acceptance/commissioning rounds for any updates. Lastly, mechanisms for continuous evaluation and improvement of the CQMP processes should be established, which could involve regular audits, feedback mechanisms from end-users, and incorporating lessons learned from previous rounds.4 Nowadays, major healthcare systems in the US consider their data as immensely valuable assets that require rigorous protection to ensure Health Insurance Portability and Accountability Act (HIPAA) compliance, as well as intellectual property considerations. It can be very difficult for researchers to share clinical data with vendors for development purposes without a significant return being specified to the institution, such as joint intellectual property or substantial grant funding. Instead, these healthcare systems encourage their researchers to commercialize their findings independently, allowing the institution to retain full rights to intellectual property. That said, the realization of federated learning would be a significant advancement. To achieve this, a powerful pre-trained model that would be adaptable to operation on different scales and in various clinical scenarios is necessary. It is plausible that local adaptation may not require substantial computing power or AI expertise. This concept is particularly intriguing and could be beneficial to smaller centers and clinics in underserved areas. However, the primary challenge is the cost. As we become more reliant on AI systems like OpenAI's ChatGPT or Google Gemini, we often overlook the fact that these conveniences come with a hefty price tag, costing billions of dollars to develop and maintain.5 As medical physicists we and other healthcare professionals can anticipate that AI will significantly transform healthcare, improving efficiency, accuracy, and the level of detail that can be extracted from imaging, and methods of therapy. These technological advancements are expected to bring immense value to the field, offering a new horizon in diagnostic and therapeutic capabilities. Yet, we also must recognize that it also introduces potential significant risks and ethical dilemmas. One of the primary concerns is the possibility of bias in AI, which can stem from the training data, the algorithms, or their application, leading to potentially detrimental effects on patient care. As medical physicists, we should acknowledge that the complexity and lack of transparency in AI decision-making processes present obstacles in terms of accountability and rectifying errors and requires greater oversight and responsibility. The integration of AI also has great capacity in redefining the role of medical physicists, impacting education and employment within the field. Addressing these issues necessitates the creation of ethical standards for AI in healthcare, emphasizing transparency, responsibility, and equity, with contributions from diverse stakeholders, including patients, medical professionals, and ethicists.6 Such measures are crucial to ensure the responsible utilization of AI in healthcare, and ultimately serve the best interests of patients and society. We anticipate that continued guidance from our professional societies will be helpful as our collective communities develop methods and approaches that help us learn, adopt, and employ AI responsibly. Advocacy: increase educational initiative, public awareness, and recommending processes at all levels of the clinical workforce, as well as patient engagement. Broadening Perspectives: encourage Interdisciplinary Collaborations that allow medical physicists to work with professionals from other disciplines such as computer science, data science, and biomedical engineering, to gain insights into different perspectives on AI applications in healthcare. This enables medical physicists to provide continuing education and connect the community with research opportunities. Improving Coordination and Communication through creating team collaboration: enhance communication with healthcare professionals, administrators, and patients by clearly defining and articulating the role of medical physicists in AI applications. Promote the sharing of knowledge, as exemplified by creating data repositories through contributions, to further creating the foundation of our understanding and application of AI in the field. We consider the concept of Real AI in our context to be aimed at providing and/or qualifying a ready AI product that has undergone a rigorous QA process, that is free of false additives and biases, with data carefully curated to represent the demographics and be attuned to the needs of the clinic, sourced with proper ingredients, and abiding by laws and regulations that can ensure the product serves the common health needs of patients and benefits the public's interest. What AI 'is' and what it 'is not' is a complex topic that warrants further exploration and understanding, but one vital for comprehension of what utility AI can fulfill in the clinical process, what its advantages and limitations are, and how it can be curated to perform in the clinical scenarios relevant to a particular radiology/radiation oncology practice. Multiple data-analysis algorithms have been created over the course of years, and not all of them qualify as AI.7 What distinction(s) lie in what constitutes AI? One possible interpretation is that AI is a system that can adapt to new data, or a system that generates insights driven by data. AI systems are designed to "learn" and adapt to new data and be stable over the course of introducing data perturbations or employ model adaptation mechanisms. AI systems can adjust the underlying data-processing mechanisms based on the input they receive, which allows them to improve their performance and make more accurate predictions or decisions over time. This is often achieved through techniques such as machine learning, where algorithms are trained on a dataset and then used to make predictions or decisions without being explicitly programed to perform the task.8 Understanding how such datasets are selected, what data needs to be fed into AI model to achieve desired results, and how to prevent common pitfalls and ethical conundrums associated with the use of AI models requires additional training that might yet be lacking in the traditional training of the radiology/radiation oncology adjacent specialists. The scope of involvement of each member of the team when it comes to AI integration into the clinic continues to be determined as the field rapidly evolves. When it comes to the role of medical physicists in conjunction with AI, an open discussion of the exact responsibilities is still ongoing, and feedback is encouraged from all the members of the community. So, what can medical physicists do? They can use AI to enhance quality improvement and safety by analyzing medical data to identify trends, patterns, and outliers.9 This can lead to the identification of areas for improvement or potential safety hazards and help them enter the realm of Responsible AI. AI can also improve diagnostic and therapeutic techniques by enhancing the quality of medical imaging and automating image interpretation.10 Furthermore, AI can help in integrating diagnostics, personalized medicine, and theragnostics by analyzing large datasets to tailor treatment plans to individual patients.11 This can lead to more effective and personalized care. AI can also automate routine tasks in medical physics, such as treatment planning and QA processes, leading to increased efficiency.12 Lastly, AI techniques like machine learning and deep learning can be leveraged for research and development to analyze complex datasets, discover patterns, and develop innovative techniques for disease detection, treatment, and monitoring.13 Whether it involves developing AI-driven solutions like automated segmentation, dose calculations, addressing intricate problems in the clinic, or potentially even contributing to open-source AI initiatives, such activities will empower medical physicists to enhance their skills and make tangible contributions to the advancement of healthcare. Embracing AI not only fosters a sense of accomplishment but also opens doors to the world of `automation' and scaling that will pervade all technologies of the future. The AHAIBC committee is at the center of bringing the medical physicist forward by developing curriculum concepts, bootcamps, and engendering engagement for our society. Integration of AI into the realm of medical physics education is critical, especially considering the potential significance of incorrect AI usage or misapplication. The physicist is responsible for installing and commissioning the AI software, ensuring the modeling is not biased, performing continuing QA on the hospital data and processes, and establishing efficient resource management. Embracing education in AI offers new benefits for medical physicists as it is already revolutionizing various industries and professional practices and we need to be equally prepared. One way to engage and prepare healthcare professionals for the upcoming AI wave is to start with the roots of quality safety and assurance. To do this, we should enable a comprehensive QA program that encompasses all clinical operations related to medical fields including radiology, nuclear medicine, and radiation oncology. Ensuring the safe operation of hardware, software, clinical operation processes and machinery is of utmost importance and one of the most crucial responsibilities of a medical physicist. A Real AI approach can be highly beneficial in achieving the goal of safe clinical implementation. Understanding the potential and limitations of AI serves as a cornerstone for fostering engagement not only within our profession but with other healthcare providers. Continuous learning and participation in hands-on experience are essential components for navigating the complexities of AI applications within healthcare. Collaboration, networking, and exploring AI's purpose and impact are equally vital in this journey. Additionally, some physicists may choose personal projects, embracing challenges in small groups, and actively contributing to AI-focused teams to amplify the motivation and expertise of our field. Insights through personal and collaborative opportunities ultimately provide for and encourage professional growth and innovation within our medical physics field. Some medical physicists may be able to attend specialty meetings and conferences dedicated to AI which further enriches their knowledge base and provides them avenues for fruitful collaboration. There are successful educational programs such as the Radiological Society of North America Artificial Intelligence (RSNA AI)-certificate program.14 Interdisciplinary cooperation and inter-institutional collaboration for AI experts is of paramount importance for integrating AI into medical physicists' practice on a larger scale, and mechanisms enabling this collaboration should be provided to the community. In summary, the authors believe that being prepared for and embracing the changes that AI is already bringing at the current time will benefit our community, healthcare, patient care, and society at large immediately and for the future. We are at a critical juncture, which can be considered a fourth industrial revolution, where AI and automation are applied more broadly. Medical physicists have a pivotal role to play in this revolution. We need to position ourselves at the forefront of 'Real AI' and lead the charge in this exciting new era. It is time for action, and we can take the first steps with potentially just a few ABCs. All authors contributed their efforts in writing and editing this call for action. ChatGPT search engine has been utilized to provide additional background to the subject of matter for illustrative purposes. The authors appreciate members of the Ad. The authors declare no conflicts of interest. The content for this call for action has been edited with the help of large language models ChatGPT and Google NotebookLM.