Background: ChatGPT is a conversational large language model that has the potential to revolutionize knowledge acquisition. However, the impact of this technology on the quality of education is still unknown considering the risks and concerns surrounding ChatGPT use. Therefore, it is necessary to assess the usability and acceptability of this promising tool. As an innovative technology, the intention to use ChatGPT can be studied in the context of the technology acceptance model (TAM). Objective: This study aimed to develop and validate a TAM-based survey instrument called TAME-ChatGPT (Technology Acceptance Model Edited to Assess ChatGPT Adoption) that could be employed to examine the successful integration and use of ChatGPT in health care education. Methods: The survey tool was created based on the TAM framework. It comprised 13 items for participants who heard of ChatGPT but did not use it and 23 items for participants who used ChatGPT. Using a convenient sampling approach, the survey link was circulated electronically among university students between February and March 2023. Exploratory factor analysis (EFA) was used to assess the construct validity of the survey instrument. Results: The final sample comprised 458 respondents, the majority among them undergraduate students (n=442, 96.5%). Only 109 (23.8%) respondents had heard of ChatGPT prior to participation and only 55 (11.3%) self-reported ChatGPT use before the study. EFA analysis on the attitude and usage scales showed significant Bartlett tests of sphericity scores (P<.001) and adequate Kaiser-Meyer-Olkin measures (0.823 for the attitude scale and 0.702 for the usage scale), confirming the factorability of the correlation matrices. The EFA showed that 3 constructs explained a cumulative total of 69.3% variance in the attitude scale, and these subscales represented perceived risks, attitude to technology/social influence, and anxiety. For the ChatGPT usage scale, EFA showed that 4 constructs explained a cumulative total of 72% variance in the data and comprised the perceived usefulness, perceived risks, perceived ease of use, and behavior/cognitive factors. All the ChatGPT attitude and usage subscales showed good reliability with Cronbach α values >.78 for all the deduced subscales. Conclusions: The TAME-ChatGPT demonstrated good reliability, validity, and usefulness in assessing health care students’ attitudes toward ChatGPT. The findings highlighted the importance of considering risk perceptions, usefulness, ease of use, attitudes toward technology, and behavioral factors when adopting ChatGPT as a tool in health care education. This information can aid the stakeholders in creating strategies to support the optimal and ethical use of ChatGPT and to identify the potential challenges hindering its successful implementation. Future research is recommended to guide the effective adoption of ChatGPT in health care education.

The rise of superintelligent open-source generative AI (genAI) heralds both extraordinary potential and unprecedented risk, exemplified by the rapid emergence of DeepSeek as a global AI innovator. This perspective article examines the dual-edged nature of open source genAI technologies, highlighting their capacity to democratize innovation while exposing critical vulnerabilities. By providing affordable, high-performing, and openly available models like DeepSeek-R1 and DeepSeek-V3, this Chinese AI company has disrupted the proprietary dominance of Western AI giants. These advancements are expected to empower researchers in resource-limited settings, foster global collaboration, and enable breakthroughs across numerous fields. Open-source AI, as illustrated by DeepSeek, has the potential to redefine the technological landscape by making advanced capabilities accessible to underrepresented communities and encouraging ethical and inclusive innovation. However, the openness that drives such progress is fraught with existential risks. Superintelligent open-source models, accessible to anyone with minimal resources, lower barriers for misuse by malicious actors. From automated cyberattacks and disinformation campaigns to destabilizing critical infrastructures, the potential for harm is vast and unprecedented. Beyond immediate security concerns, these technologies threaten economic stability by displacing entire workforces and exacerbating inequalities, and they undermine human agency by enabling manipulation on an individual and societal level. This perspective seeks to explore the profound benefits of open-source superintelligent AI while critically addressing the urgent need for ethical and regulatory frameworks to mitigate its risks. The story of DeepSeek underscores the fragile balance between innovation and destruction in an era where technological progress outpaces safeguards. Humanity’s ability to harness the transformative power of open-source AI without succumbing to its destructive potential is not just a technological challenge—it is an existential imperative. This perspective argues for vigilance, responsibility, and global cooperation to ensure that the promise of open-source AI serves humanity rather than imperiling it.

The integration of artificial intelligence (AI) in healthcare education is inevitable. Understanding the proficiency of generative AI in different languages to answer complex questions is crucial for educational purposes. The study objective was to compare the performance ChatGPT-4 and Gemini in answering Virology multiple-choice questions (MCQs) in English and Arabic, while assessing the quality of the generated content. Both AI models’ responses to 40 Virology MCQs were assessed for correctness and quality based on the CLEAR tool designed for evaluation of AI-generated content. The MCQs were classified into lower and higher cognitive categories based on the revised Bloom’s taxonomy. The study design considered the METRICS checklist for the design and reporting of generative AI-based studies in healthcare. ChatGPT-4 and Gemini performed better in English compared to Arabic, with ChatGPT-4 consistently surpassing Gemini in correctness and CLEAR scores. ChatGPT-4 led Gemini with 80% vs. 62.5% correctness in English compared to 65% vs. 55% in Arabic. For both AI models, superior performance in lower cognitive domains was reported. Both ChatGPT-4 and Gemini exhibited potential in educational applications; nevertheless, their performance varied across languages highlighting the importance of continued development to ensure the effective AI integration in healthcare education globally.

The rapid digital transformation of education, driven by the widespread adoption of smart devices and online platforms, has ushered in the era of smart education. While this shift enhances learning experiences, it also introduces significant cybersecurity risks that threaten the confidentiality, integrity, and availability of educational resources, student data, and institutional systems. This survey examines how deep learning (DL) and computer vision (CV) techniques can enhance cybersecurity in smart education environments. By reviewing 202 peer-reviewed research papers published between January 2022 and June 2025 across leading publishers such as ACM Digital Library, Frontiers, Wiley Online Library, IGI Global, Nature, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, Taylor & Francis, Sage, BMC, and Google Scholar, the study explores the integration of these advanced technologies to address emerging threats. It highlights the use of DL in intrusion detection, anomaly detection, and biometric authentication to protect digital learning platforms. It also examines how CV techniques, such as facial recognition, behavioral analysis, and emotion detection, enhance security and foster adaptive learning environments. The survey also addresses key challenges, including data quality, model interpretability, computational costs, and ethical considerations. By identifying research gaps and proposing future directions, this survey offers valuable insights for researchers, educators, and policymakers aiming to develop robust, scalable, and ethical AI-driven cybersecurity solutions for smart education.

Background: The potential of generative artificial intelligence (genAI) tools, such as ChatGPT, is being increasingly explored in healthcare settings. However, the same tools also introduce significant cybersecurity risks that could compromise patient safety, data integrity, and institutional trust. This study aimed to examine real-world security breaches involving genAI and extrapolate their potential implications for healthcare settings. Methods: Using a systematic Google News search and a consensus-based approach among the authors, five high-profile genAI breaches were identified and analyzed. These cases included: (1) Data exposure in ChatGPT (OpenAI) due to an open-source library bug (March 2023); (2) Unauthorized data disclosure via Samsung’s (Samsung Group) use of ChatGPT (2023); (3) Logical vulnerabilities in Chevrolet (General Motors) AI-powered chatbot resulting in pricing errors (December 2023); (4) Prompt injection vulnerability in Vanna AI (Vanna AI, Inc.) which enabled remote code execution (2024); and (5) the deepfake technology used in a scam targeting the engineering firm Arup (Arup Group Limited), leading to fraudulent transactions (February 2024). Hypothetical healthcare scenarios were constructed based on the five cases, mapping their mechanisms to vulnerabilities in electronic health records (EHRs), clinical decision support systems (CDSS), and patient engagement platforms. Each case was analyzed using the Confidentiality, Integrity, and Availability (CIA) triad of information security to systematically identify vulnerabilities and propose actionable safeguards. Results: The analyzed cases of AI security breaches revealed significant risks to healthcare systems. Confidentiality violations included the potential exposure of sensitive patient records and billing information, extrapolated from incidents such as the ChatGPT data exposure and Samsung’s cases. These identified security breaches raised concerns about privacy violations, identity theft, and non-compliance with regulations such as Health Insurance Portability and Accountability Act (HIPAA). Integrity vulnerabilities were highlighted in Vanna AI's prompt injection flaw incident, with risks of altering patient records, compromising diagnostic algorithms, and misleading CDSS with erroneous recommendations. Similarly, logic errors identified in the Chevrolet case exposed potential risks of inaccurate billing, double-booked appointments, and flawed treatment plans within healthcare contexts. Availability disruptions, observed through system outages and operational suspensions following breaches like the ChatGPT and deepfake cases, can delay access to EHR systems or AI-driven CDSS. Such interruptions would directly impact patient care and create inefficiencies in administrative workflows. Conclusions: Generative AI presents a double-edged sword in healthcare, with transformative potential accompanied by substantial risks. Extrapolation of security breach cases in this study highlighted the urgent need for robust safeguards if genAI is implemented in healthcare settings. To address these vulnerabilities, healthcare institutions must implement strong security protocols, enforce strict data governance, and create AI-specific incident response plans. The balance between genAI-enabled innovation and protection of patient safety and data integrity trust requires proactive safety measures.

BACKGROUND ChatGPT is a conversational large language model that has the potential to revolutionize knowledge acquisition. However, the impact of this technology on the quality of education is still unknown considering the risks and concerns surrounding ChatGPT use. Therefore, it is necessary to assess the usability and acceptability of this promising tool. As an innovative technology, the intention to use ChatGPT can be studied in the context of the technology acceptance model (TAM). OBJECTIVE This study aimed to develop and validate a TAM-based survey instrument called TAME-ChatGPT (Technology Acceptance Model Edited to Assess ChatGPT Adoption) that could be employed to examine the successful integration and use of ChatGPT in health care education. METHODS The survey tool was created based on the TAM framework. It comprised 13 items for participants who heard of ChatGPT but did not use it and 23 items for participants who used ChatGPT. Using a convenient sampling approach, the survey link was circulated electronically among university students between February and March 2023. Exploratory factor analysis (EFA) was used to assess the construct validity of the survey instrument. RESULTS The final sample comprised 458 respondents, the majority among them undergraduate students (n=442, 96.5%). Only 109 (23.8%) respondents had heard of ChatGPT prior to participation and only 55 (11.3%) self-reported ChatGPT use before the study. EFA analysis on the attitude and usage scales showed significant Bartlett tests of sphericity scores (P<.001) and adequate Kaiser-Meyer-Olkin measures (0.823 for the attitude scale and 0.702 for the usage scale), confirming the factorability of the correlation matrices. The EFA showed that 3 constructs explained a cumulative total of 69.3% variance in the attitude scale, and these subscales represented perceived risks, attitude to technology/social influence, and anxiety. For the ChatGPT usage scale, EFA showed that 4 constructs explained a cumulative total of 72% variance in the data and comprised the perceived usefulness, perceived risks, perceived ease of use, and behavior/cognitive factors. All the ChatGPT attitude and usage subscales showed good reliability with Cronbach α values >.78 for all the deduced subscales. CONCLUSIONS The TAME-ChatGPT demonstrated good reliability, validity, and usefulness in assessing health care students’ attitudes toward ChatGPT. The findings highlighted the importance of considering risk perceptions, usefulness, ease of use, attitudes toward technology, and behavioral factors when adopting ChatGPT as a tool in health care education. This information can aid the stakeholders in creating strategies to support the optimal and ethical use of ChatGPT and to identify the potential challenges hindering its successful implementation. Future research is recommended to guide the effective adoption of ChatGPT in health care education.