Redefining Student Achievement

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

PurposeThe purpose of this study is to critically investigate the effects of artificial intelligence (AI)-driven learning environments on learner autonomy, knowledge co-construction and digital equity in Thailand’s online education context. It aims to analyze how algorithmic learning tools and AI-based systems shape learners' educational experiences, particularly focusing on whether these technologies enhance or constrain learner autonomy and collaborative interactions. Additionally, it evaluates ethical and social implications, particularly the digital divide, algorithmic biases and the transparency of AI-mediated educational processes. The study seeks to provide actionable insights and recommendations for policymakers, educators and developers to optimize AI integration for inclusive lifelong learning.Design/methodology/approachThis research adopts a qualitative case study methodology involving in-depth, semi-structured interviews, participant observations and detailed document analysis from 30 diverse participants, including urban and rural learners, educators and instructional designers. Employing actor-network theory (ANT) and posthumanist theoretical frameworks, the study critically examines interactions between human and non-human actors within AI-driven educational environments. This approach allows for comprehensive exploration of participants' lived experiences, perceptions and interactions with AI technologies. Triangulation of multiple data sources ensures depth and reliability, providing nuanced insights into how AI systems influence learner autonomy, collaborative learning processes and educational equity.FindingsThe study identifies AI-driven learning platforms as having dual impacts on learner autonomy, simultaneously enabling greater self-directed learning through personalized guidance and real-time feedback while imposing algorithmic constraints that restrict intellectual exploration. AI-facilitated knowledge co-construction effectively organizes and structures peer interactions yet often results in depersonalized, transactional communication lacking deep human engagement. Furthermore, significant digital inequities are evident, with rural learners disproportionately disadvantaged due to limited technological infrastructure and algorithmic biases embedded within AI-driven platforms. These inequities highlight a critical need for inclusive, transparent and ethical design practices to ensure equitable access and meaningful learner agency.Research limitations/implicationsThe qualitative, context-specific nature of the case study limits the generalizability of findings beyond Thailand’s online educational environment. The rapidly evolving nature of AI technologies suggests that findings reflect a specific temporal context, necessitating continuous research updates. Participant selection bias may have influenced findings, with participants potentially holding strong pre-existing opinions on AI-driven education. Future studies should consider longitudinal analyses to understand long-term impacts, comparative cross-cultural research to validate findings and deeper exploration of algorithmic biases, fairness and ethical considerations in AI-driven educational tools, particularly how they affect diverse socio-economic groups.Practical implicationsPractically, this research underscores the necessity of designing AI-driven learning platforms that genuinely empower learner autonomy and critical thinking rather than restrict or overly dictate educational pathways. Educational practitioners and technology developers should implement transparent AI decision-making processes, enabling learners to understand and critically evaluate algorithmic recommendations. Policies must prioritize digital infrastructure expansion and digital literacy programs to reduce urban-rural digital disparities. Furthermore, pedagogical practices should integrate human-centric approaches to complement AI tools, ensuring that AI technologies serve to enhance, rather than replace, essential human interactions within collaborative learning environments.Social implicationsThe study emphasizes that AI-driven education holds significant potential for democratizing lifelong learning by offering personalized and adaptive learning experiences. However, it also warns of the risks associated with reinforcing existing socio-economic and digital inequalities, particularly between urban and rural learners. Addressing algorithmic biases and digital divides through targeted policy interventions, infrastructural investments and inclusive educational practices is critical for fostering equity and inclusivity in education. By ensuring that AI platforms are accessible and beneficial for all learner demographics, societies can leverage technology as a tool for broad-based educational empowerment and social development rather than a mechanism perpetuating exclusion.Originality/valueThis research contributes original insights into the nuanced impacts of AI-driven learning on learner autonomy, collaborative interactions and digital equity within the Thai educational context – areas relatively underexplored in existing literature. By uniquely integrating actor-network theory and posthumanist perspectives, it offers a sophisticated understanding of the interplay between humans and technologies in educational settings. The empirical findings deliver crucial evidence-based recommendations for educators, policymakers and developers aiming to ethically and inclusively leverage AI in education. Ultimately, the study enriches ongoing discussions on balancing technological advancement with human-centric, equitable and socially responsible educational practices.

Art education has undergone significant transformation in the era of digitalization. With the advancement of Artificial Intelligence (AI) technology, there is tremendous potential to enhance the art learning experience. This research aims to elucidate how AI technology can be effectively utilized to improve art education in the context of digitalization. The study aims to introduce AI technology and explore its potential applications in enhancing the art learning experience. In the context of art learning experience, AI technology can provide broader access to digital art collections, online galleries, and virtual museums. Students can explore various artworks and cultures through digital platforms, thereby enriching their understanding and appreciation of art. The research findings reveal that AI technology can offer personalized guidance to students in developing their artistic skills. Intelligent AI systems can analyze students' artworks, provide tailored suggestions, and assist in enhancing their technical and creative abilities. In conclusion, the integration of AI technology in art education holds immense potential to elevate students' learning experiences. By expanding access, providing personalized guidance, and creating interactive experiences, AI technology enriches art learning in the era of digitalization. This is expected to provide greater insights and understanding of how AI technology can be harnessed to enhance the art learning experience in the context of digitalization. By optimizing the potential of AI, art education can become more inclusive, personalized, and engaging.

The article is devoted to the study of the application of artificial intelligence technologies in law enforcement activities in Ukraine and the world. The purpose of the article is to analyze the international and domestic experience of using digital technologies in law enforcement activities, to identify the shortcomings of the legal regulation of the use of artificial intelligence in this area, to provide proposals for its improvement in Ukraine. It was found that one of the countries that is actively expanding the use of artificial intelligence and successfully introducing relevant technologies into public administration is China. The artificial intelligence system which is used in China called «Zero Trust». The purpose of using this system is to prevent the commission of corruption offenses. It is emphasized, that the disadvantage of the «Zero Trust» system is that artificial intelligence, identifying the cause of corruption, does not indicate the algorithms and principles of this process. At the same time, the experience of using artificial intelligence in the field of preventing and combating corruption in Ukraine is considered when carrying out automated verification of persons declarations of authorized to perform state and local government functions. Ways of improving legal regulation when conducting financial control of declarations using artificial intelligence technologies are proposed. It has been found that artificial intelligence is widely used in the USA to predict the prevalence and state of crime. It was emphasized that Ukraine also has experience in the application of artificial intelligence technologies in law enforcement activities. Artificial intelligence is used in the development and implementation of departmental specialized intelligent software, the implementation of operative and investigative actions (for the recognition of video objects observation by comparing images in social networks, etc). It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity. Modern developments in the field of artificial intelligence and advanced digital technologies, which are implemented in foreign countries to prevent and fight crime, are of great importance for the improvement of legal regulation in this field in Ukraine.

The increasing integration of artificial intelligence (AI) technologies into educational systems has introduced multifaceted and complex challenges across pedagogical, ethical, and institutional domains—particularly in contexts where the process of digital transition intersects with the cultural and structural constraints of a given society. This study investigates the perceptions and evaluations of secondary school teachers regarding AI-based technologies in teaching and student academic performance assessment processes. Employing a cross-sectional survey design, the study involved the participation of 317 secondary school teachers working in Tehran. The research data collection instrument consisted of 25 items across five latent constructs, whose validity and reliability were evaluated and confirmed through exploratory and confirmatory factor analyses. To test the study’s hypothesized model, structural equation modeling (SEM) techniques were applied. The analysis revealed that none of the four predictor variables examined had a statistically significant effect on the dependent variable—namely, the behavioral intention of teachers to adopt AI technologies. The findings indicate the presence of a significant structural disconnect between teachers' attitudinal awareness of AI and their motivational readiness to actively engage with it. The results suggest that teachers' engagement with AI technologies is influenced more by systemic, institutional, and cultural ambiguities than by individual beliefs. The study underscores the importance of developing and applying context-sensitive models in the investigation of AI technology acceptance in educational settings. Furthermore, it opens new avenues for future research into the mediating and conditional factors affecting teachers’ professional behavior in relation to AI integration.

PurposeThe introduction of artificial intelligence (AI) technology has had a substantial influence on the retail industry. However, AI adoption entails considerable responsibilities and risks for senior managers. In this study, the authors developed an evaluation and selection mechanism for successful AI technology adoption in the retail industry. The multifaceted measurement and identification of critical factors (CFs) can enable retailers to adopt AI technology effectively and maintain a sustainable competitive advantage.Design/methodology/approachThe evaluation and adoption of organisational AI technology involve multifaceted decision-making for management. Therefore, the authors used the analytic network process to develop an AI evaluation framework for calculating the weight and importance of each consideration. An expert questionnaire survey was distributed to senior retail managers and 17 valid responses were obtained. Finally, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to identify CFs for AI adoption.FindingsThe results revealed five CFs for AI adoption in the retail industry. The findings indicated that after AI adoption, top retail management is most concerned with factors pertaining to business performance and minor concerned about the internal system's functional efficiency. Retailers pay more attention to technology and organisation context, which are matters under the retailers' control, than to external uncontrollable environmental factors.Originality/valueThe authors developed an evaluation framework and identified CFs for AI technology adoption in the retail industry. In terms of practical application, the results of this study can help AI service providers understand the CFs of retailers when adopting AI. Moreover, retailers can use the proposed multifaceted evaluation framework to guide their adoption of AI technology.

The convergence of blockchain, artificial intelligence (AI), and cloud computing is catalyzing a paradigm shift in developing secure, intelligent, and scalable digital infrastructures. This triad of technologies is increasingly utilized to improve performance, transparency, and trust in engineering-driven and socio-technical environments. This study systematically reviews the evolution, integration strategies, and applications of blockchain, AI, and cloud computing in digital ecosystems. The analysis is based on 108 peer-reviewed studies spanning the years 2012 to 2025. A comprehensive literature analysis was conducted to identify trends, synergies, and sector-specific implementations of these systems. The review explores how their integration supports real-world engineering and operational use cases. Blockchain contributes to decentralized architectures, secure data exchange, and identity verification. AI supports adaptive behavior, autonomous decision-making, and predictive analytics. Cloud computing offers the scalable infrastructure necessary for deployment. Key challenges addressed include interoperability, latency, security trade-offs, and resource allocation. Use cases in digital finance, supply chain management, and industrial automation demonstrate the effectiveness of this integration in building resilient, ethically aligned, and high-performance infrastructures. The findings offer valuable insights and technical considerations for engineers and architects seeking to design next-generation cyber-physical systems that are secure, intelligent, and socially responsive. Clinical Trial Number Not applicable. This article accentuates the extensive use of blockchain, cloud computing, and AI principles, emphasizing their crucial significance in the contemporary digital environment and paths in which blockchain technology may be employed to automate financial transactions, enhance the security of personal data and records, facilitate cross-border transactions, verify identities, execute smart contracts, utilize cryptocurrencies, and manage supply chains. This research systematically assesses the development and practical application of blockchain, cloud computing, and artificial intelligence technologies over the past decade. This study primarily focuses on monitoring the progress, practical applications, and benefits of blockchain technology in the context of financial transactions. This article probes the symbiotic relationship between artificial intelligence, cloud and blockchain technology, analyzing the evolution and fundamental concepts of their integration from 2012 to 2025. This paper also presents a detailed analysis of 108 research works acquitted over the past decade. The study provides a comprehensive examination of the current state and fundamental principles related to the integration of artificial intelligence, cloud, and blockchain technology. This paper offers a comprehensive analysis of the major obstacles confronted by blockchain technology, examining its wide-ranging applications over several industries and its socko role in transaction systems and the domain of cryptocurrencies.

In addition to creativity, modern landscape design also needs some auxiliary tools to predict the design effect, so as to ensure the design effect before the completion of the landscape. With the development of computer network and electronic information technology, human society has entered the information age, and artificial intelligence technology has been applied to all walks of life. With the continuous development of artificial intelligence, artificial intelligence has existed in all aspects of our lives and has a great impact on our lives. At the same time, through the achievements in recent years, artificial intelligence gradually affects other fields such as design. Therefore, combined with the current situation of landscape design auxiliary system in China, this paper introduces artificial intelligence and information technology into it, and makes the landscape design auxiliary system get further development with the help of artificial intelligence and information technology. This paper first introduces artificial intelligence, analyzes the advantages and development trend of automatic informatization of landscape design auxiliary system, and finally discusses the application of artificial intelligence technology in landscape design auxiliary system.

Objectives: The objective of this scientific paper is to shed light on the challenges facing the legislative industry in the field of artificial intelligence (AI) technologies. Specifically, it aims to anticipate and predict the difficulties related to creating an independent legal personality for AI tools, enabling them to assume rights and obligations, as well as establishing criminal responsibility and punishment in cases where AI technologies commit crimes punishable by law. Methods: To achieve the stated objectives, this paper employs a theoretical and analytical approach. It involves a comprehensive review and analysis of existing literature, legal frameworks, and case studies related to the legal implications of AI technologies. Additionally, hypothetical scenarios and potential challenges are discussed to illustrate the complexities involved in creating legal frameworks that address the unique nature of AI tools and their impact on society. Results: The analysis reveals several key challenges facing the legislative industry in the field of AI technologies. These challenges include the conceptualization of an independent legal personality for AI tools, determining the rights and obligations associated with such personality, and establishing mechanisms for holding AI technologies accountable for their actions. Furthermore, the paper explores the difficulties in imposing criminal responsibility and punishment on AI technologies in cases where they commit crimes. Conclusion: In conclusion, this scientific paper highlights the complexities and challenges associated with creating legal frameworks for AI technologies. By anticipating and predicting these challenges, it provides valuable insights for legislators, policymakers, and legal professionals involved in shaping the regulatory landscape for AI. Moving forward, it is essential to address these challenges proactively and develop legal frameworks that strike a balance between promoting innovation and safeguarding societal interests in the era of artificial intelligence.

The acceptance of technology at the higher educational level has been a significant discussion, with little attention on the gender dynamics on the acceptance of artificial intelligence (AI ) tools by senior lecturers. This study delved into a detailed analysis of the gender dynamics in the discussion of technology acceptance mainly AI tools, in foreign language (FL) education. Quantitative study approach was adopted in the process, and survey design was implemented. Data was collected using structured digital questionnaire, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A total of ninety-five (95) male senior lecturers and one hundred and three (103) female senior lecturers participated in the study. Analysis was conducted using relevant statistical measures. The results showed disparities in the attitudes and views of senior lecturers towards artificial intelligence (AI) technologies in the context of FL education, greatly influenced by gender. In relation to usage, male senior lecturers have higher positive reactions (61.06%) in comparison to their female counterparts (46.6%). However, in relation to the assumption that AI technologies improve the performance of learners, 69% of male senior lecturers agree with this notion, but a substantially greater percentage of 72.81% of female senior lecturers hold the same perspective. Moreover, there exists a little disparity in the level of proficiency in using AI technologies across genders. Specifically, 56.84% of male senior lecturers see it as uncomplicated, while 61.16% of their female counterparts share the same sentiment. The gender discrepancy that is most notable pertains to the perceived level of ease in using artificial intelligence (AI) technologies during foreign language (FL) lessons. The data reveals that a majority of male senior lecturers, calculated as 69.48%, see the use of these tools very easy. In contrast, a much higher proportion of female senior lecturers, 86.41%, share the same perception. This discrepancy highlights a notable disparity in confidence levels between the two genders. These results together emphasise the changing gender dynamics in the acceptance of technology, interrogating conventional assumptions and underscoring the need for customised support systems to guarantee fair and efficient integration of artificial intelligence (AI) technologies in foreign language instruction among senior lecturers.

Artificial intelligence (AI) technology, as one of the most sophisticated science and technology in today's world, is increasingly being used to production and life, particularly in the manufacturing business. it demonstrates how artificial intelligence technology is used in mechanical manufacturing, namely in defect detection, quality inspection, enhancing workplace safety, and other areas. Artificial intelligence technology is becoming increasingly important in people's lives as it becomes more widely used in people's daily lives, such as the widespread use of smart dishwashers and smart sweepers, which are the products of the fusion of artificial intelligence and the mechanical manufacturing industry. Indeed, artificial intelligence technology has been widely utilized in the mechanical manufacturing business, which not only ensures production precision, but also enhances job productivity and workplace safety. The rise of artificial intelligence has caused significant changes in the manufacturing industry as a whole. Without exception, the manufacturing industry must rely on AI technology to accomplish automation and intelligent development, as well as to improve productivity. Using artificial intelligence to categorize mechanical components, we may propose parts from a based solely on an image or CAD model. To find a necessary component in a machines we must currently browse through a catalogue and be able to discern which part you want based on the available possibilities and your understanding of the catalogue. There are serial numbers to memorize since a single digit or character change might indicate a different sort of part. The algorithm will choose which sections are the best and will significantly facilitate our search.

The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.

Artificial Intelligence (AI) technologies are becoming increasingly pervasive throughout the business industry. The rapid diffusion of this technology in organizations is due to the intrinsic benefits that can be realized by those who successfully adopt them into their business practices. Those organizations that fail to adopt AI technologies will be at a competitive disadvantage over those that do. As can be found throughout IS literature, successful adoption depends on users’ attitudes and behavioral intentions toward AI technology. Therefore, given the rapid diffusion of AI and the need for organizational success, it is important to research and understand the determinants of users’ attitudes and behavioral intentions toward adopting AI technologies.This current research proposed an extended Technology Acceptance Model (TAM) that includes variables identified within literature to be determinants of users’ attitudes and behavioral intentions toward adopting AI technologies. The proposed model (PM) was then tested by conducting a cross-sectional survey of students in a business college. The survey results were analyzed to determine which of the proposed variables increase TAM’s ability to explain attitudes and predict behavioral intentions toward adopting AI technologies. Further analysis also identified which of the proposed variables significantly influence business students’ attitudes and behavioral intentions to adopt AI technologies.The study found support that perceived usefulness was a significant determinant of students’ attitudes towards AI technologies. However, contrary to previous findings, the study found perceived ease of use negatively influenced attitudes towards AI technologies. From these findings, this study sought to provide insights that can inform educators and practitioners of curriculum and training design topics that will increase the likelihood of AI technology adoption. Specifically, the results suggested that educators and curriculum and instructional design practitioners should focus on usefulness and not on simplicity of use. The study also recommended that future research extend the current model with moderators and mediators such as innovation resistance and perceived risk.<br>

Currently, the development of technologies affects all spheres of society. Artificial intelligence technologies are intensively developing and are beginning to be actively used to solve various problems both at the everyday and at the scientific level. Accordingly, there are discussions in the pedagogical circle about the possibilities of using artificial intelligence to solve educational tasks: from finding educational material, translating educational material into another language, creating a curriculum, computer presentation for an educational session, creating a computer program or project to communication in another language with an IA-assistant (Artificial Intelligence). That is, a scientific and pedagogical worker can delegate the performance of certain tasks to artificial intelligence technologies (but with further verification of the completed tasks by artificial intelligence technologies) and reduce his time for preparing for educational classes. Increasingly, scientists emphasize the possibility of using artificial intelligence technologies in the educational process and the need to train scientific and pedagogical workers to use artificial intelligence. Accordingly, the purpose of the article was to analyze the concept of "artificial intelligence" and describe existing scientific approaches to the use of artificial intelligence in the educational process. The methodology of the research was the analysis of the concept of "artificial intelligence" by both Ukrainian and foreign scientists, analysis of scientific approaches, explanation, comparison and systematization of directions, advantages, disadvantages of using artificial intelligence in education. The definition of the concept of "artificial intelligence" by both Ukrainian and foreign scientists is presented. We defined the concept of "artificial intelligence" as an information technology that ensures the performance of certain complex intellectual tasks. Examples of artificial intelligence technologies that can be used in the educational process include: Anima, Grammarly, CENTURY, IntelliMetric, API DeepL, OpenArt, GodeRabbit, etc. The areas of use of artificial intelligence in education are described and highlighted: individualized training, intelligent training systems, automated assessment, group training. The advantages and disadvantages of using artificial intelligence in the educational process are characterized. The results of the study emphasize the importance of studying and using artificial intelligence in the educational process, because this technology is developing rapidly, has prospects for further active development and is used in various spheres of human activity: scientific, medical, military, pedagogical, industrial, household, etc.

In recent years, with the rapid development of Brain-Computer Interface (BCI) and Artificial Intelligence (AI) technologies, a trend of increasing integration between the two has emerged. In some practical applications, BCI has been combined with AI to enhance the overall performance of systems, including usability, user experience, and satisfaction, especially in terms of intelligent capabilities. However, this technological integration also introduces new or exacerbates existing ethical risks, such as neural privacy breaches, cross-domain misuse, and unclear system responsibility attribution. This paper discusses the novel or more severe ethical challenges arising from the fusion of BCI and AI technologies, as well as measures and strategies to address these ethical issues, calling for the establishment of more comprehensive ethical guidelines and governance frameworks. It is hoped that this paper will contribute to a deeper understanding and reflection on the ethical risks and corresponding regulations related to the integration of BCI and AI technologies.