Editorial: Artificial intelligence for early diagnosis of colorectal cancer.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

The integration of artificial intelligence (AI) tools in the workflow of scholarly writing, including scientific and medical writing, offers transformative advantages while raising critical ethical and practical concerns. Among the pros, AI substantially enhances efficiency by automating several time-consuming tasks such as literature review, data synthesis and text editing. In particular, tools such as ChatGPT, Grammarly, and SciSpace Copilot remarkably empower researchers when they draft, refine, and format manuscripts, increasing precision and speed. In addition, AI tools may foster inclusivity by assisting non-native English speakers with seamless translations and also enabling interdisciplinary collaboration, thereby hopefully democratizing access and boosting scientific communication and cooperation. However, the rapid adoption of novel AI tools brings significant challenges. First, there is a distinct risk of perpetuating biases in training datasets, and other key issues include ambiguity in authorship accountability and the potential erosion of critical thinking skills. In addition, AI tools could be purposefully misused to generate mock datasets and fraudulent papers (e.g. by paper mills), and this clearly poses a threat to academic integrity. This challenge is all too pressing given that traditional plagiarism detection tools often fall short against sophisticated AI-generated content. We hereby explore the pros and cons of AI on medical writing, poignantly leveraging Sergio Leone’s The Good, The Bad, and The Ugly movie as a metaphor. By providing general concepts as well as focusing on several key AI tools, we are hopeful this overview may prove useful to anyone wishing to conscientiously adopt AI tools for scientific writing. In addition, we make a compelling case for transparent guidelines, robust and freely available detection mechanisms, and ongoing critical oversight to ensure AI will be able to serve as a catalyst for innovation and dissemination without compromising the credibility of scholarly endeavors.

In today’s evolving world of work, Artificial Intelligence (AI) has emerged not merely as a technological tool but as a catalyst for rethinking how jobs are structured and performed (Bankins et al., 2024; Parker & Grote, 2022). Across diverse job families and organizational contexts, tools like generative AI (GAI) go beyond mere instruments for data analysis or structuring and can adopt increasingly far-reaching roles through rapid progress in generating new graphic or textual content or algorithmic decision making (Ferràs- Hernández, 2018; Walsh et al. 2019). Central to this symposium is the recognition that this changing nature of work due to digitalization, propelled by AI and related tools, calls for a fresh look at established theories of job design and strategies for human resource management. Traditionally, research has illuminated how fostering characteristics like autonomy, skill and task variety, or adequate demands affect employee motivation, satisfaction, and well-being and thereby further organizational outcomes (Oldham & Fried, 2016; Parker et al., 2017). Yet the introduction of AI – particularly generative AI – adds complexity: Who performs which tasks? How are decisions made, and by whom? Which skills remain uniquely human, and which may be augmented or even entirely adopted by AI? Such questions demand a more nuanced and strategic approach to designing work in the future that balances efficiency and innovation with psychological and ethical considerations. The four presentations in this symposium aim to contribute to theory building on how AI affects work in terms of work design, the interplay of employees and AI use, and work outcomes. In particular, they highlight potential risks or unique challenges when working with AI (e.g., in terms of less enriched work, lowered autonomy levels, threats to job identity or risks due to disclosure of use) and path the way for interventions to achieve benefits of such technologies at work. In detail, the empirical studies in this symposium illuminate these issues at three interconnected levels: 1. Task-Level Insights: Two contributions focus on the more granular task characteristics (e.g., autonomy, skill variety) in modern, AI-enhanced work contexts. They demonstrate how AI use influences the allocation of specific tasks when designing work roles and how the perception of task characteristics changes when AI is used in a decision-making task. 2. Job- and Identity-Level: One paper zooms out from individual tasks to jobs as a whole and explores how AI-driven change might disrupt employees’ job identity. This lens demonstrates how task changes cascade into bigger questions of role definition and identity. 3. Organizational Context: Finally, one presentation adopts the broadest perspective and investigates managerial and organizational factors that might shape employees’ willingness to disclose and ethically integrate AI tools. Taken together, these presentations point toward a future in which AI’s transformative potential hinges on thoughtful coordination across tasks, roles, and organizational systems. By combining established frameworks with new insights drawn from (quantitative and qualitative) surveys and experiments conducted in Germany, Austria, the United States, the United Kingdom, and Australia, these contributions illustrate how AI can enrich, rather than diminish, work and thereby strengthen human-centered organizations. By focusing on variables at different levels, the studies path the way for applications in practice, emphasizing the importance of individual approaches (e.g., training for job design, supporting self-efficacy), or on the organizational level (e.g., aligning HR practices, develop clear and supportive organizational policies and guidelines for the use of AI at work). Investigating the Influence of Work Design Perspective and AI Integration on Enriched Job Design Author: Tanja Bipp; Heidelberg University Author: Thea Ebert; Aalen University Collaborative Intelligence: Integrating Generative AI in Knowledge Work and its Impact on Job Design Author: Sabina Hodzic; University of Graz Author: Simon Grob; University of Graz Author: Bettina Kubicek; University of Graz Job Identity as Starting Point for SHRM in AI-Driven Organizational Change Author: Sophie Berretta; Ruhr University Bochum Author: Annette Kluge; Ruhr University Bochum Silent Adoption: Understanding the Drivers of Non-Disclosure of Generative AI Tools by Employees Author: Fangfang Zhang; Curtin University - Perth

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

The integration and usage of Artificial Intelligence (AI) tools in academic research writing has ushered in a paradigm shift, fundamentally transforming traditional research methods. AI tools have reshaped scholarly writing by enhancing accuracy, efficiency, and productivity. This study investigated the level of awareness, adoption, challenges, perceived benefits, and effectiveness of AI tools among research scholars from India and the United Arab Emirates (UAE), via a structured questionnaire administered to 174 research scholars across various academic disciplines in leading universities. The study identified various generative AI platforms used by scholars and explored key metrics, such as brainstorming, generation of ideas, grammar and style checking, literature discovery, paraphrasing, citation management, and plagiarism detection. The findings revealed a strong positive response related to awareness and adoption rates as well as significant differences based on geographic location, gender, academic discipline, and years of research experience. Despite acknowledging the perceived benefits and effectiveness of AI tools, concerns and challenges related to ethical usage, accuracy and bias, academic integrity, overdependence, and technical difficulties exist. The study provides various recommendations for AI use in academic writing, including the need for AI literacy training, institutional support, and policies related to ethical standards, and offers important implications for academic institutions, AI tools developers, research supervisors, and policy makers, fostering the effective integration of AI in academic research practices, following an ethical framework.

The integration of artificial intelligence (AI) tools into postgraduate supervision in higher education has accelerated globally, offering opportunities to enhance efficiency in research processes and academic mentoring. However, limited empirical evidence exists regarding the risks and challenges of this integration, particularly within Global South contexts such as South Africa. This study investigates the challenges associated with the use of AI tools in postgraduate supervision from a South African perspective. Anchored in a constructivist paradigm, the study adopts a qualitative research design, employing semi-structured interviews with 20 purposively selected participants—10 postgraduate students and 10 supervisors from faculties that are actively integrating AI into supervisory practices. Data were analysed thematically using qualitative content analysis. The findings identify six key challenges: increasing dependence on AI that may erode students’ critical thinking and originality; insufficient digital literacy and institutional support; financial and sustainability constraints; the questionable reliability and accuracy of AI-generated outputs; ethical dilemmas and limited cultural contextualisation; and resistance to technological change among supervisors. While acknowledging the potential of AI to enhance research productivity and the quality of supervision, the study cautions against its uncritical adoption, which may compromise academic integrity, creativity, and equity. It recommends institutional strategies, including subsidised AI access, structured training on ethical and critical AI use, the embedding of digital literacy in postgraduate curricula, and the fostering of collaboration with AI developers to ensure culturally relevant systems. A context-sensitive approach is essential to balance the affordances of AI with the preservation of human intellectual agency and critical scholarly engagement in postgraduate supervision.

The integration of generative artificial intelligence (AI) tools in art and design has disrupted the traditional creative landscape, leading to debates on the legitimacy of AI-generated art and the emergence of new markets such as non-fungible tokens (NFTs). The US Copyright Office’s February 21, 2023, ruling withdrawing copyright protection for AI-generated comic artwork, while protecting the accompanying text and arrangement, highlights the contested nature of AI art and suggests that significant human intervention in the creative process will be required for monetization. Whether considered content interpolation or content creation, AI generative content for the creation of art and design is here with human-AI collaboration. To explore the potential of AI tools in creative practice, this study introduced students in a digital art course to Craiyon and Midjourney generative AI tools, with DALL-E 2 selected as the primary tool due to its varied output. The students were tasked with selecting a preferred prompt from one tool and then reproducing the output from both tools. The results revealed significant variations in replicating the outputs of different AI tools and limited exploration of prompt engineering, leading to restrictions in the iterative process of artmaking. The students agreed that generative AI tools are not a substitute for human creativity and should be used for final projects. The study demonstrates the potential and limitations of integrating AI tools in art and design and suggests the need for further research in developing effective prompt engineering strategies.

Reading proficiency is a fundamental skill in higher education, yet many undergraduate students in Nigeria struggle with comprehension due to limited support and outdated instructional methods. The integration of artificial intelligence (AI) tools into education offers new possibilities for personalized and adaptive learning, but little research has examined their specific impact on reading development in Nigerian undergraduate settings. This study explores the perception and experiences of undergraduate students regarding the use of AI tools such as chatgpt, Rewordify and Read theory for enhancing students’ academic reading proficiency. Using a qualitative research approach, semi-structured interviews were conducted with eighty students from diverse disciplines who had prior exposure to AI tools. The thematic analysis framework was used to identify key themes across participant responses. Emerging themes included personalized and self-paced learning, vocabulary support and comprehension, increased engagement, and provision academic confidence. However, challenges such as over reliance on AI generated content, inconsistent usage habit, and digital literacy gaps were also identified. Finding suggested that AI tools, implemented with appropriate guidance, equitable access, and curriculum alignment, can significantly enhanced students’ reading confidence, comprehension, and motivation. The study contributed to the body of literature on AI in education by offering context-specific insight for technology integration in literacy instruction. It highlighted the need for inclusive, students centered strategy to promote reading proficiency in resource-constrained academic environment. Future research should explore teachers respective, long-term learning outcomes, and the integration of AI tools across diverse linguistic and institutional contexts to further validate and expand on these findings.

The integration of Artificial Intelligence (AI) tools in higher education has emerged as a transformative force, reshaping pedagogical practices across diverse disciplines. This research examined the awareness, application, challenges, and effectiveness of utilizing AI tools for teaching research methods among lecturers at the University of Calabar, Nigeria. The study was anchored on Technology Acceptance Model (TAM). The study answered 3 research questions and tested 4 hypotheses as a guide for the study. Drawing on a descriptive survey research design, data was collected from a sample of 206 lecturers (115 male; 91 female) representing 115 departments from 24 faculties that teach their students directly or anchor research methods/its equivalence for other departments in the same faculty. A structured questionnaire, in form of modified Likert four -type scale, validated for face, content and assessed for reliability through a trial test with coefficients ranging from 0.77 to 0.81, served as the primary data collection instrument. Lecturers' awareness of AI tools for research methods instruction, their active integration of AI tools, and challenges encountered were systematically explored. Data was analysed using descriptive (mean, standard deviation), and inferential (independent t-test, one-way ANOVA) statistics. Findings revealed that awareness and utilization levels varied across sexes and departments with male lecturers and science departments scoring higher mean utilization values. While results showed that academic staff generally have awareness of the use of AI in research methods, awareness levels between male and female lecturers, and among academic disciplines were not significantly different. From the findings too, lecturers generally perceived the utilization of AI tools for effective instruction to be inadequate. It was found that challenges related to technical issues, lack of skills, faculty training, restricted access, and costs persist. The study underscores the need for comprehensive faculty training programs, institutional support for technology infrastructure, and clear communication to address faculty concerns, emphasizing the importance of faculty engagement in the AI integration process. Ultimately, this research contributes to the discourse on AI-enhanced pedagogy in the Nigerian higher education context and provides practical recommendations for enhancing research methods instruction through AI tools.

As the authors of "The Comparative Diagnostic Capability of Large Language Models in Otolaryngology," 1 we would like to extend our gratitude to Maniaci et al. for their thoughtful comments on our study.Their insights contribute meaningfully to the ongoing discussion regarding the integration of artificial intelligence (AI) tools, such as ChatGPT, into clinical practice. 2 We agree that the potential of AI in health care is both promising and complex.Like Maniaci et al., we emphasize the need to approach these innovations with "tempered hope."Although ChatGPT and similar tools offer significant diagnostic potential, they also carry inherent limitations that require cautious, informed use to ensure patient safety and adherence to established clinical guidelines.The introduction of medically oriented AI models like DoximityGPT and AmbossGPT presents an important opportunity to further evaluate how AI aligns with clinical standards, offering a broader scope for research and refinement.Additionally, we appreciate Maniaci et al.'s emphasis on using standardized evaluation tools.The implementation of instruments such as the AI Performance Index (AIPI) in future studies can help ensure the validity and reliability of AI-generated outputs.We also echo their suggestion for the development of additional standardized tools that can rigorously assess the content generated by AI models.Establishing such frameworks will be critical to maintaining consistency and quality as AI tools become more integrated into medical practice.

This study explores teachers’ readiness and perceptions regarding the integration of artificial intelligence (AI) tools in education. The study was conducted among 3,848 Estonian teachers. Given AI’s transformative potential in enhancing teaching effectiveness, automating administrative tasks, and supporting personalized learning, it is critical to assess whether teachers are equipped to effectively utilize these technologies. Utilizing a comprehensive framework informed by human-centered approaches, this research investigates teachers’ familiarity, attitudes, perceived usefulness, and readiness to implement AI-driven tools in educational settings. A survey conducted among teachers reveals a balanced perspective characterized by interest, openness, and awareness of both opportunities and potential risks associated with AI. Findings indicate significant predictive relationships between perceived usefulness and readiness, as well as attitudes toward AI tools and actual adoption intentions. Teachers’ confidence and perceived relevance of AI in educational contexts emerge as key factors facilitating their willingness to engage with AI technology. Conversely, factors such as AI anxiety and limited training opportunities are identified as barriers to effective implementation. The study underscores the need for targeted professional development programs, ethical guidelines, and policy support to enhance teachers’ readiness and facilitate the responsible integration of AI into educational practices.

Simple SummaryThe combination of digital pathology (DP) with artificial intelligence (AI) offers faster, more accurate, and more comprehensive diagnoses, resulting in more precise individualized treatment. As this technology is constantly evolving, it is critical to understand the current state of AI applications in DP. Thus, it is necessary to analyze AI patent applications, assignees, and leaders in the field. In this study, five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, were searched using key phrases, such as DP, AI, machine learning, and deep learning, and 523 patents were shortlisted based on the inclusion criteria. Our data demonstrated that the key areas of the patents were whole-slide imaging, segmentation, classification, and detection. In the past five years, an increasing trend in patent filing has been observed, mainly in a few prominent countries, with a focus on the digitization of pathological images and AI technologies that support the critical role of pathologists.The integration of digital pathology (DP) with artificial intelligence (AI) enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. As technology is advancing rapidly, it is critical to understand the current state of AI applications in DP. Therefore, a patent analysis of AI in DP is required to assess the application and publication trends, major assignees, and leaders in the field. We searched five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, from 1974 to 2021, using keywords such as DP, AI, machine learning, and deep learning. We discovered 6284 patents, 523 of which were used for trend analyses on time series, international distribution, top assignees; word cloud analysis; and subject category analyses. Patent filing and publication have increased exponentially over the past five years. The United States has published the most patents, followed by China and South Korea (248, 117, and 48, respectively). The top assignees were Paige.AI, Inc. (New York City, NY, USA) and Siemens, Inc. (Munich, Germany) The primary areas were whole-slide imaging, segmentation, classification, and detection. Based on these findings, we expect a surge in DP and AI patent applications focusing on the digitalization of pathological images and AI technologies that support the vital role of pathologists.

The use of corpora and artificial intelligence (AI) tools into education marks a significant evolution in teaching and assessment methodologies. Historically, Arabic corpora have been pivotal in linguistic research, expanding notably through scholarly publications and symposia since the late twentieth century. However, the scarcity of specialized resources such as parallel corpora and the prevalence of web-generated Arabic corpora have restricted thorough empirical studies of authentic Arabic texts and their application in educational settings. Additionally, while the integration of AI into Arabic pedagogy has increased, its application in Arabic/English legal translation remains scarce and underdeveloped. To address this gap, this paper aims to showcase the use of corpus tools and AI to advance research-driven and blended-learning approaches in Arabic/English legal translation. It uses advanced corpus tools and diverse corpora (large datasets) available on Sketch Engine, including the Leeds Monolingual and Parallel Corpora of Arabic and English Countries’ Constitutions and the UN Parallel Corpora. These resources, integral to research and instruction, have supported the MA translation programmes at the University of Leeds for five years. Moreover, ChatGPT 4 is applied as an interactive tool in designing teaching activities around legal documents such as contracts and agreements. This method not only mitigates the lack of specialized corpora in this genre but also explores new possibilities for using AI in education. The paper utilises a descriptive methodology to showcase how monolingual and parallel corpora can enhance interactive legal text learning, highlighting the educational and scientific innovation. Theoretical frameworks like Vinay and Darbelnet’s (1995) contrastive linguistic analysis and Vermeer’s Skopos theory (1978) provide foundational insights for designing and interpreting pedagogical activities involving corpus-based tools. Although the study illustrates the tools’ potential to enrich the learning environment and meet diverse educational demands, it acknowledges AI's limitations in ensuring accuracy and stylistic precision in complex legal texts. The scope of this study does not include an exhaustive evaluation of pedagogical impacts or a comparison with traditional methods, suggesting the need for further research to validate these findings and expand upon them.

The rise of artificial intelligence (AI), particularly ChatGPT, has transformed educational landscapes globally. Moreover, the Beijing Consensus on Artificial Intelligence and Education and the ‘Pact for the Future’ propose that AI can support UNESCO in achieving development goals, especially focusing on SDG 4, which emphasizes quality education. Thus, this study investigates undergraduate students’ familiarity with and attitudes toward AI tools, as well as their perceived risks and benefits of using AI tools at a private university in China. An explanatory sequential mixed-method design was employed with an online survey of 167 students, followed by a qualitative analysis of open-ended responses. Data were analyzed using the one-sample Wilcoxon signed-rank test and thematic analysis, supported by SPSS and ATLAS.ti 25. The findings revealed that students demonstrated moderate familiarity with AI tools, particularly ChatGPT and willingness to use them in coursework. Positive attitudes toward AI’s value in education were evident, although concerns such as dependence and reduced independent thinking, algorithmic bias and ethical concerns, accuracy and information quality, data security, and privacy concerns were observed among students. Moreover, students generally viewed AI positively and perceived AI integration as inevitable and becoming common in academic settings. Students were concerned that the misuse of AI by their teachers was minimal and trusted their teachers to use AI effectively in teaching. Students also perceived AI’s benefits, such as personalized learning, efficiency and convenience, career and skill development, and support for independent learning. This study contributes to the discourse on AI integration in higher education by highlighting students’ nuanced perceptions and balancing their benefits with potential risks. The findings of this study were limited by the small sample size and institution. Future research should explore diverse contexts to develop comprehensive AI implementation frameworks for higher education.

BackgroundThe therapeutic relationship is a professional partnership between clinicians and patients that supports open communication and clinical decision-making. This relationship is critical to the delivery of effective mental health care. The integration of artificial intelligence (AI) into mental health care has the potential to support accessibility and personalized care; however, little is known about how AI might affect the dynamics of the therapeutic relationship.ObjectiveThis study aimed to ascertain how physicians anticipate AI tools will impact the therapeutic relationship in mental health care.MethodsWe conducted 42 in-depth interviews with psychiatrists and family medicine practitioners to investigate physician perceptions regarding the impact of AI on mental health care.ResultsPhysicians identified several disruptions from AI use, noting that these tools could impact the dyad of the patient-physician relationship in ways that are both positive and negative. The main themes that emerged included potential disruptions to the therapeutic relationship, shifts in shared decision-making dynamics, and the importance of transparent AI use. Participants suggested that AI tools could create efficiencies that allow for relationship building as well as help avoid issues with miscommunication during psychotherapeutic interactions. However, they also expressed concerns that AI tools might not adequately capture aspects of the therapeutic relationship, such as empathy, that are vital to mental health care. Physicians also raised issues related to the impact that AI tools will have on maintaining relationships with patients.ConclusionsAs AI applications become increasingly integrated into mental health care, it is crucial to assess how this integration may support or disrupt the therapeutic relationship. Physician acceptance of emerging AI tools may be highly dependent on how well the human elements of mental health care are preserved.