Artificial Intelligence as a Catalyst: A Case Study on Adaptive Learning in Programming Education

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

Purpose: This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.Methods: A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.Results: The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.Conclusion: The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools.

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.

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 recent years, generative artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize collaborative learning experiences. This study investigates the role of generative AI in facilitating innovative collaboration among Saudi university students engaged in group projects within digital learning environments. As higher education institutions increasingly adopt digital tools and platforms, understanding how generative AI can enhance creativity, teamwork, and overall learning outcomes becomes paramount.Utilizing a mixed-methods approach, this research employs a comprehensive questionnaire distributed to a diverse sample of Saudi university students. The questionnaire is designed to assess participants’ experiences with generative AI tools, their perceptions of these tools' effectiveness in enhancing collaboration, and the impact on their creativity and problem-solving abilities during group projects. The survey includes both closed-ended questions for quantitative analysis and open-ended questions for qualitative insights, allowing for a nuanced exploration of the role of generative AI in educational settings.The findings reveal that a significant majority of participants (approximately 78%) reported that generative AI tools, such as AI-driven brainstorming applications and collaborative writing platforms, have had a positive influence on their group project experiences. Students highlighted that these tools fostered a more inclusive environment, enabling team members to contribute ideas more freely and creatively. Moreover, 65% of respondents noted improved communication and coordination among team members, attributing this enhancement to the real-time collaborative features provided by generative AI tools.Additionally, qualitative feedback from open-ended questions indicated that students appreciated the ability of generative AI to generate diverse perspectives and ideas, which enriched the brainstorming process and led to more innovative project outcomes. Participants expressed that the use of AI tools reduced the cognitive load associated with project management, allowing them to focus more on creative problem-solving and critical thinking. However, some concerns were raised regarding the potential for over-reliance on AI-generated content, emphasizing the need for a balanced approach that combines human creativity with AI assistance.This study contributes to the growing body of literature on the integration of artificial intelligence in education, particularly in the context of collaborative learning. The implications of these findings suggest that incorporating generative AI tools into the curriculum can significantly enhance the collaborative capabilities of Saudi university students, fostering a culture of innovation and creativity in digital learning environments. As educational institutions continue to navigate the complexities of digital transformation, understanding the potential of generative AI as a catalyst for collaboration will be essential for fostering effective and engaging learning experiences.In conclusion, this research highlights the transformative potential of generative AI in enhancing group projects among Saudi students, offering valuable insights for educators and policymakers aiming to leverage technology for improved collaborative learning outcomes.

The paper presents the results of the research study conducted on students at the Faculty of Humanities and Social Sciences in Zagreb (FHSS), Croatia, at the end of 2024, focusing on their experiences with artificial intelligence (AI) tools, and its use in academic activities, students' trust in AI, and the future use of AI by students. While over 81 % of respondents reported using AI tools for study purposes, more than 89% did not receive any education about AI and expressed a need for such education. Generative AI tools for text creation are the most frequently used type of AI tools, but students still require additional knowledge to use them effectively. AI tools are also utilized for seeking advice, finding information, being available 24/7, and assisting in writing student papers, though not yet on a massive scale. Teachers remain the most trusted source for information and for recommending AI tools. Despite known shortcomings of AI tools such as content errors, potential costs and ethical or privacy concerns, students expressed their intention to continue using AI tools in the future.

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.

This study presents results of a systematic literature review (SLR) of the responsible use of artificial intelligence (AI) tools in higher education, identify patterns of ethical and irresponsible use, and propose a conceptual framework for predicting ethical AI adoption. Following PRISMA guidelines, was conducted on 60 peer-reviewed studies published between 2022 and 2026, sourced from Google Scholar. Studies were mapped against four research questions addressing AI tools used, their applications, reported unethical practices, and predictive modelling approaches. Results reveal that general AI, generative AI tools, and large language models dominate higher education contexts, primarily deployed for personalized learning, academic work, and teaching. Irresponsible practices were documented in one-third of studies, including academic integrity breaches (13.33%), algorithmic bias, and privacy violations. Critically, no existing study developed a real-time predictive model capable of monitoring ethical AI use, despite four studies demonstrating predictive modelling capabilities for other purposes. This study addresses a significant gap by proposing a novel conceptual framework that integrates AI tool deployment, user behaviour, governance measures, and predictive analytics to forecast ethical outcomes. The framework provides higher education institutions with a pathway toward data-informed, proactive governance of AI technologies.

This paper discusses the integration of artificial intelligence (AI) tools in education, delineating their potential to transform pedagogical practices alongside the challenges they present. Generative AI models like ChatGPT, had a disruptive impact on teaching and learning, due to their ability to create text, images, and sound, revolutionizing educational content creation and modification. However, nowadays the educational community is polarized, with some embracing AI for its accessibility and efficiency thus advocating it as an indispensable tool, while others cautioning against risks to academic integrity and intellectual development. This document is designed to raise awareness about AI tools and provide some examples of how they can be used to improve education and learning. From an educator’s perspective, AI is an asset for curriculum development, course material preparation, instructional design and student assessment, while reducing bias and workload. For students, AI tools offer personalized learning experiences, timely feedback, and support in various academic activities. The Turkish Biochemical Society (TBS) Academy recommends educators to embrace and utilize AI tools to enhance educational processes, and engage in peer learning for better adaptation while maintaining a critical perspective on their utility and limitations. The transfer of AI knowledge and methods to the teaching experiences should complement and not replace the educator’s creativity and critical thinking. The paper advocates for an informed embrace of AI, AI fluency among educators and students, ethical application of AI in academic settings, and continuous engagement with the evolving AI technologies, ensuring that AI tools are used to augment critical thinking and contribute positively to education and society.

Every day Artificial Intelligence (AI) technologies are more deeply inte-grated into education in general and into the process of a foreign language teaching in particular. Methodologists are developing language-specific methods of teaching lin-guistic university students foreign language aspects, types of speech activities, culture and translation based on specific AI tools. Most of these studies have a narrow focus and are aimed at solving individual and sometimes discrete pedagogical tasks. Such integration of AI into the educational process allows us to create additional conditions for extracurricular language practice for students and contributes to the further devel-opment of their foreign language communicative competence in all the diversity of its components. At the same time, the majority of methodological studies have a narrow focus and are oriented towards solving individual and sometimes discrete educational problems related to the development of specific speech skills or the formation of lexical, grammatical or phonetic speech skills of students. At the present stage, the language teaching potential of AI technologies allows to systematically consider the issue of in-tegrating AI into the process of language and methodological pre-service teachers’ training, which has not yet been the subject of a separate study. The aim of the paper is to develop a structural model for the integration of AI technologies in language and methodological pre-service teachers’ training. The model proposed by the authors for the integration of AI technologies in lan-guage and methodological pre-service teachers’ training consists of five blocks: lin-guistic disciplines, narrow professional disciplines, methodological disciplines, peda-gogical practice and research work. Within each block there are sub-blocks aimed at solving specific pedagogical/research tasks. The authors show which AI tools and tasks can be used in the context of teaching the disciplines from each block. The methodo-logical content of the blocks is based on the typology of feedback from generative AI as applied to teaching a foreign language, including educational-social, information-reference, methodological, analytical, evaluative and conditionally creative types. The methodological content of the blocks is based on the typology of feedback from generative AI in relation to foreign language teaching. The research is based on systematic and expert approaches using theoretical (comparative, component and complex analysis of AI tools, classification, synthesis, generalisation, modelling) and empirical (observa-tions and interviews with teachers and students) research methods. The research materials included scientific articles (Article and Review) published in academic journals indexed in the Scopus and Web of Science databases (Q1 and Q2), as well as Russian academic journals from the list of the Higher Attestation Commission of the Russian Federation on pedagogical sciences (С1 and С2). The subject of the research in the works used was the development of practical methods for teaching a foreign language (types of speech activity and aspects of language) using specific artificial intelligence tools.

Introduction: Artificial intelligence (AI) is revolutionizing medical education by enhancing learning experiences, improving knowledge retention, and providing personalized guidance. Through interactive simulations, virtual tutors, and adaptive learning platforms, AI-powered solutions help students in preclinical education grasp difficult ideas. Thus this study aims to examine the impact of AI on preclinical medical education students' academic performance, engagement, and readiness for clinical training among preclinical medical students, examining its applicability, efficacy, potential, and limitations. Which is done by outlining the objectives and evaluating the effectiveness, opportunities, and challenges of integrating artificial intelligence (AI) tools in medical education among Year 1 and Year 2 MBBS students in a private medical university, Malaysia. Methodology: The study involved 300 sample population, including 152 Year 1 and 148 Year 2 students. A cross-sectional questionnaire-based study was conducted, with a sample size of 184 with a 95% confidence level. Data were collected through online surveys and analysis using Microsoft Excel and IBM SPSS version 23.0.Result: All 184 preclinical students used AI tools in their medical education, mainly relying on ChatGPT. About 84.2% are familiar with AI in this context. The effectiveness of AI in improving learning was rated from 1 to 3, with most students scoring AI as 4 or 5 in problem-solving, decision-making, critical thinking, and inspiring new ideas, indicating a high perception of its effectiveness. Many believe AI supports traditional teaching. However, concerns exist about over-reliance on technology (83.2%) and loss of critical thinking skills (77.7%). Also, 42.9% rated their worries about AI's impact on clinical decision-making skills as a 3. Conclusion: Most preclinical students know about AI in medical education and believe it helps improve learning. AI assists students in solving problems, making decisions, encouraging critical thinking, and generating new ideas. However, concerns felt about much dependence on technology and weaken critical thinking skills in medical education. Students believe that AI will not entirely harm clinical decision-making skills. In summary, AI offers both advantages and disadvantages in medical education.

Focusing on the generative artificial intelligence (AI) tools using large language models, the present research explores the individual factors impacting the low-income Koreans’ attitudes toward generative AI tools utilized to search for information (e.g., ChatGPT, Google Gemini, etc.). Specifically, we first examine whether low-income Korean individuals’ prior AI experience, perceived usefulness of AI, and general attitude toward AI influence their attitudes toward generative AI tools (RQ1). Second, we examine whether the prior AI experience affects the attitude toward generative AI tools via the perceived usefulness of AI (RQ2). Third, we examine whether the prior AI experience influences the attitude toward generative AI tools via the general attitude toward AI (RQ3). Fourth, we examine whether the relationship between prior AI experience and attitude toward generative AI tools is serially mediated by the perceived usefulness of AI and general attitude toward AI (RQ4). To answer the research questions, we conducted a hierarchical multiple regression analysis and a mediation analysis using the low-income Koreans who were aware of the generative AI tools (n = 770). The results indicate that (1) both the perceived usefulness of AI and general attitude toward AI are positively associated with the attitude toward generative AI tools; (2) the perceived usefulness of AI mediates the relationship between the prior AI experience and the attitude toward generative AI tools; (3) the indirect effect of prior AI experience on the attitude toward generative AI tools, via the general attitude toward AI, is not statistically significant; and (4) the prior AI experience influences the attitude toward generative AI tools through a sequential process of the perceived usefulness of AI and general attitude toward AI. The findings provide important implications to enhance the attitude toward generative AI tools.

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.

This workshop aims to design advanced empathic user interfaces for in-vehicle displays, particularly for high-level automated vehicles (SAE level 3 or higher). Incorporating model-based approaches for understanding human emotion regulation, it seeks to enhance the user-vehicle interaction. A unique aspect of this workshop is the integration of generative artificial intelligence (AI) tools in the design process. The workshop will explore generative AI’s potential in crafting contextual responses and its impact on user experience and interface design. The agenda includes brainstorming on various driving scenarios, developing emotion-oriented intervention methods, and rapid prototyping with AI tools. The anticipated outcome includes practical prototypes of affective user interfaces and insights on the role of AI in designing human-machine interactions. Through this workshop, we hope to contribute to making automated driving more accessible and enjoyable.