Generative Artificial Intelligence (GenAI) has emerged as a transformative and disruptive force in education and society, with the potential to both create and correct misinformation. In education, misinformation manifests at three levels: the individual (when students hold misconceptions or inaccurate beliefs); the community (when groups of individuals share the same misconceptions or inaccurate beliefs); and the system (when educational policies and practices are not based on scientific evidence). We conducted a systematic scoping review to identify existing challenges and opportunities (...) for misinformation generation, sharing, and correction across these levels in education, as well as inform future research in this area of work. Our results indicate three approaches to GenAI and misinformation in education: identifying misconceptions, prebunking, and (re)producing misinformation, with most research conducted at the individual level. We conclude with suggestions for future research and practice. (shrink)

Safeguarding GenAI models against threats and aligning them with security requirements is imperative yet challenging. This chapter provides an overview of the security landscape for generative models. It begins by elucidating common vulnerabilities and attack vectors, including adversarial attacks, model inversion, backdoors, data extraction, and algorithmic bias. The practical implications of these threats are discussed, spanning domains like finance, healthcare, and content creation. The narrative then shifts to exploring mitigation strategies and innovative security paradigms. Differential privacy, blockchain-based provenance, quantum-resistant (...) algorithms, and human-guided reinforcement learning are analyzed as potential techniques to harden generative models. Broader ethical concerns surrounding transparency, accountability, deepfakes, and model interpretability are also addressed. The chapter aims to establish a conceptual foundation encompassing both the technical and ethical dimensions of security for generative AI. It highlights open challenges and lays the groundwork for developing robust, trustworthy, and human-centric solutions. The multifaceted perspective spanning vulnerabilities, implications, and solutions is intended to further discourse on securing society’s growing reliance on generative models. Frontier model security is discussed using Anthropic proposed approach. (shrink)

In this article, I argue that semi-autonomous avatars relying on generative artificial intelligence to replicate or represent real human persons—GenAI avatars—open a new type of responsibility gaps, which I call “proxy gaps”. Proxy gaps refer to situations when we cannot hold anyone morally responsible for the outcomes of GenAI avatars, because the representation relationship between avatars and humans is shaped by multimodal Large Language Models (LLMs). In addition to epistemic gaps by AI avatars discussed in the literature, I (...) argue that GenAI avatars also open control gaps—where no one really controls the output of the avatar. I introduce the “proxy-control paradox” to explain why control gaps arise: in trying to achieve improved control over the desired outcome of their avatar, humans need to delegate control over the process leading to that outcome to the GenAI technology. Together, the epistemic and control gaps complicate the two criteria traditionally used for moral responsibility, resulting in a proxy gap by GenAI avatars. Despite inherent proxy gaps, I argue that, under certain circumstances, we can still rightfully hold individuals morally responsible for the outcome of their GenAI avatars. I detail four conditions pertaining to human understanding and LLM personalization, as well as the right to veto and outcome control, which, taken together, can ground individual moral responsibility for the outcome of personal GenAI avatars. (shrink)

Purpose The increasing adoption of generative artificial intelligence (GenAI) tools in university education has raised significant ethical concerns regarding academic integrity and fairness. This study aims to address these concerns by reviewing existing models and frameworks for ethical GenAI use and proposing a preliminary roadmap to establish ethical standards for GenAI use in higher education. Design/methodology/approach This study reviews current models and frameworks for ethical GenAI use, identifying their strengths and limitations. Based on this literature review (...) and an approach combining interpretative phenomenological analysis and a hybrid phenomenological qualitative method, a six-phase roadmap is proposed, consisting of awareness and understanding, policy development, curriculum integration, technology and infrastructure, continuous evaluation and adaptation and collaboration and outreach. Findings This paper emphasizes the need for clear policies, interdisciplinary curriculum integration, robust technological infrastructure and ongoing stakeholder collaboration. Practical recommendations are provided for each phase of the roadmap, offering strategic guidance for universities to navigate the ethical complexities of GenAI implementation. Originality/value The proposed roadmap serves as a foundational step for developing policies and guidelines that ensure GenAI supports academic integrity and fosters innovative learning environments. Future research will focus on empirical validation and refinement of the roadmap to enhance its applicability and effectiveness in diverse educational contexts. (shrink)

Generative artificial intelligence (GenAI) is a subset of artificial intelligence (AI) that can generate content such as texts, images, videos, sounds, etc. While GenAI tools have been utilized in various contexts, their utilization in the academic context is still a controversial topic. Scholars observed that many universities have banned GenAI due to the potential for unethical usage. In this opinion article, we promote the utilization of GenAI tools as feedback agents to alleviate challenges in conventional feedback (...) provision. Feedback is a valuable learning source, especially in writing instructions. However, conventional feedback provision is challenging due to the increase in teacher’s workload and social irritation issues in peer feedback. Utilized ethically, GenAI tools can alleviate these challenges. A few critical considerations are briefly presented. (shrink)

Techno-solutionism (Ts) amplifies academic integrity issues endemic to using Generative AI in Open and Distance education (ODE). It (Ts) induces in Higher education (HE) the disposition that technology can and should be employed in every aspect of teaching, learning, and assessment. The prevalence of Ts in ODE and the consequence of undermining academic integrity is found in the surge in published papers. A 2023 study by Nature of over 1600 scientists reports that nearly 30% use GenAI to write papers, (...) and 5% use it in grant applications. A 2024 Originalityai report on 13,000 analysed papers on arXiv indicates a sudden increase of 200.42%, 44.68% and 22.04%, respectively, in Computer Science, Physics and Mathematics in papers published in Jan. 2019 and those published in Nov. 2023. Apr. 2024 paper on arXiv notes Computer Science to be the most prolific in LLM usage with up to 17.5% increase. The problem this study addresses is balancing the challenges and opportunities of GenAI in ODE. Is there a limit to AI use in ODE? How can we upsurge academic integrity in the age of AI? How might we rethink the culture of publish or perish in the age of AI? Adopting the scoping review methodology and discourse analysis, this paper analyses the gains and pains of AI in ODE. Leveraging the critical theory of technology, it recommends clear green and red lines on using AI in teaching, research, and assessment. (shrink)

The use of generative artificial intelligence (GenAI) in the educational and research activities of university stakeholders is not inherently a violation of academic integrity. However, the output of AI may contain violations of academic integrity. This chapter explores two questions: “How can AI be used in educational and research activities in honest way?” and “How familiar are university stakeholders with the dangers of using AI in their educational and research activities?” We analyze the functioning of AI as a tool (...) for a non-specialist user. First, we investigate ways to avoid violations of academic integrity when creating a text product in a dialogue mode with AI. We gave ChatGPT several tasks that “reveal” its shortcomings, including the fabrication of a list of references, lack of references in the text, incorrect use of the Creative Commons license, recognition of text authorship, and distortion of facts. The dialogue is conducted from the perspectives of students and teachers. We analyze each response and provide recommendations for AI users designing prompts. Second, we analyze the results of a survey of Ukrainian academics awareness of possible violations of academic integrity in texts created by AI. The survey highlights the main challenges with AI in educational and scientific texts. (shrink)

The complex and intricate features of legal language (referred to as _legalese_) have long been discussed in the literature, and scholars have advocated the use of plain language to enhance comprehension. The advent of ISO 24495-2 [ 23 ] on plain language and legal communication has brought about important changes in legal language drafting. According to the new standards, complex legal information must be conveyed clearly, effectively and accurately to become accessible to laypeople. In the current AI-driven mediascape, researchers have (...) also experimented whether and how GenAI (more precisely, general chatbots) are capable of easifying legal convoluted language, albeit often reaching unconvincing results. This contribution is aimed at exploring whether current targeted GenAI tools (such as Notebook LM and ChatLaw) are able to disentangle the complexities of legal discourse, thus offering plain language alternatives. For this purpose, a host of contract clauses and statutory provisions are entered into the above-mentioned GenAI platforms, where the ISO 24495-2 is also uploaded for reference purposes. The chatbots are prompted to easify the texts inputted and re-write them in plain language by scanning and applying the principles expressed in the ISO standards. To allow for the evaluation of the AI-driven outputs, literature-driven easified versions of the legal extracts considered are accounted for. In this way, the scholarly recommended plain language versions of the texts are compared with the GenAI-powered outputs. The findings reveal very satisfactory results, with chatbots generating only a few minor inaccuracies. Namely, shortcomings mostly revolve around parenthetical information (which give rise to syntactical discontinuities), some non-simplified expressions (e.g., “unless the context suggests otherwise”), and the use of the passive form. In most of the cases, however, chatbot-based texts appear as more comprehensible and accessible than the literature-driven easified versions, thereby confirming the huge strides forward made by AI. (shrink)

In just a few years, generative artificial intelligence (GenAI or GAI) has distinctly impacted numerous facets of human activity, particularly in how information is processed and transformed into knowledge. As centers for knowledge (co)creation and appropriation, universities face the opportunity and challenge of navigating a de facto integration of this promising yet disruptive technology. This study examines how instructors and students form their common-sense understanding of GenAI and endorse actions that resolve the paradoxical tensions associated with GenAI. (...) Utilising an integrated social representational and paradoxical lens, 12 semi-structured interviews were conducted with instructors and students, and the data were analysed using reflexive thematic analysis. Instructors and students share social representations of GenAI as an innovative yet demanding tool, a revolutionary progress, and a normative challenge. Nevertheless, instructors and students locate themselves differently within diverse paradoxical dimensions (e.g., openness vs vigilance) of these shared representations. These paradoxes further shape not only their identities as agentic and reflective tech adopters but also their expectations and recommendations for balancing managed and organic GenAI adoption. With aid from these findings, higher education institutions can establish sound and balanced GenAI-induced organisational and digital transformation processes and practices by mindfully fostering ambidextrous behaviours and strategies. These can then enrich existing expertise and perspectives among diverse university stakeholders and support renegotiation of boundaries between contradictory demands. (shrink)

The potential of generative artificial intelligence (GenAI) has sparked ethical debates about its use in academic research, with some authors describing such practices as research misconduct. However, most international journals and conferences now encourage transparent disclosure of GenAI assistance in research manuscripts. Despite these policies, many scholars still refrain from acknowledging their use of these tools. This study examines the psychological factors that influence researchers’ willingness to disclose their use of GenAI. After rigorous validation of a survey (...) instrument with 777 participants, data from an additional 1,514 respondents across multiple countries were analysed using structural equation modelling (SEM). The results indicate that internal justification mechanisms, such as moral reasoning and perceived legitimacy, shape disclosure behaviour, whereas feelings of conflict between personal values and actions promote transparency. In contrast, a tendency to normalise GenAI use or rely on prior ethical behaviour reduces the likelihood of disclosure. Furthermore, moral identity moderates some of these relationships, reinforcing ethical self-consistency. The findings contribute to understanding how psychological and ethical factors jointly shape responsible research conduct and offer practical guidance for promoting transparent AI use in scholarly communication. (shrink)

This article critically examines the moral arguments for restrictive policies regarding student use of generative AI in higher education. While existing literature addresses various concerns about AI in education, there has been limited rigorous ethical analysis of arguments for restricting its use. This article analyzes two main types of moral arguments: those based on direct difference‐making (where individual university actions have measurable impacts) and those centered on non‐difference‐making participation (where symbolic participation in harmful systems matters regardless of direct impact). Key (...) concerns examined include environmental harm from AI energy consumption, exploitative labor practices in AI development, and privacy risks. Through careful analysis, the article argues that these arguments face significant challenges when examined in depth. The difference‐making arguments often fail to establish that individual university actions meaningfully contribute to claimed harms, while the non‐difference‐making arguments lead to impractical conclusions when applied consistently across university operations. Rather than supporting blanket restrictions, the analysis suggests universities should focus on fostering responsible AI engagement through ethical guidelines, licensed tools, and education on responsible use. The article concludes that a balanced approach considering both moral and practical factors is more effective than restrictive policies in addressing ethical concerns while preserving educational benefits. (shrink)

Generative AI agents (GenAIs) powered by Large-language models (LLMs) have emerged as prominent technological advancements. As these sophisticated systems permeate diverse sectors ranging from business to entertainment, their capability to handle moral queries becomes a focal point of exploration. This study investigates how users perceive Delphi, a GenAI trained to respond to moral queries (Jiang et al., 2025). Participants were instructed to interact with the agent, implemented either as a humanlike robot or a web client, to assess its moral (...) competence and trustworthiness. Both agents received high scores for moral competence and perceived morality, yet fell short by not offering justifications for their moral decisions. Despite being deemed trustworthy, participants were hesitant about relying on such systems in the future. This study offers an initial evaluation of an algorithm with moral competence in an embodied human-like interface, paving the way for the evolution of ethical robot advisors. (shrink)

Generative AI (GenAI) grading and tutoring systems are rapidly entering higher education, promising increased efficiency and personalized learning experiences. We believe this technology should be viewed with caution, particularly in the case of philosophy. In this article, we first outline the nature and purported benefits of these systems. Next, we examine the economic motivations driving their adoption within institutions, such as reducing labor costs. Finally, we argue that these motivations, while compelling to administrators, may fail to consider potential negative (...) consequences. We present six pedagogical reasons to be critical of the widespread implementation of GenAI in education: (1) limitations on teacher efficacy; (2) increased student alienation; (3) the undermining of educators’ duty of care; (4) the outsourcing of core teaching tasks; (5) questions regarding the accuracy and utility of GenAI graders; and (6) the risk of implementing GenAI as a misguided solution to real problems in education. (shrink)

This response engages the special issue on the mind–technology problem in the age of generative AI by situating its central concerns within a longer philosophical genealogy. While the editors productively frame contemporary debates about large language models and GenAI as successors to the Cartesian mind–body problem, this commentary argues that the underlying structure of the problem emerges much earlier, most notably in Plato’s Phaedrus. Plato’s reflection on writing as a disruptive cognitive technology already articulates the key tensions that animate (...) current discussions of AI: whether technologies extend or undermine cognition, how they transform knowledge practices, and how they reconfigure agency and responsibility. More importantly, the Phaedrus destabilizes the opposition between mind and technology by reinscribing writing within thought itself, revealing cognition as dependent on technical inscription. Drawing on Derrida’s reading of Plato, this response suggests that the mind–technology problem is not simply about how technologies affect minds, but about how the concept of mind has always been constituted through technological metaphors and practices. Seen in this light, GenAI intensifies—rather than inaugurates—a longstanding philosophical entanglement. (shrink)

Situated within the scholarship on how artificial intelligence (AI) affects and is affected by society, this article offers a distinctly empirically grounded, methodologically rigorous, theoretically rooted, and conceptually novel understanding of the great Generative AI (GenAI) quandary in higher education (HE). Drawing on sociological discourse on the purposes of education, it analyses exploratory qualitative data produced from semi-structured individual interviews (n = 10) and focus groups (n = 5) conducted with staff across Faculties at a Russell Group university. Serving (...) as both educators and academic integrity officers, the research participants were uniquely positioned to provide valuable insights into how GenAI use speaks to the notion of what a university is for. These insights are embodied in the themes of pedagogical conundrum, assessment anxiety, and employability dilemma. Premised on socialisation processes, the pedagogical conundrum stems from GenAI’s potential to expand the scope of learning, a potential often eclipsed by the ethical considerations and unintended consequences associated with its (mis)use. Assessment anxiety emanates from staff awareness of the numerous ways in which GenAI tools could be misused by students in HE appraisals without detection, thereby making authenticity an urgent priority in evaluation processes to sustain the legitimacy of HE qualifications. The employability dilemma arises from the subjectification function of HE, which is perturbed when staff acknowledge the need to upskill the future workforce for an increasingly GenAI-powered labour market yet feel unable to fully dismiss lingering concerns that overuse of GenAI could deskill prospective graduates in core employability skills. Woven together, these themes generate a conceptually innovative continuum of emerging opportunities and ongoing challenges of embedding GenAI in HE practices that embodies a new discourse in the form of the great GenAI quandary. From this theoretical development, the article considers possible adaptations required for GenAI’s assimilation into the institutional framework for teaching and learning in HE institutions. Overall, the article offers timely insights into the ongoing and rapidly evolving impact of GenAI on the social contract between education and society. (shrink)

This article explores the ethical considerations surrounding using Generative Artificial Intelligence (GenAI) in preserving movable cultural heritage, focusing specifically on its application in restoration, reconstruction, and recreation. While GenAI offers innovative methods for preserving and recreating cultural heritage, it also presents significant ethical challenges. The article reviews current studies on the role of GenAI in heritage preservation alongside relevant ethical guidelines and proposes a tailored ethical framework for its application in movable heritage. The framework addresses several critical (...) ethical concerns, including cultural integrity and sensitivity, accuracy and authenticity, intellectual property rights, sustainability and social impact, and governance and ethical accountability. The article adopts a systematic methodology, combining a comprehensive literature review, thematic analysis, and expert evaluation to develop practical guidelines that ensure GenAI enhances rather than compromises movable heritage’s cultural and historical value. This ethical framework advocates for the responsible use of GenAI, emphasising the importance of collaboration with cultural experts and relevant communities, ensuring transparency in the use of data, and promoting robust ethical governance in AI-driven heritage preservation projects. Ultimately, the framework aims to guide practitioners and institutions in using GenAI in ways that respect and uphold the integrity of cultural heritage while also utilising the benefits of this cutting-edge technology as a tool for better assistance of heritage preservation. (shrink)

Two types of privacy policies—privacy assurance and personalization declaration—are commonly utilized in online cookies. This study aims to compare the effectiveness of imbalanced privacy policies (focusing on either privacy assurance or personalization declaration) with balanced privacy policies (which emphasize both equally) in the context of GenAI applications. Drawing from cognitive dissonance theory and goal-framing theory, we investigated how these (im)balanced privacy policies affect customer ethical judgment and purchasing responses, particularly in relation to normative and hedonic goals. Our mixed-methods research, (...) which includes one qualitative study and three empirical studies, consistently indicates that imbalanced privacy policies outperform balanced ones in promoting the ethical acceptability of GenAI content. Moreover, the positive effects of imbalanced privacy policies diminish when customers prioritize higher normative goals but strengthen when they focus on hedonic goals. Mediation analyses further revealed that the ethical acceptability of GenAI content mediates the relationship between (im)balanced privacy policies and customer purchase intentions and amounts. Overall, these findings provide insights for companies using GenAI to effectively design their cookie policies, enhancing customer ethical judgment and purchase responses. (shrink)

This study examines the attitudes of academic staff toward the use of generative artificial intelligence (GenAI) in higher education teaching. Focusing on faculty members at a college in Israel, the study explores how attitudes are associated with self-reported levels of technological pedagogical content knowledge (TPACK) self-efficacy, personal innovativeness in IT (PIIT), and two perceptual constructs drawn from the Technology Acceptance Model (TAM): perceived usefulness and perceived ease of use. A cross-sectional survey design was used, with data collected from 84 (...) lecturers. Correlation and hierarchical regression analyses revealed that both PIIT and TPACK self-efficacy were positively associated with attitudes toward GenAI, with perceived usefulness and ease of use emerging as significant mediators. Specifically, the effect of TPACK self-efficacy on attitudes was fully mediated by these perceptual variables, while PIIT retained a significant direct effect. The findings suggest that faculty attitudes toward GenAI are shaped by both individual dispositions and evaluative judgments about the tool’s pedagogical relevance and usability. This research contributes to the growing literature on AI in education by providing empirical evidence on the attitudinal antecedents relevant to faculty engagement and may inform institutional strategies that support the thoughtful and differentiated integration of GenAI in teaching. (shrink)

This chapter explores student perspectives on the impact of Generative AI (GenAI) in higher education. By examining narratives across various disciplines—healthcare, business, human resources, law, English literature, and computing—it seeks to understand the benefits and challenges GenAI presents to students, as well as its broader implications for higher education and future careers. Qualitative data from student surveys from various fields was gathered to showcase student perspectives through a narrative analysis. This approach captures diverse views on how GenAI (...) might affect learning and research, which can offer insights into students’ experiences in an AI-influenced academic environment. The findings highlight that students are keenly aware of GenAI’s impact on their education, often more so than their educators or instructors. While some students are optimistic about GenAI’s potential, others express concern over the ethics of its use, its effects on traditional learning, and implications on their professions. This chapter highlights the necessity of including student voice in discussions about GenAI’s role in higher education. As primary stakeholders, their insights are vital in shaping educational policies and practices in response to GenAI’s growing presence. The findings also contribute to ongoing debates around the role of higher education in preparing students for the changing requirements of future careers in an increasingly AI-driven world. (shrink)

This chapter analyses academic integrity policies and practices at nine public universities in Croatia. Generative AI (GenAI) is in use in Croatian higher education, with the number of research projects related to artificial intelligence growing across different fields. Despite the term academic integrity not being used widely in Croatia, the Codes of Ethics and Disciplinary Codes of the universities address academic integrity, misconduct, and ethical values concerning faculty, employees and students. The findings conclude that all universities need to update (...) their disciplinary procedures and reflect on the opportunities and threats of GenAI, as none of them directly link the use of advanced digital tools with academic misconduct. Universities must develop practices that teach students and staff members about GenAI and a culture of academic integrity. (shrink)

In the twenty-first century, ever more aspects of our technologically mediated lifeworld have become infused with AI or ‘smart’ artefacts. This includes smartphones we control with gestures, smart...

Generative AI (GenAI) models have revolutionized various industries, enabling unprecedented creativity and efficiency. However, evaluating their suitability for specific tasks is crucial for maximizing their potential and minimizing their risk. This evaluation requires a thorough understanding of task requirements, model capabilities, and potential limitations. Factors such as data quality, domain specificity, and ethical considerations played a significant role in this assessment. Organizations must align their objectives with the model's strengths and weaknesses to achieve meaningful results. Challenges in evaluating (...) class='Hi'>GenAI models include a lack of transparency in the decision-making process, data bias, scalability issues, and ethical considerations. Real-world examples, such as Air Canada's chatbot misinformation and Samsung's data leak, highlight the risks of deploying AI systems without proper oversight. To mitigate these risks, best practices should include clearly defining objectives, domain-specific fine-tuning, rigorous testing, regular updates, ethical guidelines, and collaborations with interdisciplinary teams. Establishing robust evaluation criteria, testing in controlled and real-world scenarios, checking robustness and adaptability, validating ethical and bias implications, and considering scalability is essential for ensuring the reliability and effectiveness of GenAI models. By adhering to these best practices and learning from real-world examples, organizations can confidently harness the power of GenAI to drive innovation and create meaningful impacts while aligning with organizational and societal expectations. (shrink)

The essay is facing a legitimacy crisis. With students increasingly able to generate plausible submissions using Generative AI, the essay’s status as a valid instrument of assessment of student learning is under serious threat. Yet, rather than abandoning the essay or turning to superficial fixes, this paper argues that the current disruption offers a chance to reconsider what essays are for as well as the chance to consider what they could be. In this paper, we distinguish between the standardised academic (...) essay of today from the historically contextualised essay. Drawing from the history of ideas, we argue that essays are best understood not as products to be measured against static criteria, but exploratory engagements with thought, shaped by uncertainty, divergence, and the recursive relationship between writer and text. This understanding has long been at odds with how essays are typically assessed and it is this misalignment, not AI alone, that we suggest underpins the current crisis. In response, we propose a set of structural changes to essay assessment design. We propose a holistic approach to assessment which, we suggest, helps show how the essay can be reconfigured as both an educationally meaningful and valid assessment task, fit for an age of AI without being defined by it. (shrink)

The rapid development of generative artificial intelligence has intensified concerns about AI-driven manipulation. This chapter examines one of its most controversial applications, namely deepfake-technology, which enables the creation of highly realistic audiovisual content. While existing theories of manipulation correctly identify deceptive deepfakes as manipulative, they often overlook a crucial dimension: the structural epistemic risks embedded in the technology itself. This chapter shows that distinguishing between benign and malicious deepfakes provides only a partial account of deepfake manipulation if this distinction is (...) not connected to the epistemic harm deepfakes may cause. The central claim is that malicious deepfakes are manipulative not only insofar as they are created and disseminated to deceive, but also because they transform the epistemic risk inherent in deepfake-technology into concrete epistemic harm. (shrink)

Acceleration ethics addresses the tension between innovation and safety in artificial intelligence. The acceleration argument is that risks raised by innovation should be answered with still more innovating. This paper summarizes the theoretical position, and then shows how acceleration ethics works in a real case. To begin, the paper summarizes acceleration ethics as composed of five elements: innovation solves innovation problems, innovation is intrinsically valuable, the unknown is encouraging, governance is decentralized, ethics is embedded. Subsequently, the paper illustrates the acceleration (...) framework with a use-case, a generative artificial intelligence language tool developed by the Canadian telecommunications company Telus. While the purity of theoretical positions is blurred by real-world ambiguities, the Telus experience indicates that acceleration AI ethics is a way of maximizing social responsibility through innovation, as opposed to sacrificing social responsibility for innovation, or sacrificing innovation for social responsibility. (shrink)

Generative artificial intelligence in the form of chatbots based on large language models (LLMs) has taken the world of law by storm. Philosophy of law is struggling to catch up with the theoretical significance of the advent of technological development and the way it may modify traditionally established understanding of legal phenomena, such as law-creation and authority. In this sense, for the most part, heated philosophical debates have circled around a normative question: ‘_Should_ AI create and interpret law?’. Much less (...) attention has been given to a different, albeit previous, question: ‘C_an_ AI create and interpret law?’ That is, is AI capable of producing outputs that can be deemed as ‘law’ (at least, law as we know it)? Can AI be a ‘legal author’? This paper explores this unattended question and endeavours to provide some provisional answers. In the first part, we define legal authority, legal authorship, and legal interpretation and claim that the intention of a determinate authoritative author is often considered the condition of the possibility of creating and interpreting contemporary legal texts. In the second part, we argue that LLM AI, in general, and ChatGPT, in particular, generate legal texts without having any intention. In the third part, we consider the positions of the authors that downplay or even eliminate intention from the discussions about the legal character of prescriptive texts. 4. Finally, we argue that there are good reasons to side with the second group of authors. The ability of agents without intentions, like ChatGPT, to create legal text is an argument in favour of the thesis that law can be created without intention behind the creation and that nonintentional creation can be interpreted to arrive at legal norms. (shrink)

This chapter elucidates on the role of GenAI in the society. Attempts to regulate the training and use of GenAI are discussed. The generic concept of translation ethics is applied to GenAI and notions such as bias and data privacy are considered. Accessibility and social inclusion are also briefly mentioned.

Acceleration ethics addresses the tension between innovation and safety in artificial intelligence. The acceleration argument is that risks raised by innovation should be answered with still more innovating. This paper summarizes the theoretical position, and then shows how acceleration ethics works in a real case. To begin, the paper summarizes acceleration ethics as composed of five elements: innovation solves innovation problems, innovation is intrinsically valuable, the unknown is encouraging, governance is decentralized, and ethics is embedded. Subsequently, the paper illustrates the (...) acceleration framework with a use-case, a generative artificial intelligence language tool developed by the Canadian telecommunications company Telus. While the purity of theoretical positions is blurred by real-world circumstances, the Telus experience documents acceleration AI ethics as a way of maximizing social responsibility through innovation. (shrink)

We argue that placing a generative artificial intelligence model between qualitative researchers and their subjects of inquiry fundamentally displaces and distorts the conditions under which meaningful and epistemologically sound knowledge can emerge. We further argue that such mediation raises ethical implications that are incompatible with the principles of responsible research that underpin business and society scholarship.

This chapter examines the dual nature of deepfakes and Generative AI (GenAI) in the context of disinformation, evaluating whether these technologies constitute a false alarm or a genuine threat. As advances in AI continue to enhance the realism of synthetic media, the risks associated with their misuse—intentionally or not—increase, posing serious challenges to information integrity and public trust. We analyse recent trends in the proliferation of GenAI content across multiple platforms, investigating their disruptive potential and their use in (...) public debate, election campaigns and conflicts. Through this exploration, our objective is to illuminate the complex landscape of GenAI, highlighting the urgent need for effective strategies to combat disinformation while acknowledging the technology’s potential for positive impact. (shrink)

Early artificial intelligence research was dominated by intellectualist assumptions, producing explicit representation of facts and rules in “good old-fashioned AI”. After this approach foundered, emphasis shifted to deep learning in neural networks, leading to the creation of Large Language Models which have shown remarkable capacity to automatically generate intelligible texts. This new phase of AI is already producing profound social consequences which invite philosophical reflection. This paper argues that Charles Peirce’s philosophy throws valuable light on genAI’s capabilities first with (...) regard to meaning, then knowledge and truth. Firstly, I explore how Peirce’s icon/index/symbol distinction illuminates the functioning of genAI. I argue that genAI’s engineers have skilfully captured a form of symbolicity, but no other sign-kind. In lacking indexical signs, LLMs lack connection with, and accountability to, particular worldly objects. In lacking iconic signs, LLMs are insufficiently disciplined by structural – most notably logical – relationships. Then I argue that GenAI’s astounding stream of articulate, truth-semblant, yet worthless texts issues a timely reckoning to modern philosophy’s representational realism. By contrast, Peirce’s pragmatism scaffolds a rich relational realism (Gili and Maddalena 2022), which shows how meaningful concepts, and a grasp of truth, can only occur across multiple cognitive systems who are simultaneously richly related with one another, and a shared environment in which they continually act and receive feedback, within a logical space of reasons. As Peirce himself noted, “Mere knowledge, though it be systematized, may be a dead memory; while by science we all habitually mean a living and growing body of truth”. (shrink)

The concept of Generative Artificial Intelligence (GenAI) is ubiquitous in the public and semi-technical domain, yet rarely defined precisely. We clarify main concepts that are usually discussed in connection to GenAI and argue that one ought to distinguish between the technical and the public discourse. In order to show its complex development and associated conceptual ambiguities, we offer a historical-systematic reconstruction of GenAI and explicitly discuss two exemplary cases: the generative status of the Large Language Model BERT (...) and the differences between protein structure predictions from AlphaFold 2 and 3. Our analysis shows that there is no unique and unambiguous definition of GenAI based on a purely technical account of the term. Following this conclusion, we argue that the public discourse is not simply a less complex way of speaking, but instead transcends its technical basis. As a means to structure this newly emerging discussion landscape we introduce a non-exhaustive list of four central aspects of GenAI: (multi-)modality, interaction, flexibility, and productivity. These dimensions constitute a first step towards defining GenAI beyond its technical basis. (shrink)

This study examines researchers’ ethical concerns toward the deployment of GenAI in research and their emotional responses. To acquire an in-depth understanding, we used narrative frames and follow-up interviews to collect data from 22 researchers who reported extensive experience with GenAI. An inductive thematic analysis revealed three themes capturing ethical concerns that invoked three types of emotional reactions. From an ethical perspective, our participants were concerned with “human ethical agency in AI research practices,” “cognitive impacts of overreliance on (...) GenAI in research,” and “ethical issues of access, accuracy, and privacy.” From an emotional perspective, they showed “mixed emotions,” “positive emotions,” and “negative emotions” when dealing with GenAI tools. There were close connections between the ethical implications of GenAI and the emotional reactions to them. In this light, we conclude that emotional reactions to GenAI, which are determinants of future use, should be taken more seriously in further research. (shrink)

This paper presents a conceptual approach for integrating artificial intelligence (AI) into the mechanics of narrative-based educational games. Drawing on theoretical analysis and a comprehensive literature review, the proposed model emphasizes the use of generative artificial intelligence (GenAI) to personalize game worlds, adapt storylines dynamically, and simulate the consequences of players’ moral decisions. Rather than offering static scenarios, the system enables real-time narrative adaptation based on decision patterns and player profiles, promoting deeper engagement and ethical reflection. The core contribution (...) lies in demonstrating how AI can support the development of critical thinking and moral sensitivity through interactive storytelling. The proposed solutions may inform the design of educational games and training environments that foster moral development in a socially responsible and pedagogically grounded manner. (shrink)

As GenAI becomes more prominent in business and society, global policymakers are racing to legislate around its possible negative impacts. This chapter looks at legal and regulatory issues about corporate compliance in four fundamental pillars: AI taxonomy, intellectual property, data management, and liability of products and services. It describes the landmark EU AI Act as the world’s first comprehensive, risk-based regulatory framework, contrasting it with the sectoral, pond-hopping approach of the USA. It covers the explosion of US state-level AI (...) bills and legislative activity in other jurisdictions and aspires to international responsiveness. A deep dive into IP issues reveals a lack of consensus surrounding authorship and fair use of training data, while data governance deals with privacy, security, localization, and cross-border transfer obligations. It also looks at two principal AI product liability paradigms: those based on fault and those based on strict liability. With the maturing of legal frameworks, enterprises must remain proactive, ensuring compliance is built into the AI development lifecycle so that innovation remains within global legal frameworks. (shrink)

Generative artificial intelligence (GenAI) tools like ChatGPT are reshaping higher education, raising concerns about academic integrity alongside potential benefits. The psychological tension accompanying the decision whether to use GenAI or not for a particular task may lead to “AI guilt”—students’ moral discomfort when using GenAI for traditionally human tasks. This study examines the impact of AI guilt on students’ use of ChatGPT for academic tasks, focusing on disciplinary differences between pure and applied fields. We conducted a survey (...) at a Singaporean university, measuring AI guilt and ChatGPT usage. Using logistic regression analysis, we found that AI guilt significantly reduces ChatGPT use for creativity-based tasks but not for routine-based tasks. The relationship between AI guilt and ChatGPT use is linear in pure fields, while applied fields show a nonlinear pattern. Specifically, the perceived risk of being detected and potential academic penalties decreases ChatGPT use in creativity-based tasks for all samples, while rationalization tendencies increase it. Interestingly, the interaction between rationalization tendencies and perceived social norms reduces usage, reflecting the tension between internal justification and external pressures. Heterogeneous analysis shows that rationalization tendencies and perceived risk influence pure fields more, whereas social norms drive applied fields more. These results show how AI guilt varies across disciplines and affects task-level GenAI tool use, offering insights for developing tailored ethical guidelines and integration strategies in higher education. (shrink)

Generative Artificial Intelligence (GenAI) is fundamentally transforming notions of creativity and creative production across disciplines, yet a comprehensive understanding of professional attitudes and integration patterns remains challenging. This scoping review examines how creative professionals perceive and integrate GenAI technologies across four domains: visual art and design, writing and literature, performing arts, and environmental and spatial design. Following PRISMA-ScR guidelines, we analysed 57 papers (2022–2025) from multiple databases, focusing mainly on empirically based studies of professional creative practice. We identify (...) universal trends, including the shift from creation to curation and meta-creation, the emergence of new literacies (prompt engineering, AI evaluation), and the reconfiguration of expertise hierarchies. Career stage emerges as a critical factor: across domains, entry-level professionals demonstrate enthusiasm and view GenAI as a natural extension of digital tools, while senior practitioners express scepticism about expertise devaluation. All fields consistently position GenAI in early-stage conceptualisation rather than final production, developing hybrid methodologies that preserve human judgment in convergent creative phases. However, impacts manifest differently based on each field/ sub-field’s relationship to embodiment and materiality. Integration levels follow an inverse relationship with traditional notions of “pure” creativity—fields prioritising embodied practice and cultural authenticity (fine arts, literary fiction, classical music) show the most significant resistance. At the same time, commercially oriented domains embrace higher adoption for efficiency gains. Visual artists face “erasure by obscurity” from AI output volume; writers negotiate between nonlinear, associative language generation and AI’s tendency toward coherence and cliché; performers uniquely embrace computational unpredictability as a generative force; architects balance efficiency imperatives with concerns about cultural homogenisation effects of Western-centric AI systems. Our conceptual framework positions creative professionals’ attitudes along two dimensions: knowledge codifiability (tacit/embodied to explicit/codifiable) and output materiality (physical/permanent to digital/ephemeral). Key tensions include balancing productivity with meaningful engagement, volume with distinction, precision with serendipity, and individual with collective intelligence. Ultimately, our findings reveal complex patterns of ambivalence shaped by competing value systems. This review contributes a comprehensive mapping of GenAI applications across creative disciplines, potential disconnects between educational emphasis on critique and industry’s pragmatic adoption, and gaps for future research on longitudinal skill impacts, emerging AI-native practices, Global South perspectives, and forms of creative resistance. (shrink)

Generative artificial intelligence (GenAI) tools like ChatGPT are reshaping higher education, raising concerns about academic integrity alongside potential benefits. The psychological tension accompanying the decision whether to use GenAI or not for a particular task may lead to “AI guilt”—students’ moral discomfort when using GenAI for traditionally human tasks. This study examines the impact of AI guilt on students’ use of ChatGPT for academic tasks, focusing on disciplinary differences between pure and applied fields. We conducted a survey (...) at a Singaporean university, measuring AI guilt and ChatGPT usage. Using logistic regression analysis, we found that AI guilt significantly reduces ChatGPT use for creativity-based tasks but not for routine-based tasks. The relationship between AI guilt and ChatGPT use is linear in pure fields, while applied fields show a nonlinear pattern. Specifically, the perceived risk of being detected and potential academic penalties decreases ChatGPT use in creativity-based tasks for all samples, while rationalization tendencies increase it. Interestingly, the interaction between rationalization tendencies and perceived social norms reduces usage, reflecting the tension between internal justification and external pressures. Heterogeneous analysis shows that rationalization tendencies and perceived risk influence pure fields more, whereas social norms drive applied fields more. These results show how AI guilt varies across disciplines and affects task-level GenAI tool use, offering insights for developing tailored ethical guidelines and integration strategies in higher education. (shrink)

Generative Artificial Intelligence (GenAI), such as ChatGPT, shows great promise and potential and is gradually being used in mental health care, but it also raises ethical concerns. These relate t...

As GenAI tools become readily accessible and increasingly integrated into higher education teaching, learning, and assessment, significant concerns and debates have emerged about their implications for academic integrity. In this context, recent years have witnessed a rapidly expanding field of research on higher education student experiences, perceptions, attitudes, beliefs, concerns, or practices related to academic integrity in the use of GenAI. This scoping review maps this field by analysing 38 empirical studies on student voices on academic integrity in (...) the use of GenAI. The review reveals a complex, dynamic landscape of student understandings, attitudes, perceptions, concerns, and practices regarding academic integrity in the use of GenAI in higher education. Overall, student voices on issues of academic integrity in using GenAI reveal their struggles, negotiations, and adaptations as they navigate an ethical ‘grey zone’. Students emerge not as passive recipients of GenAI technology or institutional policies, nor as culprits who breach academic integrity without ethical constraints, but as active agents who seek to interpret, adapt, and shape the ethical contours of GenAI use in their studies. Meanwhile, the gaps between students’ ethical awareness and their practices of compromising academic integrity, as well as their rationalisations of these gaps, are notable. Studies also show that student voices are shaped by intersecting demographic, psychological, and contextual factors, highlighting the need for differentiated policies and educational responses that recognise students as partners in developing and implementing policies and educational approaches to address the challenges of academic integrity entailed by GenAI. (shrink)

Generative artificial intelligence (GenAI) continues to transform education, yet its integration raises ethical concerns such as algorithmic bias, privacy erosion, and diminished autonomy. This integrative review synthesizes literature to clarify how ethical risks emerge and how governance can respond. Using integrative review procedures, the study synthesized theoretical and policy sources from Scopus and Web of Science. Analysis applies three lenses: Critical Data Studies, FATE (fairness, accountability, transparency), and IEEE Ethically Aligned Design, producing a sociotechnical account. Across studies, risks cluster (...) upstream in data and epistemic bias reproducing linguistic, cultural, and socioeconomic hierarchies; midstream in algorithmic failures weakening transparency, accountability, and contestability; and downstream in design and governance deficits eroding consent, recourse, and human-centered implementation. Implications include institutional lifecycle governance and auditing, participatory design, AI literacy, and targeted research priorities, especially intersectional and Global South studies. The review contributes a framework linking structural, operational, and design-level ethics for GenAI in education. (shrink)

Generative artificial intelligence (GenAI) promises to revolutionize data-driven fields (Milmo 2023). Building on decades of large language modeling (LLM) (Toner 2023), GenAI can collect, harmonize...

Generative artificial intelligence (genAI) tests the limits of copyright law in the United States (US), requiring the US Copyright Office to reassess its longstanding position on computer-made works. The Copyright Office argues against awarding copyright to genAI works because it overextends the limits of Sweat of the Brow Doctrine, which rewards effort with ownership and protection because the result has value. Sweat of the Brow Doctrine is based on John Locke’s labor theory of property, which posits that mixing (...) labor with resources yields property and rights to said property; however, awarding copyright to genAI works would deplete the commons due to the incredibly high rate of productivity exhibited by genAI. This article reinterprets Locke’s labor theory of property and proposes an extension to the framework. This extended framework is then applied to a hypothetical scenario that shows how technology firms may use genAI, streaming media platforms, and automated copyright enforcement systems to flood websites with genAI content, enabling them to assert claims over similar human-authored works. The result of this scenario is expanded to the larger terrain of US copyright law, showing that such privatization of content would impoverish the public domain and exploit unpaid human creativity. Under the proposed extended theory, AI outputs belong in the public domain, demonstrating how the Copyright Office’s argument ultimately protects the commons. (shrink)

As generative Artificial Intelligence (GenAI) tools become increasingly embedded in academic writing practices, questions of authorship, integrity, and accountability require new assessment approaches. This study examines teacher-led interviews as a mediational assessment practice, informed by an integrative cultural–historical activity theory (iCHAT) perspective that emphasizes how reasoning, responsibility, and authorship develop through guided social interaction. Teacher-led interviews were designed to prompt students to explain and justify their research writing decisions, including how they engaged with GenAI support. Reflection data were (...) collected from 24 Master’s-level English as a Foreign Language (EFL) students, who were asked to participate in short teacher-led interviews about their research proposal submissions as part of the unit assessment. Thematic analysis indicates that the interviews supported the emergence of an ethical orientation toward academic work, expressed through cognitive, emotional, and agentive developments. Students engaged more critically with ideas and AI-generated suggestions (cognitive), negotiated feelings of anxiety, fairness, and recognition (emotional), and adopted more proactive and self-regulated preparation strategies (agentive). These outcomes appeared to be shaped by how students experienced the interview (perezhivanie), suggesting that the interview reoriented the object and motive of the writing task from producing text to being able to account for one’s reasoning and decisions. The study proposes a cultural-historical framework conceptualizing teacher-led interviews as a mediational space where dialogic accountability supports reflective, ethical, and principled engagement with GenAI. Implications highlight the need for assessment practices that cultivate development through guided explanation rather than rely solely on detection or surveillance. (shrink)

Advances in Generative AI (GenAI) have the potential to transform education. In this chapter, we explore both promising possibilities for transforming creative teaching and learning and potential pitfalls. We also discuss how ignoring, underutilizing, and misusing GenAI can undermine the co-creative process and offer insights for mitigating these risks while maximizing GenAI’s potential benefits in Human x AI co-creative processes in education. More specifically, we introduce principles that can guide educators and researchers in understanding how humans can (...) use GenAI tools to provoke creative inquiry and facilitate the co-creation of new ideas, possibilities, and products in educational contexts. We then offer conceptual case-studies illustrating how GenAI tools might be used to transform creative teaching and learning. We conclude with implications for theory, research, and practice. (shrink)

Generative AI (GenAI) has the potential to affect artists’ control over their own music due to the illegal usage of copyrighted material for training. However, GenAI also creates exciting opportunities for artists to expand their material and working processes. Artists working with GenAI and documenting their outcomes can assist other artists as well as wider society in understanding how GenAI operates and can benefit human artistic output. This paper provides an autoethnographic case study into how a (...) new GenAI tool influenced an established composing practice during the writing of the experimental musical work, Control Yourself (2023). The Koup Music prototype by Kopi Su Studio was trained on vocal inputs by the author and subsequently generated bespoke sonic material. While identifiably true to the author’s musical—and literal—voice, the outputs were novel and perceived as imbued with emotion, leading to subsequent anthropomorphising of the AI. Written by a former AI sceptic, this paper details how the emotive power of the AI’s non-verbal, human-like sounds informed the narrative and structure of the resulting work and imparted a sense of collaboration, rather than solo authorship. Furthermore, the influence of the AI was felt beyond its actual involvement, with the project taking on a more playful approach less centred on the artistic control of the human composer. Following these observations, this paper discusses how GenAI served as a tool for musical experimentation and exploring creative ‘blind spots.’ These insights are also contextualised by current discourse on the perception and use of GenAI in the arts, the role of artistic control in human–AI co-creation, and how anthropomorphism has manifested in past human–AI partnerships. (shrink)

As generative artificial intelligence (GenAI) increasingly produces literary texts, ethical questions arise about how readers emotionally engage with AI-generated content, particularly when authorship is uncertain. This mixed-method study compares readers’ empathy toward human- and AI-translated literary excerpts. Quantitative results showed no significant differences in affective, cognitive or associative empathy, or in perceived quality, between conditions. Qualitative analysis revealed that readers’ engagement was driven primarily by textual features rather than authorship assumptions. However, when prompted to consider possible AI involvement, some (...) participants reconsidered their judgments through attributional reasoning about machine capabilities. The findings suggest that authorship disclosure shapes interpretive autonomy and evaluative judgments of machine-generated texts, with important ethical implications. (shrink)

As Artificial Intelligence is increasingly employed to mediate human interactions, there is uncertainty around how these technologies impact human behaviour and how such mediated interactions are perceived. One such case is the expanding use of Generative Artificial Intelligence (GenAI) to create and disseminate content across a range of media contexts, known as AI-Mediated Communication (AI-MC). Such cases raise important questions about how those on the receiving end of such outputs respond to these new forms of interpersonal communications. Based on (...) evaluating a piece of written text, this study explores how recipients' perceptions of the content creator's trustworthiness and authenticity, alongside their willingness to use the content (via knowledge uptake) are impacted by author type (human, AI-assisted, or fully AI) and media context (workplace email vs. social media post). We conducted a pre-registered experimental survey study with a 3 (author type) x 2 (media context) between-subjects factorial design (N = 547) where participants evaluated a piece of written text. Our findings demonstrate an “AI penalty”, with communication involving AI being perceived as less trustworthy, less authentic, and less useful for knowledge uptake. We also identify a “disclosure paradox”, as while participants believed it is important to disclose AI use, they also penalise the communication when such disclosure is made, which risks creating perverse incentives for non-disclosure of GenAI use for written communications. (shrink)