Artificial intelligence is increasingly interwoven with design thinking (DT), yet comparative, stage-by-stage syntheses across canonical DT models remain scarce. This literature review maps how AI augments and challenges the major stages of widely used models and relates these effects to five illustrative domains. Following the SPAR-4-SLR protocol, we searched the Web of Science (2005–August 2025), screened records in two stages and assembled a corpus of 205 eligible studies for comparative synthesis. Across models, AI scales early-stage evidence work through large-N text and behavioral analytics, widens ideation via generative systems and accelerates prototyping and testing through simulation and predictive evaluation; at the same time, risks include bias, privacy and sovereignty concerns, evaluation opacity and homogenization of creative output. The weight of evidence supports hybrid intelligence: allocate divergent exploration primarily to AI while retaining human judgment for convergent selection and ethical decision-making. A complementary AI-native “Stingray” model highlights concurrent train–develop–iterate workflows that treat AI as a co-designer, while underscoring governance needs around interpretability and auditability. Overall, the review offers a model-by-model, stage-specific map of AI’s roles in DT, along with practical guidance for responsible deployment and research priorities for assessing boundary conditions and external validity.

Feedback is integral to second language (L2) writing instruction. However, large class sizes and limited teacher time often challenge the delivery of personalized feedback, prompting interest in AI-powered solutions such as ChatGPT (Escalante et al., 2023; Huete-García & Tarp, 2024; Steiss et al., 2024; Yoon et al., 2023; Zhang, 2024). This study evaluates a task-customized GPT model, “Belinda,” trained to assess A1-level Spanish learners’ writing and provide feedback. Two research questions guided the investigation: (1) Can Belinda accurately score beginner Spanish writing using a provided rubric? (2) Can Belinda deliver constructive qualitative feedback? Human and GPT-generated scores were compared for inter- and intrarater reliability, and qualitative analyses categorized the feedback for usability in the classroom. Results revealed moderate alignment between Belinda’s scores and human raters, though reliability of the GPT fell short of calibration benchmarks. Feedback quality varied, with Belinda often providing vague, incomplete, or inaccurate suggestions. Despite iterative training, the GPT struggled to balance error correction with encouragement, a critical need for novice learners. Additionally, inconsistencies in identical GPT versions raised concerns about reliability. While Belinda showed potential in automating feedback, its limitations in accuracy, contextual understanding, and positivity suggest it is not yet a viable substitute for human evaluation by itself. These findings emphasize the challenges of integrating AI into L2 instruction and call for the need for extensive datasets, robust training, and human–AI collaboration to achieve pedagogically sound outcomes. Future research should explore hybrid feedback models and scalable solutions to enhance AI’s role in language education without compromising learner progress or confidence.

This article presents a proposal for personalised learning itineraries in the field of Ancient History, designed specifically for neurodivergent university students, particularly those with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), or dyslexia. Drawing on the flipped classroom model, the approach shifts the centre of gravity of learning to the students, who are asked to engage with historical content through autonomous preparation, artificial intelligence (AI)-supported tasks, and collaborative in-class activities. Two differentiated learning paths are outlined: one structured and linear and one open and creative. Both incorporate digital tools, such as Google Forms and Canva, and regulation strategies to support executive functioning and autonomy. The article discusses the inclusive potential of this model and reflects on how the integration of AI reshapes historical learning and critical engagement.

The rapid evolution of artificial intelligence (AI) has profoundly reconfigured the contemporary workplace, redefining the interactions among human employees, AI systems, and organizational processes. Yet, most research adopts a tool-centric view, overlooking how AI’s emergence as an alternative working agent reshapes managerial attention and employee welfare. Drawing on the attention-based view (ABV) and a dual-agent model, we theorize that AI adoption activates two opposing mechanisms: a human attention gain mechanism, where collaboration needs heightened focus on employees and increased employee-related corporate social responsibility (ECSR), and an AI attention shift mechanism, where deep AI embedding redirects attention toward AI, suppressing ECSR. Using panel data from 2575 Chinese listed firms (2013–2023), we find an inverted U-shaped relationship between AI adoption and ECSR. Moreover, industry AI substitution risk sharpens and left-shifts this curve, while top management team (TMT) functional diversity and employee stock ownership flattens and right-shifts it. These findings advance research on AI adoption, managerial attention, and employee-focused CSR by illuminating how attention allocation in dual-agent contexts shapes ethical and strategic outcomes, offering actionable insights for balancing human–AI integration with sustained employee welfare.

The incorporation of algorithmic systems into organizations is reconfiguring decision-making processes and raising new ethical challenges related to transparency, impartiality, and accountability. This study maps the field of algorithmic ethics in organizational contexts through a co-citation–based bibliometric analysis of 1,437 Web of Science publications (search conducted on August 20, 2025). The analysis identifies 12 thematic clusters and reveals a robust intellectual structure, with high modularity (Q = 0.726) and a high weighted mean silhouette value (S = 0.894). The findings highlight the centrality of algorithmic management, responsible artificial intelligence, and explainability, as well as bridging works that connect technical, normative, and management-oriented perspectives. The study advances an integrative conceptual model and a future research agenda that point to the emergence of algorithmic ethics as an institutional logic of organizational governance. For managers, the results underscore the need to embed algorithmic ethics within organizational decision-making and control systems.

This paper investigates a specific culture of interdisciplinarity that has gained traction at the intersection of applied AI and ethics. To address social and ethical harms of AI applications, scholars have suggested importing norms, methodologies and governance frameworks from established disciplines such as the social sciences or medicine. I show how this importation presupposes and endorses a framing of applied AI as a domain separate from established disciplines. Yet, such separation is what initially allows AI practitioners to operate outside those disciplinary norms that have evolved to prevent harms now associated with AI applications. Conversely, if AI applications were understood as situated firmly within these disciplines, practitioners would already be accountable to their norms and standards. Paradoxically, this culture of interdisciplinarity might thus reinforce a problematic disciplinary isolation of applied AI underlying the very ethical issues it seeks to mitigate – fighting symptoms while playing into their cause. In response, I outline three paths forward.

Linnaean taxonomy, which imposes hierarchical classifications based on morphological characteristics, has become deeply embedded in modern data architecture, from databases to metadata schemas to AI training datasets. With its hierarchical structure and rigid categorization, Linnaean taxonomy privileges one type of knowledge while marginalizing alternative taxonomies that offer more fluid, contextual, and relational understandings of the natural world. This paper examines how the legacy of Linnaean taxonomy continues to shape contemporary classification systems and artificial intelligence (AI). Indigenous knowledge systems, which include spiritual, cultural, and ecological dimensions, view entities not as isolated objects but as nodes in dynamic, interconnected networks. We draw from the French naturalist, Comte de Buffon, who, in line with Indigenous knowledge systems, viewed nature as continuous and contextual rather than discretely compartmentalized. The dominance of Linnaean-style classification in AI and data systems perpetuates colonial power dynamics and contributes to knowledge homogenization while losing Indigenous languages and classification systems crucial for addressing contemporary environmental challenges, particularly in agriculture and biodiversity conservation. In this Age of AI, we call for a holistic and ecological approach to archives. Therefore, we propose applying ‘rhizomatic hylomorphism,’ an ethnobiological, alternative classification that transcends hierarchical taxonomies to embrace multiplicity, relationality, and contextual meaning.

In this editorial, three technological laggards reflect on the potential promises and pitfalls of artificial intelligence in psychiatry, as explored in recent BJPsych publications. We argue that the rapid changes driven by artificial intelligence are revealing underlying vulnerabilities within our profession. More importantly, we believe these shifts pose a fundamental challenge to our specialty: can we, as psychiatrists, evolve beyond mere intelligence to practice with genuine wisdom?