This study investigates whether and how interacting with ChatGPT may offer a context that supports perspective shifting and the development of cognitive flexibility, defined as the capacity to move between etic (outsider) and emic (insider) perspectives. Drawing on individual interviews with students enrolled in an advanced university-level Language for Specific Purposes (LSP) French course focused on marketing and advertising in France, this qualitative study examines students’ perspectives on their experiences using ChatGPT to conduct market research on French consumer needs and preferences. The analysis reveals that while students expressed concerns about the legitimacy, authenticity, and cultural positioning of AI-generated content, the interactive and conversational nature of the tool enabled some students to experiment with culturally unfamiliar roles, adopt emerging emic stances, and reflect on the limits of their interpretive frameworks. However, co-creative engagement or shared agency with ChatGPT was not automatic and depended on prompt design, tolerance for ambiguity, and the negotiation of subjective positioning. Rather than facilitating perspective transformation, ChatGPT-supported interactions appeared to foster more modest but meaningful shifts in interpretive positioning and dialectical thinking. The study points to prompt literacy as crucial for fostering more dynamic partnerships with ChatGPT and enabling students to explore alternative perspectives and roles in ways that support the development of intercultural competence in the L2 classroom.

This paper evaluates the performance of baseline and domain-augmented ChatGPT models for literature-based knowledge support in flood susceptibility mapping (FSM) using machine Learning approaches. To assess this, we designed five key questions related to FSM, with benchmark responses derived from our comprehensive review article (Pourzangbar et al., Journal of Flood Risk Management 18, e70042), which analyzed 100 studies on ML applications in FSM. The same questions were posed (i) to standard ChatGPT-4 and ChatGPT-4o models without additional contextual material, and (ii) to a domain-augmented GPT-4 configuration (Chat-FSM) equipped with retrieval access to the 100 reviewed articles. The comparison highlights that GPT-based models can reasonably reproduce frequently reported machine learning models and conditioning factors from the reviewed literature, but show weaker consistency in feature selection methods, often suggesting less relevant techniques. Among the models, ChatGPT-4o demonstrated the weakest alignment with benchmark data, while Chat-FSM demonstrated the highest agreement with the benchmark dataset across most evaluated questions. In terms of application-level efficiency, GPT models required substantially less time and computational effort compared to manual literature synthesis under the defined experimental setup. While ChatGPT-based systems can support literature-informed exploration in FSM, human expertise remains essential for critical reasoning, methodological design, and application to novel or context-specific scenarios.

Using the fields of memory studies and digital humanities, this article argues that there has been a shift from more collective and social memory to more personalised and individual memory. This shift, it is argued here, can be conceptualised through the psychoanalytic concept of ‘psychosis’. While the causes of the changes in our patterns of memory have been located in capitalist and neoliberal principles, the effects of the changes in our memory habits might be found in psychosis. From falling in love with machinic AI replicas to indulging in conspiracy theories to acting as if we are social media influencers or backing ourselves to win out in impossible job markets, we are inclined towards personal fantasy, often at the expense of participating in social life. But why do we do this? Why is it easier to believe a farfetched conspiracy theory or wild personal dream than it is to participate socially and collectively in the world we live in? Part of the reason, at least, is found in our increasing habitual reliance on new and emergent technologies. Often presented to us as a brand-new form of Artificial Intelligence, these generative tools are the latest update to a longer pattern in our digital world: the trend of developing ‘relationships’ with algorithms that, to larger and smaller degrees, we come to rely on for habits of cognition and recognition. By affecting our patterns of memory, these technologies produce a kind of isolation that lends itself to individual and fantastical – rather than shared and realist – thinking.

The emergence of large language models, exemplified by ChatGPT, has garnered growing attention for their potential to generate feedback in second language writing, particularly automated written corrective feedback (AWCF). In this study, we examined how prompt design – a generic prompt and two domain-specific prompts (zero-shot and one-shot) enriched with comprehensive domain knowledge about written corrective feedback (WCF) – influences ChatGPT’s ability to provide AWCF. The accuracy and coverage of ChatGPT’s feedback across these three prompts were benchmarked against Grammarly, a widely used traditional automated writing evaluation (AWE) tool. We find that ChatGPT’s ability in flagging language errors grew considerably with prompt sophistication driven by the integration of domain-specific knowledge and examples. While the generic prompt resulted in substantially lower performance than Grammarly, the zero-shot prompt achieved comparable results to it and the one-shot prompt surpassed it considerably in error detection. Notably, the most pronounced improvement in ChatGPT’s performance was observed in its detection of frequent error categories, including those of word choice or expression, direct translation, sentence structure and pronoun. Nonetheless, even with the most sophisticated prompt, ChatGPT still displayed certain limitations when compared to Grammarly. Our study has both theoretical and practical implications. Theoretically, it lends empirical evidence to Knoth et al.’s (2024) proposition to separate domain-specific AI literacy from generic AI literacy. Practically, it sheds light on the pedagogical application and technical development of AWE systems.

Extant work shows that generative AI such as GPT-3.5 and perpetuate social stereotypes and biases. A less explored source of bias is ideology: do GPT models take ideological stances on politically sensitive topics? We develop a novel approach to identify ideological bias and show that it can originate in both the training data and the filtering algorithm. Using linguistic variation across countries with contrasting political attitudes, we evaluate average GPT responses in those languages. GPT output is more conservative in languages conservative societies (polish) and more liberal in languages used in liberal ones (Swedish). These differences persist from GPT-3.5 to GPT-4. We conclude that high-quality, curated training data are essential for reducing bias.

The use of large language models (LLMs) has exploded since November 2022, but there is sparse evidence regarding LLM use in health, medical, and research contexts. We aimed to summarise the current uses of and attitudes towards LLMs across our campus’ clinical, research, and teaching sites. We administered a survey about LLM uses and attitudes. We conducted summary quantitative analysis and inductive qualitative analysis of free text responses. In August–September 2023, we circulated the survey amongst all staff and students across our three campus sites (approximately n = 7500), comprising a paediatric academic hospital, research institute, and paediatric university department. We received 281 anonymous survey responses. We asked about participants’ knowledge of LLMs, their current use of LLMs in professional or learning contexts, and perspectives on possible future uses, opportunities, and risks of LLM use. Over 90% of respondents have heard of LLM tools and about two-thirds have used them in their work on our campus. Respondents reported using LLMs for various uses, including generating or editing text and exploring ideas. Many, but not necessarily all, respondents seem aware of the limitations and potential risks of LLMs, including privacy and security risks. Various respondents expressed enthusiasm about the opportunities of LLM use, including increased efficiency. Our findings show LLM tools are already widely used on our campus. Guidelines and governance are needed to keep up with practice. Insights from this survey were used to develop recommendations for the use of LLMs on our campus.

With research showing the benefits of feedback, teachers have come under increasing pressure to provide more, including more personalised, and more detailed responses to students. This often places heavy demands on teachers and with ever-larger class sizes and heavier workloads, teacher fatigue and burn-out are common. Automation has the potential to change all this and new digital resources have already proven to be valuable in supporting L2 writing. In this paper I look at the contribution of Automated Writing Evaluation (AWE) programmes and Generative Artificial Intelligence (GenAI) to feedback. The ability to provide instant local and global feedback across multiple drafts targeted to student needs and in greater quantities promises to increase learner motivation and autonomy while relieving teachers of hours of marking. But haven’t we heard this all before? Are these empty claims which raise our expectations of removing some of the drudgery of mundane grammar correction? Most importantly, what is the role of teachers in all this, and can AI really improve writers and not just texts?

Education aims to improve our innate abilities, teach new skills and habits, and nurture intellectual virtues. Poorly designed or misused generative AI disrupts these educational goals. I propose strategies to design generative AI that aligns with education’s aims. The paper proposes a design for a generative AI tutor that teaches students to question well. I argue that such an AI can also help students learn to lead noble inquiries, achieve deeper understanding, and experience a sense of curiosity and fascination. Students who learn to question effectively through such an AI tutor may also develop crucial intellectual virtues.

This study explores the role of ChatGPT in the completeness of collaborative computer-aided design (CAD) tasks requiring varying types of engineering knowledge. In the experiment involving 22 pairs of mechanical engineering students, three different collaborative CAD tasks were undertaken with and without ChatGPT support. The findings indicate that ChatGPT support hinders completeness in collaborative CAD-specific tasks reliant on CAD knowledge but demonstrates limited potential in assisting open-ended tasks requiring domain-specific engineering expertise. While ChatGPT mitigates task-specific challenges by providing general engineering knowledge, it fails to improve overall task completeness. The results underscore the complementary role of AI and human knowledge.