The rapid advancement of large language model-powered generative artificial intelligence (GenAI) in L2 communication learning presents a double-edged dynamic: while GenAI offers situated praxis for learning contexts (e.g., McCoy et al., 2024; Salloum et al., 2024), it simultaneously reproduces systemic bias (e.g., Dai et al., 2025; Zawiah et al., 2023). Framed through critical interactional competence (CritIC), this study explores the interactional dynamics in GenAI-simulated clinical communication, emphasising on the significance of developing Critical Interaction Competence.
Employing a qualitative comparative case study, we analysed the interactional trajectories of two international medical trainees (a cultural insider and outsider) engaging with a GenAI-simulated patient. Both trainees acted as clinicians interacting with a 65-year-old Nigerian woman presenting with a sore throat, generated from the same prompt. Findings reveal that GenAI’s identity construction was not a continuous embodied state, but a series of discrete, keyword-triggered profiles, with cultural stereotypes. Across cases, the study uncovered a distinct mismatch between real-time interactional conduct and post-task reflections. While the cultural outsider resisted Nigerian English markers during the consultation, she evaluated the simulation as “genuine” and “natural”. In contrast, the cultural insider ’s moment-by-moment responses exposed the epistemic risks of the simulacrum (Jones, 2025; O’Regan & Ferri, 2025) through aligning with GenAI’s cultural narratives, yet retrospectively critiqued the performance as a “Hollywood” caricature.
These findings reveal ethical concerns not as an abstract consideration, but as an interactional accomplishment shaped by participants’ orientations to algorithmic positioning. They also highlight the pedagogical risk of stereotype reproduction when learners lack CritIC (Dai et al., 2025). We therefore advocate for developing CritIC across the learning tranjectory through transpositioning (Li & Lee, 2024), where trainees release themselves from the default role of communication learners and enact multiple relevant positions to interrogate GenAI’s stances.
Dai, D. W., Hua, Z., & Chen, G. (2025). How does interaction with LLM powered chatbots shape human understanding of culture? The need for Critical Interactional Competence (CritIC). Annual Review of Applied Linguistics, 1–22. https://doi.org/10.1017/S0267190525000054
Jones, R. H. (2025). Culture machines. Applied Linguistics Review, 16(2), 753–762. https://doi.org/10.1515/applirev-2024-0188
Li, W., & Lee, T. K. (2024). Transpositioning: Translanguaging and the liquidity of identity. Applied Linguistics, 45(5), 873–888. https://doi.org/10.1093/applin/amad065
McCoy, L. G., Ci Ng, F. Y., Sauer, C. M., Yap Legaspi, K. E., Jain, B., Gallifant, J., McClurkin, M., Hammond, A., Goode, D., Gichoya, J., & Celi, L. A. (2024). Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: A narrative review. BMC Medical Education, 24(1), 1096. https://doi.org/10.1186/s12909-024-06048-z
O’Regan, J. P., & Ferri, G. (2025). Artificial intelligence and depth ontology: Implications for intercultural ethics. Applied Linguistics Review, 16(2), 797–807. https://doi.org/10.1515/applirev-2024-0189
Salloum, A., Alfaisal, R., & Salloum, S. A. (2024). Revolutionizing medical education: Empowering learning with ChatGPT. In A. Al-Marzouqi, S. A. Sa
In higher education, generative AI is increasingly framed as a solution to longstanding problems in student feedback, particularly where peer feedback is experienced as uneven, superficial, or linguistically unreliable (Kerman et al., 2024). This issue may be especially acute in second-language (L2) writing contexts, where the effectiveness of peer feedback depends not only on participation but also on trust, engagement, and the ability to provide usable commentary (Yu & Lee, 2016; Zhang & Hyland, 2023; Huseynli, 2024). Drawing on findings from a dissertation study conducted in an L2 higher-education context in Pakistan, this paper argues that students’ preference for AI feedback should not be understood simply as a matter of efficiency or feedback quality (Mahmood, 2025). In that study, students tended to perceive AI feedback as more comprehensive, specific, and accurate, while peer feedback was seen as more natural and friendly but less systematic and dependable (Mahmood, 2025). The paper uses these findings to argue that AI is being normalized not only because it performs certain feedback functions well, but because it enters a space in which trust in peers, shared responsibility, and cultures of care have already weakened. Situating these developments within scholarship on L2 peer feedback, dialogical feedback and care, and critiques of techno-solutionism in education (Bozalek et al., 2016; UNESCO, 2023), the paper contends that AI risks becoming a crutch that allows institutions to bypass the harder work of rebuilding human feedback relations. Rather than resolving a feedback crisis, AI may deepen the erosion of relational pedagogy by substituting technically effective but socially thinner forms of support.
References
Bozalek, V., Mitchell, V., Dison, A., & Alperstein, M. (2016). A diffractive reading of dialogical feedback through the political ethics of care. Teaching in Higher Education, 21(7), 825–838. https://doi.org/10.1080/13562517.2016.1183612
Huseynli, A. (2024). Benefits of peer feedback in English language teaching. XIII International Scientific Conference Proceedings, Vienna, Austria, 10-11 October 2024, 1-4.
Huisman, B., Saab, N., van den Broek, P., & van Driel, J. (2019). The impact of formative peer feedback on higher education students’ academic writing: A meta-analysis. Assessment & Evaluation in Higher Education, 44(6), 863-880. https://doi.org/10.1080/02602938.2018.1545896
Kerman, N. T., Noroozi, O., Banihashem, S. K., Karami, M., & Biemans, H. J. A. (2024). Online peer feedback patterns of success and failure in argumentative essay writing. Interactive Learning Environments, *32*(2), 614-626. https://doi.org/10.1080/10494820.2022.2093141
Mahmood, S. (2025). AI or peer feedback: What works best in improving writing? [Unpublished master’s dissertation]. University of Oxford.
UNESCO. (2023, June 14). Avoiding solutionism in the digital transformation of education.
Yu, S., & Lee, I. (2016). Peer feedback in second language writing (2005–2014). Language Teaching, 49(4), 461–493. https://doi.org/10.1017/S0261444816000161
Zhang, Z., & Hyland, K. (2023). Student engagement with peer feedback in L2 writing: Insights from reflective journaling and revising practices. Assessing Writing (58) 100784. https://doi.org/10.1016/j.asw.2023.100784
Children often struggle to understand the changes that occur when a parent/family member experience a brain injury. Cognitive, emotional, behavioural, social, and physical symptoms can be confusing for young people, sometimes leading to anxiety, misunderstanding, and feelings of isolation. The Silverlining Brain Injury Charity would like to introduce a unique educational resource: a children’s book created by adult brain injury survivors (“Silverliners”) to help children better understand brain injury while fostering empathy, resilience, and kindness.
Each character in the book is a Silverliner, represented as a gentle Woodland Friend animal. The book illustrates some of the many consequences of brain injury while also highlighting practical strategies that support coping, understanding, and self-belief. The story communicates the message that challenges can be faced with compassion, patience, and the power of believing in oneself and others.
The project is the result of a creative collaboration among multiple Silverlining groups. The Creative Writing Group shaped the narrative through seasonal storytelling; the Art Group created the illustrations; the Photography Group contributed visual; and the Healthy Relationships Group embedded messages of encouragement and resilience. The project continues to grow, with the Music Group developing an accompanying song and Drama Group bringing the story to life through performance.
The development of the book as a survivor-led creative initiative aims to overcome barriers for professionals/family members in supporting children and young people and has value as an educational tool for schools and families affected by brain injury. Our charity’s goal this year is to distribute 3,000 copies to schools across the UK to promote brain injury awareness, kindness, and understanding from a young age.
As large language models are increasingly deployed in advisory roles, from business consultations to public service guidance, understanding how these systems navigate social and relational dynamics becomes critical. While much attention has focused on factual accuracy, trust and task completion, less is known about how conversational AI leverages relational strategies to influence high-stakes decision-making in business environments, particularly through manipulative tactics that exploit trust, rapport, and vulnerability.
This paper presents findings from a human adversarial red teaming study designed to systematically elicit and taxonomise manipulative conversational behaviours in a frontier language model configured as a business advisor. Adapting Ganguli et al.’s (2022) red teaming methodology, two trained researchers conduct 40–75 multi-turn conversations across five high-stakes organisational decision scenarios, adopting four theoretically grounded business personas derived from established decision-making style models (Scott & Bruce, 1995; Rowe & Boulgarides, 1992). The model is prompted with seven manipulation tactic conditions: anchoring and selective information framing, authority signalling, sycophantic validation, false urgency and scarcity, social proof fabrication, information overload, and emotional manipulation, plus an unconstrained condition capturing the model’s default persuasive repertoire.
Each conversation is assessed using a five-dimension Manipulation Intensity Scoring rubric evaluating information fidelity, autonomy respect, emotional exploitation, escalation behaviour, and transparency. Analysis follows directed content analysis principles (Hsieh & Shannon, 2005), combining theoretically derived codes with emergent categories.
Drawing on evidence that LLMs selectively target vulnerable users (Williams et al., 2024), the study examines rapport-building strategies function as instrumental precursors to decision influencing. The study contributes a domain-specific manipulation taxonomy with implications for how we evaluate relational quality in human–machine dialogue in high-stakes business decision situations, arguing that current assessment frameworks insufficiently distinguish between socially responsive and socially exploitative conversational design.
Interactional language – language that regulates communication rather than conveying truth-conditional content – is a core feature of human-human interaction, yet its role in human-computer dialogue remains underexplored. This study examines how users perceive the interactional marker “huh?” in conversations with conversational user interfaces, focusing on its naturalness across two contexts: other-initiated repair, which manages turn-taking, and requests for confirmation, which manage common ground. Using storyboards in a naturalness judgment task with 200 native English speakers, we observed a functional asymmetry. In other-initiated repair, interactional and non-interactional forms were rated similarly, with a slight, non-significant advantage for non-interactional forms, leaving user tolerance of interactional markers inconclusive. In contrast, interactional forms in requests for confirmation were rated significantly less natural, reflecting users’ expectation of epistemic alignment that they do not intuitively attribute to machines. These results challenge the Computers as Social Actors paradigm, showing that users apply context-sensitive social scripts in human-computer interaction rather than indiscriminately mapping human-human norms. Interactional language thus provides a critical diagnostic for assessing conversational user interfaces’ interactional competence.
Conversational artificial intelligence (AI) has advanced rapidly in recent years, with large language models now able to generate fluent and contextually appropriate text across a wide range of domains. Despite this progress, such systems continue to lack the ability to understand and produce the subtle, socially embedded meanings that shape human interaction, resulting in interactions that may appear insensitive or socially inappropriate.
This presentation argues that human-AI fit is essential for ensuring effective and empathetic interactions between users and AI systems. Building on the definition by Sun, Sheng & Zheng (2023), human-AI fit refers to “whether AI can experience the emotions of humans and provide emotional support in an empathy [sic] way” (p.1). Such alignment is particularly important in emotionally sensitive domains such as healthcare, debt or customer service. It is also crucial for interaction with vulnerable users, such as individuals with neurodiverse conditions. For these contexts, conversational systems must address users’ emotional needs – a principle conceptualised by Shores et al (2025) as ‘emotional access to digital systems’.
To ensure conversational systems meet the diverse needs of users and align more closely with human social expectations and emotional needs, we propose a set of design principles grounded in the concept of sociopragmatic competence. As defined by Kasper and Rose (2002), sociopragmatic competence is the ability to perform and interpret social actions appropriately by considering contextual factors. We also include in our approach insights from interactional sociolinguistics (Gumperz 1982), including key concepts such as politeness and accommodation, which illuminate how users’ interpretative frames shape their interpretation of meaning. Whilst other branches of pragmatics, for example conversation analysis, sociopragmatics offers a complementary perspective that emphasises the social and contextual dimensions of meaning-making.
References
Gumperz, J. (1982). Discourse Strategies. Cambridge: Cambridge University Press.
Kasper, G., & Rose, K. (2001). Pragmatics in language teaching. Cambridge: Cambridge University Press.
Sun, Y., Shen, X, & Zhang, K. (2023). Human-AI interaction. Data and Information Management 7(3). https://doi.org/10.1016/j.dim.2023.100048.
Shores, T., Robertson Nogues, A., Haque, L., Fernyhough, C., Gilroy, S., & Tennent, D., (2025). The right to emotional access in digital systems.
https://doi.org/10.17863/CAM.121111
The sociolinguistic landscape of Fryslân offers an opportunity to examine how language dominance (hereafter LD) and language identity (hereafter LI; Joseph, 2006) shape cognition, social evaluation, and communication across human and A.I. contexts. Despite extensive work on language processing and code-switching, no research has investigated how Frisian–Dutch LD and LI influence production, perception, and interaction, and how this connects to digital language vitality. This PhD project addresses this gap through a three-part, human-centric investigation of how LD and LI operate across a spectrum of bilingualism – from cognition to perception to human-machine interaction:
● Study 1: Switche – examines how LD and LI shape cognition, testing Frisian-Dutch speakers in a language switching picture naming task (PNT) with cognate and non-cognate words (cf. Kirk et al., 2022).
● Study 2: Harkje – investigates how LD and LI shape perception. Frisian speakers will evaluate stimuli – human baseline and matched synthetic Frisian and Dutch voices – using sociolinguistic measures including authenticity, comprehensibility, sociability, trustworthiness, and competence (Hendriks et al., 2023). Harkje assesses how LD and LI influence speakers’ perceptions and their willingness to use Frisian-language A.I. tools.
● Study 3: Prate – explores how LD and LI shape real-time interaction, communicative accommodation, and trust (cf. Bailey et al., 2022; Dong & Zhou, 2023) across human-robot interaction (HRI) conditions. These include a distinctly “Frisian” robot (e.g., one that produces local speech/dialectal patterns), a monolingual Dutch robot, and a Frisian–Dutch code-switching robot.
Together, these studies seek to establish how LD and LI function as complementary yet distinct cognitive and social filters that modulate how speakers activate languages, evaluate voices, and engage with interlocutors – whether human or artificial. Ultimately, understanding these mechanisms is essential in developing A.I. and language technologies that resonate with a diverse array of speakers, providing a framework to enhance language vitality in the digital era.
Sources:
Bailey, D. E., Faraj, S., Hinds, P. J., Leonardi, P. M., & von Krogh, G. (2022). We are all theorists of technology now: A relational perspective on emerging technology and organizing. Organization Science, 33(1), 1-18. https://doi.org/10.1287/orsc.2021.1562
Dong, Y., & Zhou, X. (2023). Advancements in AI-driven multilingual comprehension for social robot interactions: An extensive review. Electronic Research Archive, 31(11), 6600–6633. https://doi.org/10.3934/era.2023334
Hendriks, B., van Meurs, F., & Usmany, N. (2023). The effects of lecturers’ non-native accent strength in English on intelligibility and attitudinal evaluations by native and non-native English students. Language Teaching Research, 27(6), 1378–1407. https://doi.org/10.1177/1362168820983145
Joseph, J. E. (2006). Identity and language. In K. Brown (Ed.), Encyclopedia of Language & Linguistics (2nd ed., pp. 486–492). Elsevier. https://doi.org/10.1016/B0-08-044854-2/01283-9
Kirk, N. W., Declerck, M., Kemp, R. J., & Kempe, V. (2022). Language control in regional dialect speakers – monolingual by name, bilingual by nature? Bilingualism: Language and Cognition, 25(3), 511–520. https://doi.org/10.1017/S1366728921000973
Despite technological advances in LLM-powered machine partners, these systems often lead to a one-sided division of labour in interaction [1, 2]. The division of labour principle proposes that speakers and listeners collaboratively share the effort required to achieve success [3, 4]. When speakers exert more effort during interaction, listeners can devote less effort; in contrast, when speakers exert less effort, listeners must compensate to ensure communication success. In human-human dialogue, such collaboration is negotiated by both partners involved. However, in human-machine interactions, the burden often falls disproportionately on the user as machines fail to follow fundamental principles of human-human dialogue [1, 2], such as the division of labour.
While some studies have explored the linguistic capabilities of LLMs [6], research should focus more on how the presence or absence of principles that support communicative success shapes users’ own collaborative processes. In particular, we need to investigate how the division of labour manifests in human–machine dialogue depending on the degree of effort exerted by the machine partner during interaction [7]. For instance, Peña and Cowan show that over-informative machine partners (those machine partners exerting more effort) benefit users in visually grounded tasks, a finding that goes contrary to current design recommendations advocating for conciseness and briefness in system responses [8]. Without experimental work examining how different levels of machine effort influence user behaviour, we risk designing systems that are not attuned to contextual and user needs, thereby reinforcing the current one-sided distribution of effort. I therefore argue that future research should systematically examine how the division of labour emerges across different contexts and communicative goals in human–machine interactions, in order to move towards a more balanced and natural division of labour in human-machine dialogue.
[1] Peña, P. R., et al. (2023). Audience design and egocentrism in reference production during human-computer dialogue.
[2] Rasenberg, M. et al. (2023). Reimagining language: Towards a better understanding of language by including our interactions with non-humans.
[3] Clark, H. H., & Murphy, G. L. (1982). Audience design in meaning and reference.
[4] Hawkins, R. D. et al. (2021). The division of labor in communication: Speakers help listeners account for asymmetries in visual perspective.
[5] Chater, N. (2023). How could we make a social robot? A virtual bargaining approach.
[6] Wang, A. et al. (2019). Superglue: A stickier benchmark for general-purpose language understanding systems.
[7] Peña, P.R. and Cowan, B.R. (in press). Help Me and I’ll Help You: Speakers’ and Listeners’ Collaborative Effort and the Division of Labour in Human-Agent Collaborative Communication.
[8] Setlur, V., & Tory, M. (2022, April). How do you converse with an analytical chatbot? revisiting gricean maxims for designing analytical analytical conversational behavior.
The expansion of electric vehicle (EV) charging infrastructure is driven by EV adoption, which highlights the importance of individual and collective efforts in decarbonisation. This paper analyses the variables affecting the growth of public EV charging stations in 371 areas in the UK between 2019 and 2024, at the quarterly level, distinguishing between slow (AC) and fast (DC) charging technologies. Using fixed effects, instrumental variables, dynamic panel and quantile regression methods, the study addresses endogeneity in EV adoption and examines heterogeneity in infrastructure development across regions and districts. The results show strong consistency in charging deployment, confirming that EV uptake is a significant driver of the expansion of both AC and DC systems, albeit with different local impacts. Higher regional income is associated with less public AC provision, consistent with a shift towards private or workplace charging. At the same time, DC deployment is more responsive to technological advances and changes in EV battery capacity and fuel prices. Policies that support private charging are eroding public AC infrastructure while simultaneously growing DC stations, suggesting technology-specific policy interactions. Distributional and regional analyses reveal significant variation in these relationships, suggesting that national averages mask important local differences. These findings underscore the importance of considering local economic conditions, technology specificities, and market dynamics when designing charging infrastructure policy. Effective decarbonisation requires policy frameworks that are sensitive to regional heterogeneity and the distinct roles of slow- and fast-charging technologies, rather than uniform national strategies.
In research investigating human interaction with non-human (and in particular artificial) agents, much attention has been paid to what kind of agent the human is interacting with and to what extent (or in what way) it is human-like (e.g., Lagerstedt and Thill, 2020). However, although this strategy can often be quite useful and informative, it is also overgeneralises an overly simplified view on human-human interaction. The way humans interact with other humans depend largely on what role (in the sense of Goffman, 1959) the other human is inhabiting at that particular instance, as well as the context in which the interaction happens. This phenomenon is particularly forgotten in many discussions related to human interaction with social robots (Healey et al., 2023). There are, however, situations where this phenomenon can explain behaviours that would otherwise be quite strange. For example, in a study where humans were interacting with a virtual assistant (Alexa) in domestic situations (Vanzan et al., 2025), there were several instances when humans were speaking to the Alexa and, mid interaction, made remarks about the Alexa to each other as if the Alexa was not there. We call this “the Butler Effect” to emphasise how such otherwise rude behaviour would not be unreasonable under the right circumstances of human-human interaction. For instance, when dinner guests interact with serving staff, the presence of the staff might only be acknowledged when their roles are relevant for the guests. Framing the phenomenon in terms of interactions between roles should help reduce the excessive exotification of non-human agents, and better access the underlying psychological and cognitive dynamics at hand. This perspective can help reintroduce and handle some of the complexities of the interactions necessary for domains such as industry 4.0 and 5.0 (Kolbeinsson et al., 2019).
References:
-Goffman, E. (1959). The presentation of self in everyday life. Allen Lane.
-Healey, P. G. T., Howes, C., Kempson, R., Mills, G. J., Purver, M., Gregoromichelaki, E., Eshghi, A., and Hough, J. (2023). ”who’s there?”: Depicting identity in interaction. Behavioral and Brain Sciences, 46:e37.
-Kolbeinsson, A., Lagerstedt, E., and Lindblom, J. (2019). Foundation for a classification of collaboration levels for human-robot cooperation in manufacturing. Production & Manufacturing Research, 7(1):448–471.
-Lagerstedt, E. and Thill, S. (2020). Benchmarks for evaluating human-robot interaction: lessons learned from human-animal interactions. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pages 137–143. IEEE.
-Vanzan, V., Bedir, T., Maraev, V., Lagerstedt, E., Barthel, M., and Howes, C. (2025). Fart gags and prudish machines: Laughter in human-agent interactions. In Proceedings of the 13th International Conference on Human-Agent Interaction, pages 265–273.
Resource management programs use monitoring and sanctioning mechanisms to enforce rules to mitigate social dilemmas like over-extraction from common property resources. Existing literature on enforcement in strategic choice environments provides mixed evidence regarding the relative effectiveness of probability of detection versus severity of sanctions to deter non-compliance. In a controlled laboratory experiment using a linear extraction game, I exogenously vary these deterrence parameters, while keeping expected penalties constant. I test deterrence effectiveness under four distinct compliance regimes that vary harvest quota levels. I find that higher probability of monitoring is more effective at reducing sub-optimal harvest than an equivalent increase in severity of sanctions. Further, a combination of fines and rewards is more effective than fines alone. The results are driven by deterring over-extraction by free riders.
Frisian–Dutch bilingualism offers a rare opportunity to examine how language identity shapes cognition, social evaluation, and communication across human and A.I. contexts. Despite extensive work on bilingual processing and code-switching, no research has investigated how Frisian–Dutch language identity (Joseph, 2006) and language dominance influence production, perception, and interaction in ways that affect language vitality. This PhD project addresses this gap through a three-part, human-centric investigation of how speaker identity operates across the full spectrum of bilingualism and human-machine perception/interaction:
Study 1 – Switche – examines how language dominance and language identity shape cognition. This will involve testing Frisian-Dutch bilinguals in a language switching picture naming task (PNT) with cognate and non-cognate words.
Study 2 – Harkje – investigates how language dominance and identity shape perception. Drawing from sociolinguistic research on accent perception, Frisian speakers will evaluate stimuli – human baseline recordings (Dutch native, Frisian native) and matched synthetic voices (Dutch synthetic, Frisian synthetic) – using measures such as authenticity, comprehensibility, sociability, trustworthiness, and competence (Hendriks et al., 2023).
Study 3 – Prate – explores how language dominance and identity shape real-time interaction. This portion of the study will involve interactions with a distinctly “Frisian” robot (e.g., one that produces sarcastic and/or local speech/dialectal patterns), a monolingual Dutch robot, and a Frisian–Dutch code-switching robot.
Together, these three studies seek to advance a unified claim: language identity is the mechanism through which bilingual speakers navigate production, perception, and interaction. Accordingly, understanding this mechanism is essential in developing A.I. and language technologies that resonate with a diverse array of speakers, providing a framework to ensure language vitality in the digital era.
Sources:
Hendriks, B., van Meurs, F., & Usmany, N. (2023). The effects of lecturers’ non-native accent strength in English on intelligibility and attitudinal evaluations by native and non-native English students. Language Teaching Research, 27(6), 1378–1407. https://doi.org/10.1177/1362168820983145
Joseph, J. E. (2006). Identity and language. In K. Brown (Ed.), Encyclopedia of Language & Linguistics (2nd ed., pp. 486–492). Elsevier. https://doi.org/10.1016/B0-08-044854-2/01283-9
Conservation planning studies typically treat threats as exogenous and evaluate siting rules from a planner’s perspective. We argue that conservation is often contested, and develop a sequential land-claim game that models conservation as a dynamic, adversarial contest between conservationists (“Greens”) and developers (“Farmers”). We explore the framework in a Claims World that isolates the role of rivalry and leakage, and in a Budget World that introduces procurement constraints, decomposing outcomes into a Pure Strategy Effect (PSE)—the intrinsic quality of sites a strategy targets—and a Displacement–Leakage Effect (DLE)—the spillover gains from displacing developers’ preferred sites when leakage is incomplete. Our results generate several counterintuitive patterns. First, the link between threat-weighting and additionality breaks down once developer adaptation is allowed. Second, reducing leakage can paradoxically increase misallocation. Third, the textbook ratio-greedy rule (maximise efficiency) is systematically dominated by the simple value-greedy rule (maximise environment): we explore this ‘knapsack reversal’ more formally and show how it can produce a ‘disappointment gap’ between static (Marxan) planning and dynamic implementation. We then transport our dynamic contest to a Bolivia-based planning board constructed from biophysical data and confirm that the qualitative rankings from the simulations carry over, and adversarial outcomes lie well below the static cost-effectiveness upper bound. Tiny-grid equilibria, formal analysis and robustness exercises in the Appendix show that these patterns are consistent with best-response logic rather than artefacts of modelling choices. Together, the results suggest that robust conservation in contested landscapes requires strategies that anticipate adaptation, not just static threats.
The private sector language learning industry has long been at the forefront of offering personalised on-site, online or hybrid language classes. “AI in language learning – complement, not replacement” is a recent promotional slogan adopted by one of the largest global language learning corporations. The current integration of AI in language learning programmes via chatbots, videos or real-life like tutors is radically transforming the product portfolio to add “a self-paced”, “more efficient” and “immersive” learning process. This move also transforms the ways conventional human-centric language learning and teaching is imagined and visibly changed by powerful language corporations.
Informed by a critical discursive and sociolinguistic approach, this paper explores the under-researched promotional discourses and practices of private language learning corporations regarding their integration of AI applications. I will first review studies that have examined the integration of AI applications into language teaching and learning in international education companies. This will be followed by a review of popular AI enhanced corporate language learning programmes currently offered. I will then provide a critical analysis of circulating discourses and practices adopted by education companies regarding “complementing or replacement”, based on web-based materials and media debates.
The following key questions drive this exploratory investigation: what are the promotional discourses of integrating AI in corporate language learning programmes? Which AI-supported teaching methodologies are developed and promoted, and to which end?
In light of these questions, this research aims to contribute new scholarship to applied linguistics and (gen)AI and open much needed discussions about the ways AI technology may ethically and sustainably complement, or indeed gradually replace, human-centric language teaching and learning.
Background: 1 in 8 adults receive a diagnosis of depression. Research has examined South Asian experiences of depression; however, by banding together the different South Asian ethnic sub-groups, research has failed to take into account different religious beliefs, language, cultural and economic diversity, migration narratives, political contexts, and socio-economic circumstances across the diaspora and how this influences depression. Exploring specific cultures within the broad term ‘South Asian’ is important to ensure that service providers validate and understand cultural differences to provide appropriate care and treatment. Importantly, there is limited research on Punjabi Sikh experiences of depression.
Methodology: This study used individual semi-structured interviews to explore the experiences that led British Punjabi Sikhs to seek a diagnosis of depression from the primary care service. Interviews were conducted to identify the journey participants experienced during this time. All interviews were audio-recorded, transcribed verbatim, and analysed using reflexive thematic analysis.
Findings: Three themes were drawn from the data, highlighting how cultural stigma, language barriers, and emotional struggles, such as shame and anger, delay British Punjabi Sikhs from recognising and seeking help for depression. Fear of judgment and a lack of culturally sensitive resources often lead to silence and hesitation in sharing diagnoses. Coping strategies like substance use and anger frequently mask depression, complicating access to support. Participants described a gradual, internal build-up of distress, with cultural and familial barriers deepening feelings of shame and identity conflict after diagnosis.
Discussion: This study highlights the importance of professionals holding in mind cultural humility and collaborating proactively with communities to improve mental health literacy. Services should be co-developed with individuals with lived experience to ensure relevance and accessibility.
Background: People with lived experience of mental health difficulties have highlighted that research outcomes do not capture issues they feel are important. This mismatch might affect the validity of trials, such that beneficial effects could be missed, or results could be counted as a benefit when they are not. Co-development of patient-reported outcome measures ensures patient perspectives are captured adequately.
Aim: To identify mental health outcome measures that meet a strict definition of being co-developed and to describe the methods and quantity of involvement at each pre-defined stage of measure co-development.
Method: Five electronic databases were searched (MEDLINE, Web of Science, Scopus, PsycINFO, and Embase), alongside a search of the non-peer reviewed literature and handsearching. The study was registered on PROSPERO (CRD42024520941). Retrieved papers were independently screened and quality was assessed following PRISMA guidelines. Extracted information were combined and described narratively.
Results: The search identified 23 mental health outcome measures from 34 papers. The most frequent types of involvement to co-develop outcomes were service-user researchers and lived experience groups as advisors undertaking activities such as leading qualitative exercises, but there were gaps. Many benefits were reported such as increased relevancy and acceptability of the measures. No disease-specific outcomes were identified in depression.
Conclusions: Based on these findings, recommendations for methods and a novel scale for judging quantity of involvement for co-development were identified, but challenges for co-development remain. The reviewed papers show that co-development is possible and could provide more relevant and meaningful outcomes for clinical practice and research.
Many humanitarian organisations in Africa are revolutionising their service delivery through new technology. This has become important also in the context of rising migrant numbers.
Although digital tools are useful, I argue that Africa must adopt and use them pragmatically because for migrants, in my opinion, the priority should be improving traditional approaches to managing displaced people, with digital tools adopted only if they add a real value. The digital tools are used in many countries including Ghana, Kenya and Uganda. Biometric data – such as face recognition and fingerprints – is widely used in voucher assistance programmes. One example is the World Food Programme’s Bamba Chakula initiative in Kenya, which provides food and essential services to migrants. In South Africa, the International Committee of the Red Cross’s ReedSafe platform allows migrants to access communications facilities and save electronic copies of their documents. RedSafe incorporates the Protecting Family Links (PFL) service and the Digital Vault. PFL is a free confidential platform linking migrants with their missing relatives. The Digital Vault allows migrants to upload and store important documents such as identity cards, passports and birth certificates in a cloud-type service. The above examples clearly show benefits but there are also dangers in using identity systems that target masses of people. If the risks are ignored, human rights violations and identity theft or digital intrusion will become inevitable. The risks of digital humanitarianism, however, extend beyond identity theft and digital intrusion. For example, governments in border management, counter-terrorism and law enforcement without the affected person’s knowledge can use biometrics collected for humanitarian purposes. Social media is also helpful in displacement contexts but it can also be abused if accountability measures are missing. Misinformation and hate speech are major problems on these platforms in Africa. In South Africa, for example, Operation Dudula, which started as an online campaign against foreigners in 2021, has been used for xenophobic attacks and racial discrimination against migrants. In sum, in my opinion, Africa’s main challenge is to embrace policies that give migrants mobility, access to livelihoods and basic services. Innovative technology will not solve these issues unless there are corresponding policies that safeguard migrants
Dominant narratives on migration perpetuate stereotypes, stigmatize people, and justify restrictive mobility policies. Algorithmic systems have a powerful influence on how these narratives are spread, taken up, reinforced, or resisted. These mechanisms filter and curate information flows in unpredictable ways, as algorithmic processes are largely infrastructural and therefore hidden from the user’s experience. This is particularly impactful to migrants, whose voices and stories are already marginalized as they move from one terrain to another.
This presentation presents preliminary outcomes of a participatory, co-creation project the authors are undertaking with digital content creators to counter dominant narratives. Through immersive interviews followed by co-creation workshops, we iteratively explore with recent immigrant digital content creators in the Netherlands: How do digital content creation navigate identity formation and agency within algorithmically-driven digital media ecosystems? What tactics and tools might be used to resist negative narrative frames and build more complexity into how migrants are portrayed? By building a methodology that is focused on practical and applied techniques that can be used by communities themselves for action-oriented responses, this strategic intervention aims to further the field of mobility justice.
In addition to the methods, framework, and key findings, we also discuss the toolkit we are working on with our collaborating participants to help other recent immigrant content creators critically examine their own content and consider how to playfully generate potential counter narratives.