Inclusive AI for Language, Learning & Health

Open-weight large language models are increasingly used in education, hiring, and information-seeking contexts that touch neurodivergent people, yet we lack a clear empirical map of how their portrayals of neurodiversity shift under everyday prompt variation. We present a multi-condition, multi-model audit of LLM portrayals of eight neurodivergent identities (autism, ADHD, dyslexia, dyspraxia, dyscalculia, Tourette syndrome, OCD, and sensory-processing differences) plus a non-stigmatized medical control. Six open-weight models answer a pre-registered prompt bank that crosses seed items with seven perturbation families, including paraphrase, identity-first versus person-first language, system personas, plausible versus obviously fictional authority citations, multi-turn social pushback, leading questions, and two mitigation arms. Held-out LLM judges score each generation on three ordinal axes. Baseline portrayals are remarkably homogeneous across models, so the inconsistency observed under prompt variation is prompt-driven, not model-driven. Steering toward deficit framing is several times easier than steering toward neurodiversity affirmation. Emotional pushback shifts outputs more than evidence-laden pushback. Plausible-sounding fake citations slip past safety filters while obviously fictional ones do not. A single-line system-prompt mitigation recovers a meaningful share of the steering range at near-zero refusal cost on neutral prompts, though refusal rises sharply under adversarial pressure. The governance question for disability-relevant LLM deployment is therefore not whether the model is biased but who controls the steering, and with what accountability.

This formative study explores how second-language (L2) speakers envision personal AI assistants (PAIAs) supporting face-to-face communication with native speakers. Through participatory sessions with 14 bilingual adults combining scenario generation, role-play, and diagramming, we surfaced preliminary tensions in AI-mediated L2 interaction: managing conversational flow, balancing transparency with naturalness, and navigating context-dependent privacy. Our exploratory findings suggest three candidate mediation patterns (solo, symmetric, and relayed assistance), each with distinct trade-offs that warrant further investigation. We contribute initial design considerations including reply-when-free defaults and role-based privacy profiles, alongside a replicable protocol for eliciting AI behavior expectations. These preliminary insights establish a foundation for future empirical work on PAIAs in multilingual communication contexts.

Embodied Conversational Agents (ECAs) are increasingly used for health education, yet the role of an agent's physical embodiment remains relatively underexplored. This study examines how body mass index (BMI) cues in an embodied agent relate to user perceptions, trust in medical content, and health‐related intentions during a brief dietary counseling interaction focused on diabetes prevention. In a between‐subjects experiment, participants interacted with one of three visually distinct versions of the same healthcare coach, representing underweight, normal‐weight, or obese body types. Across conditions, participants demonstrated improvements in dietary intentions, while significant increases in diabetes memory outcomes were observed only in the underweight and normal‐weight conditions. Increases in overall behavior change intention were observed only in the underweight condition. Subjective evaluations indicated that the normal‐weight agent was rated higher on realism, likability, and interaction quality, while post‐interaction perception ratings suggested that more extreme body representations were sometimes perceived as less comfortable or less appropriate for a health advisory role. Together, these results suggest that visual body type cues may be associated with differences in users' subjective experiences of embodied health counseling agents, while evidence for broader differences in learning and motivation‐related outcomes should be interpreted cautiously.

The prevalence of remote instruction has heightened the need to understand how emerging avatar technologies might support instructor presence in virtual classrooms. Digital twins, defined here as animated avatars that mirror a speaker’s facial expressions and movements in real time, represent a potential alternative to standard webcam video. This formative study examines student perceptions following exposure to a digital twin instructor during live Zoom lectures. Twenty-four students completed a post-session survey assessing perceived realism, emotional engagement, and willingness to attend future sessions. Spearman correlation analysis revealed that affective qualities such as likeability and enjoyment showed strong positive associations with receptivity, while perceptions of awkwardness and weirdness showed strong negative associations. Perceived natural movement was strongly associated with human resemblance and inversely associated with eeriness, consistent with uncanny valley theory. These exploratory findings identify candidate factors for future controlled investigations and offer preliminary design guidance emphasizing behavioral fidelity and emotional warmth over photorealism alone.

This article presents a systematic literature review, following the PRISMA methodology, on the relevance of large language models (LLMs) such as ChatGPT in education in Latin America between January 2023 and April 2025. More than one hundred academic sources from recognized databases (Scopus, SciELO, Redalyc, ArXiv, IEEE Xplore, and Google Scholar) were analyzed, prioritizing empirical studies (quantitative, qualitative, and mixed methods), as well as theoretical articles, previous systematic reviews, and relevant regulatory or technical frameworks. The review covers all educational levels (basic, secondary, and higher education, and teacher training) and includes literature in both Spanish and English focused on Latin American contexts. The results reveal a rapid increase in research on LLMs in Latin American educational settings, with findings showing largely positive perceptions of the potential of tools such as ChatGPT to personalize teaching, improve learning, and foster skills such as critical and creative thinking, while also highlighting significant concerns regarding academic integrity, gaps in access, language bias, and the need for teacher training. The implications of these findings for educational practice in the region are discussed, including pedagogical strategies, ethical considerations, and public policy. Finally, recommendations are proposed for the responsible integration of generative AI into Latin American education, emphasizing the importance of approaches that harness its benefits while mitigating its risks.

This research explores the difficulties advanced second-language learners face in becoming fluent and the poten-tial of AI to enhance language learning apps. Through surveys, focus groups, and collaborative design sessions, we investigated learners' difficulties and practice habits. The participants created paper prototypes for an app targeting speaking and listening skills at an advanced level. Key findings suggest that fluency de-velopment requires exposure to diverse accents from both native and non-native speakers. The resulting design incorporates AI in the form of a chatbot with voice recognition capabilities. This study contributes to our understanding of advanced language learning needs and the potential of AI to address them, paving the way for innovative language learning solutions. Our findings highlight opportunities to develop more effective tools for learners striving to achieve fluency in their target languages.

Crowdsourcing markets are expanding worldwide, but often feature standardized interfaces that ignore the cultural diversity of their workers, negatively impacting their well-being and productivity. To transform these workplace dynamics, this paper proposes creating culturally-aware workplace tools, specifically designed to adapt to the cultural dimensions of monochronic and polychronic work styles. We illustrate this approach with "CultureFit," a tool that we engineered based on extensive research in Chronemics and culture theories. To study and evaluate our tool in the real world, we conducted a field experiment with 55 workers from 24 different countries. Our field experiment revealed that CultureFit significantly improved the earnings of workers from cultural backgrounds often overlooked in design. Our study is among the pioneering efforts to examine culturally aware digital labor interventions. It also provides access to a dataset with over two million data points on culture and digital work, which can be leveraged for future research in this emerging field. The paper concludes by discussing the importance and future possibilities of incorporating cultural insights into the design of tools for digital labor.

In this paper, we explore the use of Large Language Models (LLMs) to help students improve their information-seeking skills while encouraging the use of references to aid library literacy efforts. This study aims to expand the reach of library support by introducing an approach that leverages the capabilities of LLMs and well-structured prompts. Our approach begins with surveying the current changes students have faced in the last two years concerning their study habits and how they search for information. We subsequently propose a multi-step system prompt, referred as prompting architecture, for foundational and instructed LLMs. The proposed prompt architecture powers a web application named LibRef. We explore the adaptability of the prompting architecture to different information retrieval needs by refining search prompts and providing academic references. A field experiment is conducted using LibRef in academic settings. Our results suggest that the use of LibRef enhances students’ academic information-seeking experience. Our research underscores the potential of prompting architectures in procedural refinement of academic queries from students. We believe our findings can provide valuable insights on the current capabilities of LLMs for instructing students to provide more targeted prompts as well as incentivize the use of references.

This paper presents an exploration of the versatility of prompting architectures in large language models (LLMs), expanding the horizons of their application in learning and language interfaces. By leveraging the expansive capabilities of LLMs, this research probes the potential for creating structured prompts that can simultaneously support multiple use cases, namely paraphrasing, grammatical syntax guidance for introductory sentences, and conducting experiential conversations in a foreign language. In this study, we delve into the specifics of enabling such technology, including the design of the prompting architecture, the deployment process, and the intricacies of applying the same structure across diverse applications. An extensive field experiment incorporating interfaces powered by LLMs using this structured prompt has been conducted to evaluate the model's efficiency in real-world scenarios. Results from the field experiment highlight the promising adaptability of these prompting architectures, revealing remarkable efficiency across the multiple use cases explored. Furthermore, this research uncovers a new dimension of flexibility in the design and deployment of learning applications using LLMs, potentially revolutionizing language learning interfaces by establishing a one-size-fits-all solution. This paper aims to stimulate further research into refining and expanding the potential of LLMs, encouraging the exploration of how artificial intelligence can optimally benefit language learning and related applications.

Crowdsourcing platforms gather people from different backgrounds to work on completing microtasks. The workers on those platforms are presented with tasks defined by requesters from all over the world. The tasks are defined in multiple languages, with English being the predominant language. It is unclear how language barriers can affect workers whose primary language is not English while completing tasks. In this paper, we study their practices and the impact of AI-powered tools on the completion of microtasks. We conduct a pre-test test field experiment in which workers share their current experience with the platform and receive basic training in the use of AI-powered tools. After a week, workers provide feedback on their practices and experiences. We find that workers who complete tasks in their primary language report less confidence in completing English tasks, and in general, workers perceive the use of the tools as a language-learning mechanism. We aim that the understanding of language barriers in crowd markets can promote more inclusive work conditions.

There has been little effort in conceptualizing indigeneity in social computing, despite the concept being central to decolonial and postcolonial perspectives, which scholars have increasingly used in computing research for over a decade. It is crucial to reflect on who can be considered indigenous in the spirit of inclusion and reclamation since the underdevelopment of this concept and the nuances, differences, relationships, and overlaps between indigeneity and colonial marginalization may silence different populations in research. The workshop aims to bring together scholars whose works are associated with different local and indigenous cultures and their technology practices and experiences to initiate conversations around three themes: (a) defining indigeneity and identifying indigenous communities in social computing, (b) recognition in different sociopolitical contexts, and (c) contributions to social computing.

Large Language Models (LLMs), like ChatGPT, are fundamentally tools trained on vast data, reflecting diverse societal impressions. This paper aims to investigate LLMs' self-perceived bias concerning indigeneity when simulating scenarios of indigenous people performing various roles. Through generating and analyzing multiple scenarios, this work offers a unique perspective on how technology perceives and potentially amplifies societal biases related to indigeneity in social computing. The findings offer insights into the broader implications of indigeneity in critical computing.