AI-Mediated Language and Communication

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.

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.

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.

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.