<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Background:</i></b> This exploratory study examines the emerging requirements of artificial-intelligence (AI) literacies reflected in technical and professional communication (TPC) job postings and provides empirical insights into how employers articulate these literacies in hiring practices. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Literature review:</i></b> We situate this study within the existing scholarship regarding job postings analysis in TPC, AI in TPC, and AI literacy. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Research questions:</i></b> How do employers reference AI literacy in TPC job postings? How are AI literacy dimensions distributed across the TPC job description domains? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Methodology:</i></b> We collected job postings published between March 1 and June 10, 2025, across ten major online recruitment platforms, focusing on US-based positions that fall into six TPC job categories that explicitly referenced AI. This process yielded 137 eligible postings for analysis. Employing a multistage and mixed-method design for data analysis, we coded AI literacies using the AI literacy framework and coded the job descriptions using the four TPC job description domains. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Results:</i></b> Our quantitative findings show growing integration of AI literacy into TPC roles, with uneven emphasis on specific AI literacy dimensions across TPC positions. Employers prioritize practical applications of AI tools, while ethical considerations receive little attention. Our topical modeling analysis also indicates that AI literacy is an integrative skill and functions as an embedded capability in TPC jobs. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Conclusion:</i></b> This study has significant implications for TPC educators. AI literacy should be taught as an integrative skill that augments human expertise. TPC programs should embed AI instruction across the curriculum while continuing to emphasize the core competencies essential to professional communication.

Background: Corporate social responsibility (CSR) reports are essential for companies to persuade stakeholders of their commitment to social, economic, and environmental responsibilities. This persuasion is to a large degree determined by how companies construct their stance in discourse. Literature review: Among efforts in academic discourse, stance analysis of shipping industry CSR reporting remains unexplored. Research questions: 1. How are stance resources distributed in COSCO Shipping and Maersk's CSR discourse? 2. How do similarities and differences between institutional speakers instantiate their stakeholder-oriented communication strategies? 3. Which stance markers show significant changes over time, and what factors drive these changes? Methodology: This study employed corpus linguistics and discourse analysis of CSR reports (2016-2022) of COSCO Shipping and of Maersk. Python and WordSmith 8.0 were used for stance feature retrieval and frequency analysis, and hierarchical clustering analysis was conducted on hedges and boosters. Chi-square tests evaluated differences in stance marker distribution, while diachronic analysis examined changes over time. Results and conclusion: Maersk employs more stance markers of hedges, attitude markers, and general self-mentions, reflecting a more personal communication style, while COSCO Shipping favors boosters and formal self-references, indicating an authoritative approach. We argue that these features are informed by the contrast between European rationalism and Eastern empiricism in corporate communication. Our novel four-category classification of self-mentions (general, specific reference, group, and affiliated) addresses the complexities of shipping corporate discourse. Diachronic analysis shows stance marker usage fluctuations, particularly during global events like the COVID-19 pandemic. These findings develop a stance framework, offering insights in terms of stance construction for effective cross-cultural CSR communication to foster global cooperation on shared social responsibilities.

About the case: This case study examines how students, instructors, and industry partners in university and industry collaborative translation projects perceive and respond to their experiences with these projects. It reports on collaborative translation projects at Xi'an International Studies University, involving the active participation of three stakeholders. Situating the case: Translation and technical and professional communication (TPC) education both prioritize cultivating students’ practical skills of producing audience-centered content. They both value collaborative approaches as effective methods for bridging the gap between academia and industry. Although TPC education aims to foster students’ technical and professional writing competence and prepare them for industry demands, translation education focuses on developing students’ translation competence through context-dependent learning. Various models have been proposed to define translation competence, with the multicomponent model being the most prominent. This model emphasizes multiple skills and attributes that translators need, reflecting the influence of market demands and technological advancements. Collaborative approaches like project-based learning (PjBL) have been advocated to bridge the gap between academia and industry. However, research at the program level and from stakeholders’ perspectives remains limited. Methods: We adopted a mixed-method strategy and employed surveys and one-on-one interviews to examine the responses from the stakeholders about their experiences and the impacts of the collaborative translation projects. Results: Statistical analysis of the surveys and thematic analysis of interviews and open-ended questions indicate that all stakeholders concur with the projects’ benefits in enhancing students’ translation competence and shortening the gap between classroom translation education and market demands. Despite these benefits, all stakeholders face various challenges that should be addressed by expanding the scalability and sustainability of collaborative projects, fostering closer and direct collaboration between instructors and industry partners, and enhancing the ethics of professional code and strategic competence of students. Conclusion: Based on the research results, we suggest further research directed at the sustainability of these projects and the scalability of their benefits toward a bigger population of students.