Large language models (LLMs) have emerged as a powerful technology, and thus, we have seen widespread adoption and use on software engineering teams. Most often, LLMs are designed as "general purpose" technologies meant to represent the general population. Unfortunately, this often means alignment with predominantly Western Caucasian narratives and misalignment with other cultures and populations that engage in collaborative innovation. In response to this misalignment, there have been recent efforts centered on the development of "culturally-informed" LLMs, such as ChatBlackGPT, that are capable of better aligning with historically marginalized experiences and perspectives. Despite this progress, there has been little effort aimed at supporting our ability to develop and evaluate culturally-informed LLMs. A recent effort proposed an approach for developing a national alignment benchmark that emphasizes alignment with national social values and common knowledge. However, given the range of cultural identities present in the United States (U.S.), a national alignment benchmark is an ineffective goal for broader representation. To help fill this gap in this US context, we propose a replication study that translates the process used to develop KorNAT, a Korean National LLM alignment benchmark, to develop CIVIQ, a Cultural Intelligence and Values Inference Quality benchmark centered on alignment with community social values and common knowledge. Our work provides a critical foundation for research and development aimed at cultural alignment of AI technologies in practice.

While global AI development prioritizes model performance and computational scale, meaningful deployment in African markets requires fundamentally different architectural decisions. This paper introduces Contextual and Cultural Intelligence (CCI) -- a systematic framework enabling AI systems to process cultural meaning, not just data patterns, through locally relevant, emotionally intelligent, and economically inclusive design. Using design science methodology, we validate CCI through a production AI-native cross-border shopping platform serving diaspora communities. Key empirical findings: 89% of users prefer WhatsApp-based AI interaction over traditional web interfaces (n=602, chi-square=365.8, p<0.001), achieving 536 WhatsApp users and 3,938 total conversations across 602 unique users in just 6 weeks, and culturally informed prompt engineering demonstrates sophisticated understanding of culturally contextualized queries, with 89% family-focused commerce patterns and natural code-switching acceptance. The CCI framework operationalizes three technical pillars: Infrastructure Intelligence (mobile-first, resilient architectures), Cultural Intelligence (multilingual NLP with social context awareness), and Commercial Intelligence (trust-based conversational commerce). This work contributes both theoretical innovation and reproducible implementation patterns, challenging Silicon Valley design orthodoxies while providing actionable frameworks for equitable AI deployment across resource-constrained markets.

Cultural Intelligence (CQ) refers to the ability to understand unfamiliar cultural contexts-a crucial skill for large language models (LLMs) to effectively engage with globally diverse users. While existing research often focuses on explicitly stated cultural norms, such approaches fail to capture the subtle, implicit values that underlie real-world conversations. To address this gap, we introduce CQ-Bench, a benchmark specifically designed to assess LLMs' capability to infer implicit cultural values from natural conversational contexts. We generate a multi-character conversation-based stories dataset using values from the World Value Survey and GlobalOpinions datasets, with topics including ethical, religious, social, and political. Our dataset construction pipeline includes rigorous validation procedures-incorporation, consistency, and implicitness checks-using GPT-4o, with 98.2% human-model agreement in the final validation. Our benchmark consists of three tasks of increasing complexity: attitude detection, value selection, and value extraction. We find that while o1 and Deepseek-R1 models reach human-level performance in value selection (0.809 and 0.814), they still fall short in nuanced attitude detection, with F1 scores of 0.622 and 0.635, respectively. In the value extraction task, GPT-4o-mini and o3-mini score 0.602 and 0.598, highlighting the difficulty of open-ended cultural reasoning. Notably, fine-tuning smaller models (e.g., LLaMA-3.2-3B) on only 500 culturally rich examples improves performance by over 10%, even outperforming stronger baselines (o3-mini) in some cases. Using CQ-Bench, we provide insights into the current challenges in LLMs' CQ research and suggest practical pathways for enhancing LLMs' cross-cultural reasoning abilities.

CultureVo, Inc. has developed the Integrated Culture Learning Suite (ICLS) to deliver foundational knowledge of world cultures through a combination of interactive lessons and gamified experiences. This paper explores how Generative AI powered by open source Large Langauge Models are utilized within the ICLS to enhance cultural intelligence. The suite employs Generative AI techniques to automate the assessment of learner knowledge, analyze behavioral patterns, and manage interactions with non-player characters using real time learner assessment. Additionally, ICLS provides contextual hint and recommend course content by assessing learner proficiency, while Generative AI facilitates the automated creation and validation of educational content.

U.S. corporations spend $177 billion annually on talent development. But with labor competition intensifying amid the Great Resignation, expectations are growing. Employees are now demanding more diverse opportunities and have become outspoken about what will fuel their motivations and loyalty. Items of high importance include training and progressive diversity, equity, inclusion, and belonging (DEIB) solutions. According to PwC, 85% of women and 74% of men seek employers with diversity and inclusion strategies.

We are excited to be talking with Future of Work thought leaders about remote work and on-demand talent in our Uprisor podcast series. Clinton Bonner, VP at Topcoder, recently spoke with Davar Ardalan, the founder and storyteller in chief at IVOW AI (IVOW stands for Intelligent Voices of Wisdom). In this episode, Clinton and Davar discuss the evolution of artificial intelligence and Ardalan's work of shaping AI with cultural intelligence. Enjoy the conversation and check out highlights below. As a tech entrepreneur and storyteller, Davar is dedicated to improving the future of automation by shaping it with the important element of cultural intelligence.

Since the early eighteenth century, it has taken four industrial revolutions to reshape our social, cultural, economic and human environments. Now we are living through the fourth industrial revolution, which represents a fundamental change in the way we live, work and relate to one another. It is a new phase in human development, enabled by extraordinary technology advances, ubiquitous, mobile supercomputing, intelligent robots and assistants, autonomous cars, neuro-technological brain enhancements, predictive analytics, genetic editing and much more. The evidence of histrionic change is all around us and it's happening at an accelerated pace. Every industrial revolution introduced new skill sets, set echelons of expertise as well as defined strata of requisite experience, so the same is expected from the fourth industrial revolution.

This series on diversity and inclusion is sponsored by Amway, which supports a prosperous economy through having a diverse workplace. Companies committed to diversity and inclusion are better equipped to innovate and drive performance. The world is becoming a more diverse place. And companies that don't hire employees accordingly will lose out on crucial talent for generations to come. Standing in the way of increased workplace diversity, however, is often the unconscious biases of those doing the hiring. It's any prejudice we have without being aware that we have it.