AI-Agents for Culturally Diverse Online Higher Education Environments
- URL: http://arxiv.org/abs/2510.10520v2
- Date: Wed, 15 Oct 2025 20:49:42 GMT
- Title: AI-Agents for Culturally Diverse Online Higher Education Environments
- Abstract summary: This chapter reviews existing research and suggests the usage of culturally aware AI-Agents to foster engagement and improve learning outcomes in culturally diverse online higher education environments.
- Score: 3.479757825266597
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As the global reach of online higher education continues to grow, universities are increasingly accommodating students from diverse cultural backgrounds (Tereshko et al., 2024). This can present a number of challenges including linguistic barriers (Ullah et al., 2021), cultural differences in learning style (Omidvar & Tan, 2012), cultural sensitivity in course design (Nguyen, 2022) and perceived isolation when students feel their perspectives or experiences are not reflected or valued in the learning environment (Hansen-Brown et al., 2022). Ensuring active engagement and reasonable learning outcomes in such a environments requires distance educational systems that are not only adaptive but also culturally resonant (Dalle et al., 2024). Both embodied and virtual AI-Agents have great potential in this regard as they can facilitate personalized learning and adapt their interactions and content delivery to align with students' cultural context. In addition, Generative AI (GAI), such as, Large Language Models (LLMs) can amplify the potential for these culturally aware AI agents to address educational challenges due to their advanced capacity for understanding and generating contextually relevant content (Wang et al., 2024). This chapter reviews existing research and suggests the usage of culturally aware AI-Agents, powered by GAI, to foster engagement and improve learning outcomes in culturally diverse online higher education environments.
Related papers
- CultureScope: A Dimensional Lens for Probing Cultural Understanding in LLMs [57.653830744706305]
CultureScope is the most comprehensive evaluation framework to date for assessing cultural understanding in large language models.<n>Inspired by the cultural iceberg theory, we design a novel dimensional schema for cultural knowledge classification.<n> Experimental results demonstrate that our method can effectively evaluate cultural understanding.
arXiv Detail & Related papers (2025-09-19T17:47:48Z) - Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America [45.28398871440121]
We aim to construct a rich and human-centred understanding of potential areas of cultural misalignment in digital health.<n>Our findings show that academic boundaries on notions of culture lose meaning at the ground level.<n>We introduce a framework for 'Pluriversal Conversational AI for Health' which allows for the possibility that more relationality and tolerance, rather than just more data, may be called for.
arXiv Detail & Related papers (2025-07-02T13:48:25Z) - Against 'softmaxing' culture [0.21756081703275998]
I call this phenomenon "softmaxing culture," and it is one of the fundamental challenges facing AI evaluations today.<n>I propose two key conceptual shifts. First, instead of asking "what is culture?" at the start of system evaluations, I propose beginning with the question: "when is culture?"<n>I acknowledge the philosophical claim that cultural universals exist, but the challenge is not simply to describe them, but to situate them in relation to their particulars.
arXiv Detail & Related papers (2025-06-28T17:59:17Z) - Between Regulation and Accessibility: How Chinese University Students Navigate Global and Domestic Generative AI [13.558080205608668]
This study investigates how Chinese university students interact with both global and domestic generative AIs in the learning process.<n>Findings reveal that engagement is shaped by accessibility, language proficiency, and cultural relevance.<n>It advocates for human-centered, multilingual, domestic context-sensitive AI integration to ensure equitable and inclusive digital learning environments.
arXiv Detail & Related papers (2025-06-17T10:24:12Z) - CAReDiO: Cultural Alignment of LLM via Representativeness and Distinctiveness Guided Data Optimization [50.90288681622152]
Large Language Models (LLMs) more deeply integrate into human life across various regions.<n>Existing approaches develop culturally aligned LLMs through fine-tuning with culture-specific corpora.<n>We introduce CAReDiO, a novel cultural data construction framework.
arXiv Detail & Related papers (2025-04-09T13:40:13Z) - AI as a deliberative partner fosters intercultural empathy for Americans but fails for Latin American participants [4.641694322213005]
Cross-cultural deliberation increased intercultural empathy among American participants through positive emotional engagement.<n>Our analysis of participant-driven feedback, where users directly flagged and explained culturally inappropriate AI responses, revealed systematic gaps in AI's representation of Latin American contexts.<n>Our work advances both deliberation theory and AI alignment research by revealing how the same AI system can simultaneously promote intercultural understanding for one cultural group while failing for another.
arXiv Detail & Related papers (2025-04-04T17:15:01Z) - CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries [63.00147630084146]
Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding.<n>CultureVerse is a large-scale multimodal benchmark covering 19, 682 cultural concepts, 188 countries/regions, 15 cultural concepts, and 3 question types.<n>We propose CultureVLM, a series of VLMs fine-tuned on our dataset to achieve significant performance improvement in cultural understanding.
arXiv Detail & Related papers (2025-01-02T14:42:37Z) - From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents [78.15899922698631]
MAIC (Massive AI-empowered Course) is a new form of online education that leverages LLM-driven multi-agent systems to construct an AI-augmented classroom.
We conduct preliminary experiments at Tsinghua University, one of China's leading universities.
arXiv Detail & Related papers (2024-09-05T13:22:51Z) - The Honorific Effect: Exploring the Impact of Japanese Linguistic Formalities on AI-Generated Physics Explanations [0.0]
This study investigates the influence of Japanese honorifics on the responses of large language models (LLMs) when explaining the law of conservation of momentum.
We analyzed the outputs of six state-of-the-art AI models, including variations of ChatGPT, Coral, and Gemini, using 14 different honorific forms.
arXiv Detail & Related papers (2024-07-12T11:31:00Z) - CulturePark: Boosting Cross-cultural Understanding in Large Language Models [63.452948673344395]
This paper introduces CulturePark, an LLM-powered multi-agent communication framework for cultural data collection.
It generates high-quality cross-cultural dialogues encapsulating human beliefs, norms, and customs.
We evaluate these models across three downstream tasks: content moderation, cultural alignment, and cultural education.
arXiv Detail & Related papers (2024-05-24T01:49:02Z) - CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs' (Lack of) Multicultural Knowledge [69.82940934994333]
We introduce CulturalTeaming, an interactive red-teaming system that leverages human-AI collaboration to build challenging evaluation dataset.
Our study reveals that CulturalTeaming's various modes of AI assistance support annotators in creating cultural questions.
CULTURALBENCH-V0.1 is a compact yet high-quality evaluation dataset with users' red-teaming attempts.
arXiv Detail & Related papers (2024-04-10T00:25:09Z) - How Culture Shapes What People Want From AI [0.0]
There is an urgent need to incorporate the perspectives of culturally diverse groups into AI developments.
We present a novel conceptual framework for research that aims to expand, reimagine, and reground mainstream visions of AI.
arXiv Detail & Related papers (2024-03-08T07:08:19Z) - Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
This paper introduces a novel approach for massively multicultural knowledge acquisition.
Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages.
Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
arXiv Detail & Related papers (2024-02-14T18:16:54Z) - Culturally Responsive Artificial Intelligence -- Problems, Challenges
and Solutions [0.9065034043031668]
This paper explores the socio-cultural and ethical challenges stemming from the implementation of AI algorithms.
It highlights the necessity for their culturally responsive development.
It also advocates the significance of AI enculturation and underlines the importance of regulatory measures to promote cultural responsibility in AI systems.
arXiv Detail & Related papers (2023-12-13T19:09:45Z) - An Analytics of Culture: Modeling Subjectivity, Scalability,
Contextuality, and Temporality [13.638494941763637]
There is a bidirectional relationship between culture and AI; AI models are increasingly used to analyse culture, thereby shaping our understanding of culture.
On the other hand, the models are trained on collections of cultural artifacts thereby implicitly, and not always correctly, encoding expressions of culture.
This creates a tension that both limits the use of AI for analysing culture and leads to problems in AI with respect to cultural complex issues such as bias.
arXiv Detail & Related papers (2022-11-14T15:42:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.