Making Sense of Complexity: Typologies of Artificial Intelligence

 

Image Credit: Microsoft Stock Images

    

Artificial intelligence (AI) influences many aspects of modern life and has multiple applications. AI is the ability of machines or software to perform tasks that are commonly associated with human intelligence, such as recognizing patterns, making decisions, or learning from data. AI is designed to mimic human capabilities, including pattern recognition, data analysis, and decision-making, and to perform tasks rapidly and efficiently.

 

Algorithms are a set of problem-solving steps computer programs use to accomplish tasks. AI operationalizes the algorithmic steps in smart machines that perform tasks usually associated with human intelligence such as “learning, adapting, synthesizing, self-correction, and use of data for complex processing” (Popenici & Kerr, 2017, para. 3). Machine learning is an application of AI in which large data sets are analyzed, without direct instruction, to detect patterns that might elude human beings. Generative AI is an artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data.

 

AI originated in the 20^th century, but only recently have computers had the computational power to make it practical and useful (Anyoha, 2017). Most people are using AI without recognition because AI powers internet search platforms, predictive text, grammar- and spell-check, GPS, social media curation, smart devices, streaming services, and patient portals. Many people conflate generative AI with large-language models such as those used within ChatGPT, but this is only one type of AI.

 

Typologies of AI

It is essential to recognize that AI is multifaceted and has multiple applications. Therefore, it can be categorized in multiple ways: based on capability, functionality, application, or degree of supervision vs. autonomy.

 

Capability

One capability-based categorization is weak and strong or general AI. Narrow or weak AI can perform single-specific tasks such as making Netflix recommendations, facial recognition, self-driving cars, searching the internet, or translating languages. General or Strong AI can perform tasks in a human-like manner (AVContent Team, 2023). Some descriptions differentiate general AI from strong AI, with the former referring to a computer that is as smart as a human in a general sense and the latter referring to computers that have achieved human consciousness. The latter category is still somewhat theoretical because AI has not yet achieved consciousness or self-awareness. This counteracts the idea that sentient robots will take over the world and enslave humans, as many science fiction novels and films would have people believe. Think of the Terminator or 2001: A Space Odyssey. 

 

Another capability-based typology characterizes four levels of AI: (a) reactive, (b) limited memory, (c) theory of mind, and (d) self-awareness. Consistent with the weak vs strong typology, this conceptualization indicates that AI has not yet achieved theory of mind, meaning the capacity “to understand and remember other entities' emotions and needs and adjust their behavior based on these. This capability is like humans in social interaction” (Arya, 2023, para. 13). Humans develop this capacity as they mature. They also develop self-awareness and emotional intelligence, while AI does not.

Four Levels of AI

Functionality

One functionality categorization scheme asserts three categories of AI: (a) large language models (LLM), (b) learning analytics in which personalized learning is tailored for individuals, and (c) big data, meaning using large data sets to conduct comparative analysis between groups of people. These can be expressed in input and output, instructor and student, or data and functions.

Another functionality schematic suggests the following categories:

·       Analytic AI scans large datasets to identify, interpret, and communicate meaningful patterns of data.

·       Functional AI scans huge amounts of data to take actions.

·       Interactive AI automates communication without compromising on interactivity.

·       Text AI uses semantic search and natural language processing to build semantic maps and recognize synonyms to understand the context of user’s question.

·       Visual AI identifies, recognizes, classifies, and sorts objects or converts images and videos into insights. (Sarker et al., 2022).

Application

Yet another way of categorizing AI is by applications in which it is used. For example, expert systems use information collected from recognized domain experts to facilitate fast decision-making. Natural language processing (NLP) enables AI to use language in a human-like manner in chatbots, language translation, and sentiment analysis, which is used to determine whether the emotional tone of a message is positive, negative, or neutral. Sentiment analysis has become an important business function used to improve customer service, market research, and to monitor brand performance. It can distinguish the positive from the negative of a seemingly contradictory sentence such as: “While I liked this product, I was disappointed with the color.”

Supervision vs. Autonomy

This typology is often used to describe the process of machine learning, in which:

·       Supervised learning—all data are labeled.

·       Semi-supervised—some input data are labeled, while some are not.

·       Unsupervised—all input data are unlabeled (Alloghani et al., 2020).

 

These terms can also be used to describe AI. Examples of supervised processing include virtual assistants such as Siri and Alexa, while an example of unsupervised or autonomous processing is self-driving cars.

You may notice overlaps between the different typologies, as the following concept map clarifies.

No matter how you conceptualize it, the field of AI is complex, growing, and rapidly being integrated into multiple fields of professional practice. These typologies highlight the diverse nature of AI, and the various systems designed for specific purposes and possessing different levels of capabilities. The field of AI continues to advance and new typologies may be developed as its capacities evolve.

 

References

Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., Aljaaf, A.J. (2020). A systematic review on supervised and unsupervised machine learning algorithms for data science. In M. Berry, A. Mohamed, & B. Yap (Eds.), Supervised and unsupervised learning for data science. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-22475-2_1

 Anyoha, R. (2017, August 28). The history of artificial intelligence. Harvard University. Retrieved https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/

 Arya, N. (2023, November 16). Theory of mind AI in artificial intelligence. Ejable. Retrieved from https://www.ejable.com/tech-corner/ai-machine-learning-and-deep-learning/theory-of-mind-ai-in-artificial-intelligence/#:~:text=Theory%20of%20Mind%3A%20This%20is,like%20humans%20in%20social%20interaction.

AVContent Team (2023, September 14). Weak AI vs strong AI: Exploring key differences and future potential of AI. Analytics Vidhya. Retrieved https://www.analyticsvidhya.com/blog/2023/04/weak-ai-vs-strong-ai/

 Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning,12, 22. https://doi.org/10.1186/s41039-017-0062-8

 Sarker, I.H.  (2022). AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science,. 3, 158. https://doi.org/10.1007/s42979-022-01043-x

 

President Biden Issues Executive Order to Establish Standards for AI Technologies

 

By Lilian H. Hill

On October 30, 2023, President Biden issued an executive order to establish Artificial Intelligence (AI) safety and security standards, protect Americans’ privacy and security, advance equity and civil rights, and advocate for consumers and workers. It employs broad emergency powers, usually only invoked for urgent situations such as the coronavirus pandemic or war, and the power of multiple government agencies to address the risks of artificial intelligence, which Biden described as the “most consequential technology of our time.” Biden has also called on Congress to create legislation to regulate AI as multiple attempts have failed to pass.

 

Released just days before an international AI Safety Summit held in the UK (Zakrzewski et al., 2023), the 111-page Executive Order has seven focus areas: 

• safety
• protection of American’s privacy 
• preventing bias 
• supporting consumers, students, and patients 
• supporting workers 
• promoting innovation 
• advancing American leadership abroad

 

Selected details are described for each focus area below.

 

Safety

 

• AI corporations will be required to conduct safety assessments of their products and submit findings to the federal government before implementing AI technology.
• Safeguards should be in place to shield Americans from AI-facilitated fraud and deception.
• Protocols and best practices will be established to detect AI-generated content and validate official content.
• A sophisticated cybersecurity initiative will be developed to identify and rectify vulnerabilities in critical software.

Protection of Americans Privacy

• Federal backing for expediting the development and application of privacy-preserving methods will be utilized, incorporating the use of cryptographic tools.
• Efforts will be made to strengthen the methods by which federal agencies collect and use commercially available information, alongside privacy guidelines to tackle AI-related risks.

Preventing Bias

• Standards will be formulated to furnish landlords, federal benefits programs, and federal contractors with precise guidelines to prevent AI algorithms from exacerbating discrimination.
• The Department of Justice and federal civil rights personnel will receive training to address algorithmic discrimination and to adopt best practices for investigating and prosecuting AI-related civil rights violations.
• Fairness will be encouraged throughout the criminal justice system by outlining best practices for the use of AI in sentencing, parole and probation, pretrial release and detention, risk assessments, surveillance, crime forecasting and predictive policing, and forensic analysis.

Stand Up for Consumers, Students, and Patients

• The responsible use of AI in healthcare will be promoted, with the Department of Health and Human Services establishing a safety program to receive reports of, and take action against, harms or unsafe healthcare practices involving AI.
• Efforts will be made to shape AI's potential to revolutionize education by creating resources to support educators deploying AI-driven educational tools, such as personalized tutoring in schools.

Promoting Innovation

• AI research across the United States will receive support by initiating the National AI Research Resource pilot program, providing AI researchers and students access to vital AI resources and data and increased grants for AI research in critical areas such as healthcare and climate change.
• An equitable, open, and competitive AI ecosystem will be encouraged by granting small developers and entrepreneurs access to technical aid and resources, assisting small businesses in commercializing AI breakthroughs, and encouraging the Federal Trade Commission to exercise its authority.
• The opportunities for highly skilled immigrants and nonimmigrants with expertise in crucial fields to study, remain, and work in the United States will be expanded by modernizing and streamlining visa criteria, interviews, and reviews.

Supporting Workers

• Principles and best practices will be devised to mitigate the negative impacts and maximize the benefits of AI for workers, addressing job displacement, labor standards, workplace equity, health and safety, and data collection.
• A report will be compiled on AI's potential effects on the labor market, and strategies to bolster federal support for workers facing labor disruptions, including those resulting from AI, will be developed.
• The opportunities for highly skilled immigrants and nonimmigrants with expertise in crucial fields to study, remain, and work in the United States will be expanded by modernizing and streamlining visa criteria, interviews, and reviews.

Advancing American Leadership Abroad

• Strengthen bilateral, multilateral, and multi-stakeholder engagements to collaborate on AI with the State Department, in conjunction with the Commerce Department, leading efforts to establish robust international frameworks for harnessing AI's benefits and managing its risks, ensuring safety.
• Accelerate the development and implementation of crucial AI standards with international partners and in standards organizations, ensuring the technology's safety, security, trustworthiness, and interoperability.
• Advocate for the safe, responsible, and rights-affirming development and deployment of AI globally to tackle global challenges, such as advancing sustainable development and mitigating threats to critical infrastructure.

Ensuring Responsible and Effective Government Use of AI

• Issue guidelines for agencies' use of AI, including clear standards to protect rights and safety, improve AI procurement, and strengthen AI deployment.
• Assist agencies in procuring specified AI products and services more quickly, affordably, and effectively through streamlined contracting processes.
• Expedite the rapid hiring of AI professionals as part of a government-wide AI talent surge led by the Office of Personnel Management, U.S. Digital Service, U.S. Digital Corps, and Presidential Innovation Fellowship. Agencies will provide AI training for employees at all levels in relevant fields.

 

This Executive Order is far-reaching and comprehensive. It builds on and expands the considerations articulated in the White House Blueprint for an AI Bill of Rights. However, news commentary indicates that the Executive Order is only a first step in regulating rapid AI development and implementation, but also indicates that the Executive Order needs to be improved in enforcement mechanisms.

 

References

The White House. (2023, October 30). Fact Sheet: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence. Retrieved from https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/

The White House Office of Science and Technology Policy (n.d.). Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People. Retrieved https://www.whitehouse.gov/ostp/ai-bill-of-rights/#safe

Zakrzewski, C., Lima, C., & Pager, T. (2023, October 25). White House to unveil sweeping AI executive order next week. Washington Post. Retrieved https://www.washingtonpost.com/technology/2023/10/25/artificial-intelligence-executive-order-biden/?itid=ap_catzakrzewski

Digital Health Literacy Access and Skills

 

Image credit: Pexels, Telehealth

By Lilian H. Hill, PhD

Health literacy and digital health literacy are related but distinct ideas. This blog post is part of our series on different forms of literacy in which we provide definitions of health literacy, digital health literacy, and eHealth literacy. 

Health Literacy

To understand digital health literacy, a definition of health literacy is needed. In Healthy People 2030 (2023), the U.S. Department of Health and Human Services (HHS) provided an updated definition of health literacy that has two components: personal health literacy and organizational health literacy:

• Personal health literacy is the degree to which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others.
• Organizational health literacy is the degree to which organizations equitably enable individuals to find, understand, and use information and services to inform health-related decisions and actions for themselves and others. (para. 3)

Begun in 1980 and occurring decennially, the Healthy People initiative sets priority areas to improve population health, provides implementation tools, and tracks progress. This updated definition acknowledges the responsibilities of health providers and systems to communicate effectively with patients of varying identities, language skills, and literacy levels. Older definitions only included reference to personal health literacy skills, burdening patients. 

Digital Health Literacy

Digital health literacy refers to accessing health information online and navigating and using digital or electronic health information and patient resources. It includes electronic patient portals, technology for telehealth visits, and using computers and mobile devices to access medical information and interact with healthcare teams.

The World Health Organization defines electronic health (eHealth) services as the cost-effective and secure use of information communication technologies to support health. Examples include electronic communication between patients and providers, electronic medical records, patient portals, and digital personal health records. A category of eHealth is mobile health (mHealth), including phones, tablets, and computers to use applications (apps), wearable monitoring devices, and texting services. The Centers for Disease Control and Prevention (CDC) defines eHealth literacy as the ability to evaluate health information from electronic sources and apply the knowledge gained to addressing or solving a health problem (CDC).

Digital health literacy involves skills including: 

• Accessing and using online medical scheduling platforms to make appointments.
• Using and navigating electronic health records and patient portals.
• Receiving text message reminders from healthcare providers.
• Receiving digital health information instead of handouts (for example, information about medication instructions for medication adherence).
• Obtaining results of medical or diagnostic tests online.
• Searching for and evaluating online health information. (Rural Health Information Hub, n.d.).
• Comparing options and enrolling in a health insurance plan on a government website.
• Searching online for healthy recipes to prepare for a family member with health conditions such as hypertension or diabetes.

Unfortunately, these skills depend on computer and mobile device access, digital tools experience, and a robust broadband network. For example, people with limited income and live in remote rural areas need help accessing broadband. Estimates of people lacking access range between 21 to 162 million (Stauffer et al., 2020). The U.S. government announced investing over 40 billion dollars to extend broadband access to all Americans (The White House, 2023). Access depends on having a data plan with broadband access, yet 40% of low-income households are not subscribed to any data plans. Relying on limited cell phone data or public Wi-Fi spots has limitations, including interruptions and a lack of security and privacy (Sieck, 2021). 

Many healthcare organizations have invested heavily in digital resources to support patient healthcare. Research indicates these tools “can foster greater patient engagement, better support for patients outside of the clinic visit, and can improve health outcomes” (Sieck, 2021, p. 1).

Digital health literacy has become so important to healthcare that it is now included as one of the social determinants of health, the conditions in the environments where people live, learn, work, and play that influence human health, functioning, and quality of life (Sieck et al., 2021). Other elements include: 

• safe housing, transportation, and neighborhoods;
• racism, discrimination, and violence;
• education, job opportunities, and income;
• access to nutritious foods and physical activity opportunities;
• polluted air and water; and 
• language and literacy skills (USHHS, 2023). 

As clinical care delivery is quickly being integrated with digital technologies, Sieck et al. (2021) recommend that healthcare organizations adopt digital inclusive strategies, including assessing patient literacy and access and partnering with community organizations to facilitate digital skills training and connectivity. 

References

• Improving Digital Health Literacy. Rural Health Information Hub. Retrieved from https://www.ruralhealthinfo.org/toolkits/health-literacy/2/digital-literacy 
• USHHS (2023, July 26). Healthy People 2030. Retrieved from https://health.gov/our-work/national-health-initiatives/healthy-people/healthy-people-2030 
• Sieck, C. J., Sheon, Amy, Ancker, J. S., Castek, J., Callahan, B. & Siefer, A. (2021). Digital inclusion as a social determinant of health. npj Digital Medicine 4, 52.; https://doi.org/10.1038/s41746-021-00413-8 
• Stauffer, A., de Wit, K., Read, A., & Kitson, D. ((2020). How States Are expanding broadband access: New research identifies tactics for connecting unserved communities. Pew Charitable Trust. Retrieved from https://www.pewtrusts.org/-/media/assets/2020/03/broadband_report0320_final.pdf 
• U.S. White House (2023, February 26). Biden-⁠Harris Administration Announces Over $40 Billion to Connect Everyone in America to Affordable, Reliable, High-Speed Internet. Retrieved from https://www.whitehouse.gov/briefing-room/statements-releases/2023/06/26/fact-sheet-biden-harris-administration-announces-over-40-billion-to-connect-everyone-in-america-to-affordable-reliable-high-speed-internet/ 

Cultural Competence, Cultural Humility, and Intercultural Literacy

Image credit: Lillian H. Hill

By Lilian H. Hill, PhD

The multiplicity of terms related to effective intercultural interactions confirms the need for theory development and educational initiatives to develop people’s skills. Terms that have been used include cultural competence, cultural humility, intercultural literacy, cross-cultural and multicultural interaction, cultural literacy, intercultural competence, and global competence (Schliakhovchuk, 2021). This article examines three related concepts: cultural competence, cultural humility, and intercultural literacy.

Cultural Competence

Cultural competence is defined as the ability to understand one’s own cultural identity, understand and respect the cultural identities of others, and seek to understand how the various cultural realities may differ and intersect to form relationships of mutual respect, dignity, and service to others (Lekas, 2020). Within professional settings, cultural competence involves congruent attitudes, behaviors, and policies that serve intercultural interactions (Arredondo, 2013). To be culturally competent, a person must possess an internal desire to understand the various cultural beliefs and values of others, consider how these values affect life decisions, actions, and goals, and be able to integrate these into interpersonal relationships. For example, the picture above shows several people learning about the Japanese tea ceremony. The term has been used in adult education, teacher preparation, elementary and secondary education, higher education, counseling and psychology, social work, healthcare, and business.

Cultural competence is a large construct with knowledge, skill, behavioral, and attitudinal aspects. Cross et al. (1989) laid the basis for a cultural competence continuum, moving from cultural destructiveness through incapacity, blindness, pre-competence, competence, and cultural proficiency.  

Figure 1: Continuum of Cultural Competence (Cross et al.,1989)

The premise of this continuum is that individuals and organizations reflect various levels of awareness, knowledge, and skills vis-à-vis their relationship with cultural variables.  

Many of the earlier articles on cultural competence appeared to take an essentialist view of culture in which it becomes a list of characteristics to be memorized rather than a dynamic process of complex interactions (Gray & Thomas, 2006). This is illustrated by resources that, reminiscent of a cookbook, provide a cultural overview of specific groups and describe their behaviors and practices with recommendations for appropriate ways of interacting with them (see for example, see Salimbene, 2000). These resources made no allowance for differences within cultural groups. While people espousing cultural competence may have good intentions, the danger is that it can reduce people to a stereotype. Cultural competence also treats cultures as static and fails to recognize the multiplicity of identities a single individual may have. However, some voices challenge the orthodoxy of the cultural competence view rooted in cultural differences. Wear (2003) suggests educators should examine how culture is conceived. She uses Giroux's (2000) concept of "insurgent multiculturalism" which looks beyond the focus on subordinate groups' deficits, to examine the historic, semiotic, and institutional roots of racism. Over the years, a model of the higher levels of "proficiency" has emerged that acknowledges a greater recognition of societal inequities (National Center for Cultural Competence, 2007).

Critiques of Cultural Competence 

A critique of cultural competence is that cultural competence initiatives can stereotype and further marginalize people by assigning culture to people based on visible characteristics. Simplistic views of culture result in over-generalized representations of cultural identities and practices (Singer et al., 2015). Lekas et al. (2020) commented that:

Culture is not stagnant, but a changing system of beliefs and values shaped by our interactions with one another, institutions, media, and technology, and by the socioeconomic determinants of our lives. Yet, the claim that one can become competent in any culture suggests that there is a core set of beliefs and values that remain unchanged and that are shared by all the members of a specific group. This static and totalizing view of culture that connotes a set of immutable ideas embraced by all members of a social group generates a social stereotype. (p. 1)

Given the long-standing diversity of the U.S., it is arrogant and condescending to assume that a single person, institution, or system can become culturally competent in an all-inclusive manner. Everyone has their own intentional and unintentional racist, sexist, classist, and other biases, whether personally acknowledged or suppressed. Despite these biases, “the idea of cultural competency gives us a false sense of exemption from these human flaws in perception” that cause us to mistreat others (Cooks-Campbell, 2022, para. 22). Ignoring diversity does not adequately address people’s multiple identities or individuals whose identity is not immediately visible. 

Cultural Humility

Based on the flaws of cultural competence, some suggest that cultural humility should replace the term as a goal (Lekas, 2020; Tervalon & Murray-Garcia, 1998). Cultural humility is an approach to sociocultural differences that emphasizes intersectionality and understanding one’s implicit biases. This approach cultivates self-awareness and self-reflection, bringing a respectful willingness to learn to interpersonal interactions and attention to power dynamics. Reflecting upon one's culture is often a first step in becoming more aware of one's relationship with those culturally different from oneself (National Center for Cultural Competence, 2007). Self-reflection can be employed to identify how white privilege reinforces and maintains institutionalized racism (Lekas et al., 2020; Tyson, 2007). 

Intercultural Literacy

Intercultural literacy builds on the ideas of cultural competence but, much like cultural humility, adds concepts of critical reflection and self-examination. It also includes responsibility for contributing to constructive change within one’s culture. An interculturally literate person can draw on their background experience to comprehend a second culture, including its symbols and communications. Intercultural literacy requires analyzing dominant cultures as they interact with other cultures in global or cross-cultural partnerships. Intercultural literacy is “the competencies, understandings, attitudes, language proficiencies, participation, and identities necessary for effective cross-cultural engagement” (Heyward, 2002, p. 9). Yelich Biniecki and Stojanović (2023) note that cross-cultural interactions have become a daily experience for people and advocate that in “today’s internationalized work and education environments, developing the competencies, attitudes, and understandings to support cross-cultural encounters should be a priority” (p. 4). Preparation for internationalization is a goal of many higher education institutions (Yelich Biniecki & Stojanović, 2023) and businesses (Shliakhovchuk, 2021). Cross-cultural interactions are now the norm in a world with increased international interconnectedness, advanced communication technologies, frequent travel and migration, scholar and student exchanges, and displacement of populations due to conflict and devastation of natural environments (Schliakhovchuk, 2021). The current labor market requires workers with advanced skills, including soft skills that include communication, collaboration, and teamwork, all requiring the ability to work with others. 

Comparison of Related Concepts

Schliakhovchuk (2021) noted that discussion of international or global interactions emerged in the 1970s. Cultural competence was discussed as early as 1980, with cultural humility following soon after. Cultural literacy was described as early as the late 1980s, and, in the 21st century has become synonymous with “intercultural competence, intercultural literacy, CQ/cultural intelligence, or cultural mindfulness” (p. 234). The health professions intensively discussed cultural competence, and many training opportunities were offered. Over time, the reputation of cultural competence has waned because it assumes an impersonal, objective, and hypothetically superior person who is proficient in dealing with others. In contrast, intercultural literacy assumes more equality and parity among people involved in any intercultural relationship. Not only that, but intercultural literacy allows for self-examination, critical reflection, personal and cultural change, and the possibility of transformative learning.

References

• Arredondo, E. (2013). Cultural competence. In M. D. Gellman, M. D. & J. R. Turner (Eds.), Encyclopedia of Behavioral Medicine. Springer. doi.org/10.1007/978-1-4419-1005-9_172
• Brandt, D., & Clinton, K. (2002). Limits of the local: Expanding perspectives on literacy as a social practice. Journal of Literacy Practice, 34(3), 337-356
• Cooks-Campbell, A. (2022, February 14). How cultural humility and cultural competence impact belonging. Retrieved from https://www.betterup.com/blog/cultural-humility-vs-cultural-competence
• Cross, T., Bazron, B., Dennis, K., & Isaacs, M. (1989). Towards a culturally competent system of care (Vol. 1). Washington, DC: Georgetown University Child Development Center, CASSP Technical Assistance Center.
• Giroux H. (2000). Insurgent multiculturalism and the promise of pedagogy. In E. M. Duarte & S. Smith (Eds.), Foundational Perspectives in Multicultural Education (pp. 195-212). Longman.
• Gray, P. D., & Thomas, D. J. (2006). Critical reflections on culture in nursing. Journal of Cultural Diversity, 132(2), 76-82. 
• Hayes, E., & Colin III, S. A. J. (1994). Racism and sexism in the United States: Fundamental issues. In E. Hayes & S. A. J. Colin III (Eds.), Confronting racism and sexism. New Directions for Adult and Continuing Education, No. 61 (pp. 5-16). Jossey-Bass.
• Heyward, M. (2002). From international to intercultural: Redefining the international school for a globalized world. Journal of Research in International Education, 1(1), 9−32. https://doi.org/10.1177/147524090211002
• Imel, S. (1998). Promoting intercultural understanding: Trends and Issues. Center on Education and Training for Employment. (ERIC Document Reproduction Service No. ED424451.
• Lekas, H. M., Pahl, K., & Fuller Lewis, C. (2020). Rethinking cultural competence: Shifting to cultural humility. Health Services Insights, 13, 1-4. doi: 10.1177/1178632920970580
• National Center for Cultural Competence. Georgetown University Center for Child and Human Development. Retrieved February 22, 2008 from http://www11.georgetown.edu/research/gucchd/nccc/
• Salimbene (2000). What language does your patient hurt in? A practical guide to culturally competent patient care. EMC Paradigm. 
• Shliakhovchuk, E. After cultural literacy: new models of intercultural competency for life and work in a VUCA world. Educational Review, 73(2), 229-250 doi:10.1080/00131911.2019.1566211
• Tervalon, M., & Murray-Garcia, J. (1998). Cultural humility versus cultural competence: A critical distinction in defining physician outcomes in multicultural education. Journal of Health Care for the Poor and Underserved, 9(2), 117-125. doi: 10.1353/hpu.2010.0233
• Tyson, S. Y. (2007). Can cultural competence be achieved without attending to white racism? Issues in Mental Health Nursing, 28: 1341-1344.
• U. S. Department of Health and Human Services, Office of Minority Health. National Standards for culturally and linguistically appropriate services for healthcare. Retrieved from http://www.omhrc.gov/assets/pdf/checked/finalreport.pdf
• Wang, W. (2007). Cultural competence of international humanitarian workers. Adult Education Quarterly, 57, 187-204.
• Wear D. (2003). Insurgent multiculturalism: rethinking how and why we teach culture in medical education. Academic Medicine, 78(6), 549-54. doi: 10.1097/00001888-200306000-00002
• Yelich Biniecki, S., & Stojanović, M. (2023). Fostering internationalization in adult education graduate programs in the United States: Opportunities for growth. Educational Considerations, 49(2). https://doi.org/10.4148/0146-9282.2364